Agriculture

28 pages, 3635 KB

Open AccessEditor’s ChoiceArticle

Glyphosate Biodegradation by Airborne Plant Growth-Promoting Bacteria: Influence on Soil Microbiome Dynamics

by Beatriz Genoveva Guardado-Fierros, Miguel Angel Lorenzo-Santiago, Thiago Gumiere, Lydia Aid, Jacobo Rodriguez-Campos and Silvia Maribel Contreras-Ramos

Agriculture 2025, 15(4), 362; https://doi.org/10.3390/agriculture15040362 - 8 Feb 2025

Cited by 3 | Viewed by 2926

Abstract

Due to its persistence, glyphosate contamination in soil poses environmental and health risks. Plant growth-promoting bacteria (PGPB) offer a potential solution for mitigating glyphosate pollution. This study assessed the glyphosate degradation capacity of three airborne PGPB isolates (Exiguobacterium indicum AS03, Kocuria sediminis [...] Read more.

Due to its persistence, glyphosate contamination in soil poses environmental and health risks. Plant growth-promoting bacteria (PGPB) offer a potential solution for mitigating glyphosate pollution. This study assessed the glyphosate degradation capacity of three airborne PGPB isolates (Exiguobacterium indicum AS03, Kocuria sediminis AS04, and Rhodococcus rhodochrous AS33) individually and in a consortium (CS) compared to natural attenuation in microcosms as the control (CTL), where soil autochthonous microorganisms (MS) were present. AS03 exhibited the highest glyphosate degradation (86.3%), followed by AS04 and AS33 at 14 days (61.6% and 64.7%). The consortium accelerated glyphosate removal, reaching 99.7%, while the control treatment removal was 94% at 60 days. Aminomethylphosphonic acid (AMPA) is the main metabolite in glyphosate degradation, and it had a maximum peak in concentration at 28 days in the CS + MS (1072 mg kg⁻¹) and CTL (990 mg kg⁻¹) treatments. Subsequently, a decrease in AMPA concentration was observed at 60 days up to 349 mg kg⁻¹ and 390 mg kg⁻¹, respectively. These results suggested that soil autochthonous microorganisms and their interactions with a consortium have similar biotransformation of glyphosate, but the AMPA conversion to other intermedium metabolites through degradation was slow. A minimum AMPA concentration of 15–45 mg kg⁻¹ over time was detected with the consortium. The microbiome analysis revealed shifts in microbial composition, with an increase in glyphosate-degrading genera like Psychrobacter and Lyzobacter. These changes enhance soil resilience and fertility, demonstrating the potential of airborne PGPB for bioremediation and environmental sustainability. Full article

(This article belongs to the Special Issue Symbiotic Frontiers: Microbial Innovations Shaping Sustainable and Resilient Agriculture)

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22 pages, 2102 KB

Open AccessEditor’s ChoiceReview

Effects of Microplastics on Bioavailability, Persistence and Toxicity of Plant Pesticides: An Agricultural Perspective

by Kuok Ho Daniel Tang

Agriculture 2025, 15(4), 356; https://doi.org/10.3390/agriculture15040356 - 7 Feb 2025

Cited by 11 | Viewed by 4037

Abstract

Microplastic–pesticide interactions influence pesticide performance, soil health, and environmental safety. This review aims to comprehensively present the effects of microplastic–pesticide interactions on pesticide bioavailability, persistence, and toxicity, along with their agricultural implications on pest control. It reviews more than 90 related articles from [...] Read more.

Microplastic–pesticide interactions influence pesticide performance, soil health, and environmental safety. This review aims to comprehensively present the effects of microplastic–pesticide interactions on pesticide bioavailability, persistence, and toxicity, along with their agricultural implications on pest control. It reviews more than 90 related articles from established scholarly databases. Most studies indicate that pesticide bioavailability decreases in the presence of microplastics due to adsorption, which is frequently influenced by the hydrophobicity (log K_ow) of the pesticides and the surface area and type of microplastics. Higher log K_ow results in higher adsorption and lower bioavailability. Aged microplastics have higher surface areas for adsorption, thus reducing pesticide bioavailability. This decreases the effectiveness of systematic and contact pesticides. Lower bioavailability leads to less adsorption of the former by plants to control pest infestation and less contact of the latter with pests in the soil to kill them directly. Higher pesticide adsorption also increases the persistence of pesticides, as indicated by their extended degradation half-lives. However, some studies demonstrate that biodegradable microplastics, especially the aged ones, have less effect on pesticide persistence because they release pesticides for degradation when they break down. Few studies on how microplastics alter pesticide toxicity on target organisms are available, but the available ones point to potentially higher toxicity on crops and beneficial soil organisms. Overall, the review highlights a significant negative effect of microplastics on pesticide bioavailability. This may prompt the application of more pesticides to achieve the desired level of crop protection, which bears cost and environmental consequences. Full article

(This article belongs to the Section Agricultural Soils)

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14 pages, 4202 KB

Open AccessEditor’s ChoiceArticle

Targeted Metabolomics Combined with OPLS-DA to Elucidate Phenolic Differences in Eight Cashew Apples

by Haijie Huang, Huifang Ma, Li Zhao, Weijian Huang, Xuejie Feng, Fuchu Hu, Ya Zhao, Liming Chen, Yingjun Ye, Zhongrun Zhang and Yijun Liu

Agriculture 2025, 15(4), 360; https://doi.org/10.3390/agriculture15040360 - 7 Feb 2025

Cited by 5 | Viewed by 1902

Abstract

Phenolic compounds were separated and identified using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and analyzed with OPLS-DA and metabolomics in eight cashew apples. The results showed that the phenolic compound content in Bra4 was the highest, while Gua had the lowest [...] Read more.

Phenolic compounds were separated and identified using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and analyzed with OPLS-DA and metabolomics in eight cashew apples. The results showed that the phenolic compound content in Bra4 was the highest, while Gua had the lowest levels. The content of 39 phenolic compounds (nine types) showed significant variability across the 8 cashew apples. Sixteen phenolic compounds with significant differences were excavated and could be used to differentiate and identify cashew apple varieties. The 18 differentially significant phenolic compounds present in Bra4 and Gua were mainly distributed in 6 metabolic pathways, and the metabolic pathways of flavonoid biosynthesis and phenylpropanoid biosynthesis were elucidated to regulate the synthesis of luteolin and syringin, respectively. These studies provide a scientific basis for selecting and breeding cashew varieties and developing products. Full article

(This article belongs to the Special Issue Application of Antioxidants and Bioactive Compounds in Agricultural Products)

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16 pages, 2294 KB

Open AccessEditor’s ChoiceArticle

Tractor Power Take-Off and Drawbar Pull Performance and Efficiency Evolution Analysis Methodology and Model: A Case Study

by Ivan Herranz-Matey

Agriculture 2025, 15(3), 354; https://doi.org/10.3390/agriculture15030354 - 6 Feb 2025

Cited by 3 | Viewed by 2430

Abstract

Previous studies on tractor performance and efficiency were conducted prior to the implementation of emission reduction technologies and the increased density and complexity of tractor portfolios. This study presents a robust methodology for forecasting specific fuel consumption based on public information, which incorporates [...] Read more.

Previous studies on tractor performance and efficiency were conducted prior to the implementation of emission reduction technologies and the increased density and complexity of tractor portfolios. This study presents a robust methodology for forecasting specific fuel consumption based on public information, which incorporates physical attribute-based cohorts and technological generation groupings, alongside variables such as wheelbase, mass, and power take-off power. The proposed model significantly improves forecasting accuracy, enhancing the current R-squared (RSq) from 0.6091 to 0.8519 and reducing the root mean square error (RMSE) from 0.0098 to 0.0065. Additionally, the model provides accurate predictions of drawbar performance and efficiency. Its simplicity results in low cognitive and computational demands, making it accessible via widely available spreadsheet software on any computer or handheld device. This accessibility supports data-driven decision-making for tractor replacement strategies, ultimately promoting sustainable profitability in agricultural business operations. Full article

(This article belongs to the Section Agricultural Technology)

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34 pages, 13743 KB

Open AccessEditor’s ChoiceArticle

Integration of UAV Multispectral Remote Sensing and Random Forest for Full-Growth Stage Monitoring of Wheat Dynamics

by Donghui Zhang, Hao Qi, Xiaorui Guo, Haifang Sun, Jianan Min, Si Li, Liang Hou and Liangjie Lv

Agriculture 2025, 15(3), 353; https://doi.org/10.3390/agriculture15030353 - 6 Feb 2025

Cited by 13 | Viewed by 3724

Abstract

Wheat is a key staple crop globally, essential for food security and sustainable agricultural development. The results of this study highlight how innovative monitoring techniques, such as UAV-based multispectral imaging, can significantly improve agricultural practices by providing precise, real-time data on crop growth. [...] Read more.

Wheat is a key staple crop globally, essential for food security and sustainable agricultural development. The results of this study highlight how innovative monitoring techniques, such as UAV-based multispectral imaging, can significantly improve agricultural practices by providing precise, real-time data on crop growth. This study utilized unmanned aerial vehicle (UAV)-based remote sensing technology at the wheat experimental field of the Hebei Academy of Agriculture and Forestry Sciences to capture the dynamic growth characteristics of wheat using multispectral data, aiming to explore efficient and precise monitoring and management strategies for wheat. A UAV equipped with multispectral sensors was employed to collect high-resolution imagery at five critical growth stages of wheat: tillering, jointing, booting, flowering, and ripening. The data covered four key spectral bands: green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (840 nm). Combined with ground-truth measurements, such as chlorophyll content and plant height, 21 vegetation indices were analyzed for their nonlinear relationships with wheat growth parameters. Statistical analyses, including Pearson’s correlation and stepwise regression, were used to identify the most effective indices for monitoring wheat growth. The Normalized Difference Red-Edge Index (NDRE) and the Triangular Vegetation Index (TVI) were selected based on their superior performance in predicting wheat growth parameters, as demonstrated by their high correlation coefficients and predictive accuracy. A random forest model was developed to comprehensively evaluate the application potential of multispectral data in wheat growth monitoring. The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. Furthermore, dynamic analyses of UAV imagery effectively identified abnormal field areas, such as regions experiencing water stress or disease, providing a scientific basis for precision agricultural interventions. This study highlights the potential of UAV-based remote sensing technology in monitoring wheat growth, addressing the research gap in systematic full-cycle analysis of wheat. It also offers a novel technological pathway for optimizing agricultural resource management and improving crop yields. These findings are expected to advance intelligent agricultural production and accelerate the implementation of precision agriculture. Full article

(This article belongs to the Topic Advances in Smart Agriculture with Remote Sensing as the Core and Its Applications in Crops Field)

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16 pages, 1970 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Grape Pomace Supplementation in Lamb Diets to Mitigate Haemonchus contortus Infection

by Mateus O. Mena, Gustavo G. O. Trevise, Thais N. R. Silva, Victor M. Moellmann, César C. Bassetto, Bruno S. Gatti, Helder Louvandini, Ricardo V. G. Soutello, Ana C. A. Albuquerque and Alessandro F. T. Amarante

Agriculture 2025, 15(3), 341; https://doi.org/10.3390/agriculture15030341 - 5 Feb 2025

Cited by 5 | Viewed by 2087

Abstract

This study aimed to evaluate the potential benefits and feasibility of adding dried and ground grape pomace to the concentrate fed to lambs experimentally infected with Haemonchus contortus. Eighteen male Santa Inês lambs, recently weaned, were housed in individual pens and were [...] Read more.

This study aimed to evaluate the potential benefits and feasibility of adding dried and ground grape pomace to the concentrate fed to lambs experimentally infected with Haemonchus contortus. Eighteen male Santa Inês lambs, recently weaned, were housed in individual pens and were allocated into two groups based on their body weight. The lambs in the supplemented group (n = 9) initially received a diet composed of 50% ground hay and 50% concentrate. Subsequently, these animals were gradually adapted to grape pomace until its final inclusion in the concentrate reached 20%. The lambs in the control group received a concentrate without grape pomace. Both groups of lambs were artificially infected with 4000 infective larvae of H. contortus, and 28 days later, the lambs were euthanized for quantification of the nematodes present in the abomasum. The following variables showed no statistical differences (p > 0.05) between the groups: worm burden, packed cell volume, total plasma protein, blood eosinophil count, and daily weight gain. Regarding anti-Haemonchus IgG plasma levels, there was a significant time * treatment interaction (p = 0.0099) with higher values in the supplemented group. At the two final samplings, the supplemented group showed significantly lower values of eggs per gram of feces than the control group (p < 0.05). The supplemented group showed female worms shorter and with less eggs in utero than those of the control group, with significant difference for these variables (p < 0.05). In conclusion, grape pomace can be included in the diet, as it promotes more sustainable animal production, and, additionally, it can cause a reduction in H. contortus fecundity, benefiting haemonchosis prophylaxis. Full article

(This article belongs to the Special Issue Valorization of Agri-Food Waste Bioresources for Sustainable Livestock Feeding)

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24 pages, 1700 KB

Open AccessEditor’s ChoiceArticle

Antifungal Efficacy of Essential Oils and Their Predominant Components Against Olive Fungal Pathogens

by Elena Petrović, Karolina Vrandečić, Jasenka Ćosić, Tamara Siber and Sara Godena

Agriculture 2025, 15(3), 340; https://doi.org/10.3390/agriculture15030340 - 4 Feb 2025

Cited by 3 | Viewed by 3718

Abstract

The antifungal effectiveness of essential oils (EOs) and their predominant components were tested on 14 phytopathogenic fungi isolated from olive trees. Commercial EOs from holy basil (Ocimum tenuiflorum L.), Chinese cinnamon (Cinnamomum aromaticum Ness), lemon (Citrus × limon), peppermint [...] Read more.

The antifungal effectiveness of essential oils (EOs) and their predominant components were tested on 14 phytopathogenic fungi isolated from olive trees. Commercial EOs from holy basil (Ocimum tenuiflorum L.), Chinese cinnamon (Cinnamomum aromaticum Ness), lemon (Citrus × limon), peppermint (Mentha × piperita L.), oregano (Origanum compactum Benth), and thyme (Thymus vulgaris L.) and components eugenol, e-cinnamaldehyde, limonene, menthol, carvacrol, and thymol were used. Antifungal efficacy was tested on six species from the Botryosphaeriaceae family: Botryosphaeria dothidea (Moug. ex Fr.) Ces. & De Not.; Diplodia mutila (Fr.) Fr.; D. seriata De Not.; Dothiorella iberica A.J.L. Phillips, J. Luque & A. Alves; Do. sarmentorum (Fr.) A.J.L. Phillips, Alves & Luque; and Neofusicoccum parvum (Pennycook & Samuels) Crous, Slippers & A.J.L. Phillips. Other tested species included Biscogniauxia mediterranea (De Not.) Kuntze, B. nummularia (Bull.) Kuntze; Cytospora pruinosa Défago; Nigrospora gorlenkoana Novobr.; N. osmanthi Mei Wang & L. Cai; N. philosophiae-doctoris M. Raza, Qian Chen & L. Cai; Phaeoacremonium iranianum L. Mostert, Grafenhan, W. Gams & Crous; and Sordaria fimicola (Roberge ex Desm.) Ces. & De Not. The results show that Chinese cinnamon and oregano EOs, along with their components, completely inhibited the growth of all tested fungi, indicating their potential as biological control agents in sustainable agriculture. In contrast, the least effective treatments were the EOs derived from lemon and peppermint, as well as the components limonene, menthol, and thymol. Notably, the fungi Do. iberica and N. gorlenkoana were among the most sensitive to all the treatments applied. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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38 pages, 10703 KB

Open AccessEditor’s ChoiceArticle

Analysis of Soil–Straw Movement Behavior in Saline–Alkali Soil Under Dual-Axis Rotary Tillage Based on EDEM

by Zhuang Zhao, Jialin Hou, Peng Guo, Chao Xia, Haipeng Yan and Dongwei Wang

Agriculture 2025, 15(3), 337; https://doi.org/10.3390/agriculture15030337 - 4 Feb 2025

Cited by 7 | Viewed by 1431

Abstract

The layered soil crushing rotary tillage machine with L-shaped reclamation rotary blades and rotary-reclamation rotary blades combination was designed to deal with the problems of a low soil fragmentation rate, low straw mulching rate, and poor surface leveling after plowing in the traditional [...] Read more.

The layered soil crushing rotary tillage machine with L-shaped reclamation rotary blades and rotary-reclamation rotary blades combination was designed to deal with the problems of a low soil fragmentation rate, low straw mulching rate, and poor surface leveling after plowing in the traditional rotary tiller tillage mode in the coastal saline land of the Yellow River Delta. A dual active layered soil fragmentation tillage mode was proposed, and the key structural parameters, blade axis arrangement, and spatial layout of L-shaped reclamation rotary blades and rotary-reclamation rotary blades were determined based on the sliding cutting principle analysis. A discrete element model of soil straw tillage component aggregates suitable for coastal saline alkali land was constructed using EDEM, and the influence of L-shaped reclamation rotary blades and rotary-reclamation rotary blades on the soil tillage layer displacement performance and straw burial performance of saline alkali land was comprehensively analyzed from a microscopic perspective. Taking the rotation speed of the L-shaped reclamation rotary blades, the rotation speed of the rotary-reclamation rotary blades, and the forward speed as experimental factors, and using soil fragmentation rate and straw burial rate as evaluation indicators for experimental optimization analysis, the optimal parameters were obtained: the rotation speed of the L-shaped reclamation rotary blades was 295.04 r/min, the rotation speed of the rotary-reclamation rotary blades was 359.06 r/min, and the forward speed was 3.12 km/h. At this time, the theoretical soil fragmentation rate of saline alkali land was 94.67%, and the straw burial rate was 93.56%. Field experiments have shown that the average soil fragmentation rate of the L-shaped reclamation rotary blades and rotary-reclamation rotary blades combined layered soil crushing rotary tiller after cultivation is 94.37%, the straw burial rate is 95.68%, the surface flatness is 25.82 mm, and the stability of the tillage depth is 95.64%. The machine has shown increased performance in comparison to traditional single axis rotary tillers, meeting the needs of crop bed preparation in saline alkali land. Full article

(This article belongs to the Special Issue Intelligent Agricultural Equipment in Saline Alkali Land)

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30 pages, 1595 KB

Open AccessEditor’s ChoiceArticle

Assessing Honey Quality: A Focus on Some Physicochemical Parameters of Honey from Iasi County (Romania)

by Aida Albu, Florin Dumitru Bora, Simona-Maria Cucu-Man, Vasile Stoleru, Cătălin-Emilian Nistor, Ioan Sebastian Brumă and Oana-Raluca Rusu

Agriculture 2025, 15(3), 333; https://doi.org/10.3390/agriculture15030333 - 3 Feb 2025

Cited by 4 | Viewed by 3603

Abstract

The study of honey in Iasi County reveals its ecological, economic and health importance, emphasizing its unique properties, role in biodiversity and value in promoting sustainable beekeeping and regional identity. This study aimed to investigate the characteristics of honey from Iasi County, Romania, [...] Read more.

The study of honey in Iasi County reveals its ecological, economic and health importance, emphasizing its unique properties, role in biodiversity and value in promoting sustainable beekeeping and regional identity. This study aimed to investigate the characteristics of honey from Iasi County, Romania, analyzing 27 samples collected in 2020 and 2021. The samples include tilia (8 raw, 7 commercial), acacia (2 raw, 2 commercial), rapeseed (3 raw), sunflower (3 raw) and lavender (2 raw) honey. Analyses were carried out under Romanian/EU standards, assessing parameters such as color, electrical conductivity, moisture, total soluble solids (TSS), acidity (free, lactone, total), pH, hydroxymethylfurfural (HMF), ash and mineral composition (Na, K, Ca, Mg, Cu, Zn, Mn and Fe). The results revealed significant differences between raw and commercial honeys. Notably, in commercial tilia honey, higher values were found for color (38.58 mm Pfund vs. 24.14 mm Pfund), total acidity (25.93 meq·kg⁻¹ vs. 17.36 meq·kg⁻¹) and HMF levels (8.84 mg·kg⁻¹ vs. 3.68 mg·kg⁻¹). Conversely, water-insoluble solids (0.08% vs. 0.15%) and ash content (0.21% vs. 0.30%) were lower in commercial samples. Potassium was the most abundant mineral detected, while copper and zinc levels were the lowest. Significant correlations were observed between several parameters, including ash with electrical conductivity and HMF with acidity. This study underscores the impact of processing on honey quality and highlights the importance of understanding honey composition for consumers and producers alike. Full article

(This article belongs to the Special Issue Quality Assessment and Processing of Farm Animal Products)

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36 pages, 16450 KB

Open AccessEditor’s ChoiceArticle

Establishment of Whole-Rice-Plant Model and Calibration of Characteristic Parameters Based on Segmented Hollow Stalks

by Ranbing Yang, Peiyu Wang, Yiren Qing, Dongquan Chen, Lu Chen, Wenbin Sun and Kang Xu

Agriculture 2025, 15(3), 327; https://doi.org/10.3390/agriculture15030327 - 2 Feb 2025

Cited by 5 | Viewed by 1737

Abstract

To address the limitations of the current discrete element model of rice plants in terms of accurately reflecting structural differences and threshing characteristics, this study proposes a whole-rice-plant modeling method based on segmented hollow stalks and establishes a whole-rice-plant model that accurately represents [...] Read more.

To address the limitations of the current discrete element model of rice plants in terms of accurately reflecting structural differences and threshing characteristics, this study proposes a whole-rice-plant modeling method based on segmented hollow stalks and establishes a whole-rice-plant model that accurately represents the bending and threshing characteristics of the actual rice plant. Initially, based on the characteristics of the rice plant, the rice stalk was segmented into three sections of hollow stalks with distinct structures—namely, the primary stalk, the secondary stalk, and the tertiary stalk—ensuring that the model closely resembles actual rice plants. Secondly, the mechanical and contact parameters for each structure of the rice plant were measured and calibrated through mechanical and contact tests. Subsequently, utilizing the Hertz–Mindlin contact model, a multi-dimensional element particle arrangement method was employed to establish a discrete element model of the entire rice plant. The bending characteristics of the stalk and the threshing characteristics of the rice were calibrated using three-point bending tests and impact threshing tests. The results indicated calibration errors in the bending resistance force of 4.46%, 3.95%, and 2.51% for the three-section stalk model, and the calibration error for the rice model’s threshing rate was 1.86%, which can accurately simulate the bending characteristics of the stalk and the threshing characteristics of the rice plant. Finally, the contact characteristics of the model were validated through a stack angle verification test, which revealed that the relative error of the stacking angle did not exceed 7.52%, confirming the accuracy of the contact characteristics of the rice plant model. The findings of this study provide foundational models and a theoretical basis for the simulation of and analytical applications related to rice threshing and cleaning. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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17 pages, 6161 KB

Open AccessEditor’s ChoiceArticle

Efficient Triple Attention and AttentionMix: A Novel Network for Fine-Grained Crop Disease Classification

by Yanqi Zhang, Ning Zhang, Jingbo Zhu, Tan Sun, Xiujuan Chai and Wei Dong

Agriculture 2025, 15(3), 313; https://doi.org/10.3390/agriculture15030313 - 31 Jan 2025

Cited by 4 | Viewed by 1294

Abstract

In the face of global climate change, crop pests and diseases have emerged on a large scale, with diverse species lasting for long periods and exerting wide-ranging impacts. Identifying crop pests and diseases efficiently and accurately is crucial in enhancing crop yields. Nonetheless, [...] Read more.

In the face of global climate change, crop pests and diseases have emerged on a large scale, with diverse species lasting for long periods and exerting wide-ranging impacts. Identifying crop pests and diseases efficiently and accurately is crucial in enhancing crop yields. Nonetheless, the complexity and variety of scenarios render this a challenging task. In this paper, we propose a fine-grained crop disease classification network integrating the efficient triple attention (ETA) module and the AttentionMix data enhancement strategy. The ETA module is capable of capturing channel attention and spatial attention information more effectively, which contributes to enhancing the representational capacity of deep CNNs. Additionally, AttentionMix can effectively address the label misassignment issue in CutMix, a commonly used method for obtaining high-quality data samples. The ETA module and AttentionMix can work together on deep CNNs for greater performance gains. We conducted experiments on our self-constructed crop disease dataset and on the widely used IP102 plant pest and disease classification dataset. The results showed that the network, which combined the ETA module and AttentionMix, could reach an accuracy as high as 98.2% on our crop disease dataset. When it came to the IP102 dataset, this network achieved an accuracy of 78.7% and a recall of 70.2%. In comparison with advanced attention models such as ECANet and Triplet Attention, our proposed model exhibited an average performance improvement of 5.3% and 4.4%, respectively. All of this implies that the proposed method is both practical and applicable for classifying diseases in the majority of crop types. Based on classification results from the proposed network, an install-free WeChat mini program that enables real-time automated crop disease recognition by taking photos with a smartphone camera was developed. This study can provide an accurate and timely diagnosis of crop pests and diseases, thereby providing a solution reference for smart agriculture. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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20 pages, 7029 KB

Open AccessEditor’s ChoiceArticle

Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View Stereo

by Xingmei Xu, Jiayuan Li, Jing Zhou, Puyu Feng, Helong Yu and Yuntao Ma

Agriculture 2025, 15(3), 298; https://doi.org/10.3390/agriculture15030298 - 30 Jan 2025

Cited by 10 | Viewed by 2084

Abstract

Phenotypic traits of fungi and their automated extraction are crucial for evaluating genetic diversity, breeding new varieties, and estimating yield. However, research on the high-throughput, rapid, and non-destructive extraction of fungal phenotypic traits using 3D point clouds remains limited. In this study, a [...] Read more.

Phenotypic traits of fungi and their automated extraction are crucial for evaluating genetic diversity, breeding new varieties, and estimating yield. However, research on the high-throughput, rapid, and non-destructive extraction of fungal phenotypic traits using 3D point clouds remains limited. In this study, a smart phone is used to capture multi-view images of shiitake mushrooms (Lentinula edodes) from three different heights and angles, employing the YOLOv8x model to segment the primary image regions. The segmented images were reconstructed in 3D using Structure from Motion (SfM) and Multi-View Stereo (MVS). To automatically segment individual mushroom instances, we developed a CP-PointNet++ network integrated with clustering methods, achieving an overall accuracy (OA) of 97.45% in segmentation. The computed phenotype correlated strongly with manual measurements, yielding R² > 0.8 and nRMSE < 0.09 for the pileus transverse and longitudinal diameters, R² = 0.53 and RMSE = 3.26 mm for the pileus height, R² = 0.79 and nRMSE = 0.12 for stipe diameter, and R² = 0.65 and RMSE = 4.98 mm for the stipe height. Using these parameters, yield estimation was performed using PLSR, SVR, RF, and GRNN machine learning models, with GRNN demonstrating superior performance (R² = 0.91). This approach was also adaptable for extracting phenotypic traits of other fungi, providing valuable support for fungal breeding initiatives. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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23 pages, 2415 KB

Open AccessEditor’s ChoiceArticle

Framework for Apple Phenotype Feature Extraction Using Instance Segmentation and Edge Attention Mechanism

by Zichong Wang, Weiyuan Cui, Chenjia Huang, Yuhao Zhou, Zihan Zhao, Yuchen Yue, Xinrui Dong and Chunli Lv

Agriculture 2025, 15(3), 305; https://doi.org/10.3390/agriculture15030305 - 30 Jan 2025

Cited by 7 | Viewed by 1675

Abstract

A method for apple phenotypic feature extraction and growth anomaly identification based on deep learning and natural language processing technologies is proposed in this paper, aiming to enhance the accuracy of apple quality detection and anomaly prediction in agricultural production. This method integrates [...] Read more.

A method for apple phenotypic feature extraction and growth anomaly identification based on deep learning and natural language processing technologies is proposed in this paper, aiming to enhance the accuracy of apple quality detection and anomaly prediction in agricultural production. This method integrates instance segmentation, edge perception mechanisms, attention mechanisms, and multimodal data fusion to accurately extract an apple’s phenotypic features, such as its shape, color, and surface condition, while identifying potential anomalies which may arise during the growth process. Specifically, the edge transformer segmentation network is employed to combine deep convolutional networks (CNNs) with the Transformer architecture, enhancing feature extraction and modeling long-range dependencies across different regions of an image. The edge perception mechanism improves segmentation accuracy by focusing on the boundary regions of the apple, particularly in the case of complex shapes or surface damage. Additionally, the natural language processing (NLP) module analyzes agricultural domain knowledge, such as planting records and meteorological data, providing insights into potential causes of growth anomalies and enabling more accurate predictions. The experimental results demonstrate that the proposed method significantly outperformed traditional models across multiple metrics. Specifically, in the apple phenotypic feature extraction task, the model achieved exceptional performance, with accuracy of 0.95, recall of 0.91, precision of 0.93, and mean intersection over union (mIoU) of 0.92. Furthermore, in the growth anomaly identification task, the model also performed excellently, with a precision of 0.93, recall of 0.90, accuracy of 0.91, and mIoU of 0.89, further validating its efficiency and robustness in handling complex growth anomaly scenarios. The method’s integration of image data with agricultural knowledge provides a comprehensive approach to both apple quality detection and growth anomaly prediction, offering reliable decision support for agricultural production. The proposed method, by integrating image data with agricultural domain knowledge, provides precise decision support for agricultural production, not only improving the efficiency and accuracy of apple quality detection but also offering reliable technical assurance for agricultural economic analysis. Full article

(This article belongs to the Special Issue Application of Vision Technology and Artificial Intelligence in Smart Farming—2nd Edition)

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18 pages, 3969 KB

Open AccessEditor’s ChoiceArticle

An Automatic Irrigation System Based on Hourly Cumulative Evapotranspiration for Reducing Agricultural Water Usage

by Yongjae Lee, Seung-un Ha, Xin Wang, Seungyong Hahm, Kwangya Lee and Jongseok Park

Agriculture 2025, 15(3), 308; https://doi.org/10.3390/agriculture15030308 - 30 Jan 2025

Cited by 5 | Viewed by 2991

Abstract

This study investigates the development and application of an automatic irrigation system based on hourly cumulative evapotranspiration (ET) to optimize cabbage growth while reducing agricultural water usage. Traditional irrigation methods often result in inefficient water use due to reliance on human judgment or [...] Read more.

This study investigates the development and application of an automatic irrigation system based on hourly cumulative evapotranspiration (ET) to optimize cabbage growth while reducing agricultural water usage. Traditional irrigation methods often result in inefficient water use due to reliance on human judgment or fixed schedules. To address this issue, the proposed system utilizes environmental data collected from a field sensor (FS), the Korea meteorological administration (KMA), and a virtual sensor based on a machine learning model (ML) to calculate the hourly ET and automate irrigation. The ET was calculated using the FAO 56 Penman–Monteith (P-M) ET_o. Experiments were conducted to compare the effectiveness of different irrigation levels, ranging from 40, 60, 80, and 100% crop evapotranspiration (ET_c), on plant growth and the irrigation water productivity (WP_I). During the 46-day experimental period, cabbage growth and WP_I were higher in the FS and KMA 60% ET_c levels compared to other irrigation levels, with water usage of 8.90 and 9.07 L/plant, respectively. In the ML treatment, cabbage growth and WP_I were higher in the 80% ET_c level compared to other irrigation levels, with water usage of 8.93 L/plant. These results demonstrated that irrigation amounts of approximately 9 L/plant provided the optimal balance between plant growth and water conservation over 46 days. This system presents a promising solution for improving crop yield while conserving water resources in agricultural environments. Full article

(This article belongs to the Section Agricultural Water Management)

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18 pages, 5698 KB

Open AccessEditor’s ChoiceArticle

Spatial Evaluation of Salurnis marginella Occurrence According to Climate Change Using Multiple Species Distribution Models

by Jae-Woo Song, Jaho Seo and Wang-Hee Lee

Agriculture 2025, 15(3), 297; https://doi.org/10.3390/agriculture15030297 - 29 Jan 2025

Cited by 3 | Viewed by 1549

Abstract

Salurnis marginella causes agricultural and forest damage in various Asian environments. However, considering the environmental adaptability of pests and the active international trade, it may invade other regions in the future. As the damage to local communities caused by pests becomes difficult to [...] Read more.

Salurnis marginella causes agricultural and forest damage in various Asian environments. However, considering the environmental adaptability of pests and the active international trade, it may invade other regions in the future. As the damage to local communities caused by pests becomes difficult to control after invasion, it is essential to establish measures to minimize losses through pre-emptive monitoring and identification of high-risk areas, which can be achieved through model-based predictions. The aim of this study was to evaluate the potential distribution of S. marginella by developing multiple species distribution modeling (SDM) algorithms. Specifically, we developed the CLIMEX model and three machine learning-based models (MaxEnt, random forest, and multi-layer perceptron), integrated them to conservatively assess pest occurrence under current and future climates, and overlaid the host distribution with climatically suitable areas of S. marginella to identify high-risk areas vulnerable to the spread and invasion of the pest. The developed model, demonstrating a true skill statistic >0.8, predicted the potential continuous distribution of the species across the southeastern United States, South America, and Central Africa. This distribution currently covers approximately 9.53% of the global land area; however, the model predicted this distribution would decrease to 6.85%. Possible areas of spread were identified in Asia and the southwestern United States, considering the host distribution. This study provides data for the proactive monitoring of pests by identifying areas where S. marginella can spread. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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31 pages, 23482 KB

Open AccessFeature PaperEditor’s ChoiceReview

Addressing Shortages with Storage: From Old Grain Pits to New Solutions for Underground Storage Systems

by Antonella Pasqualone

Agriculture 2025, 15(3), 289; https://doi.org/10.3390/agriculture15030289 - 29 Jan 2025

Cited by 5 | Viewed by 6265

Abstract

In every era, climate variability and frequent food shortages have made it necessary to store harvested grains for more than one season. Underground grain storage has been used since ancient times throughout the world. Italy (Cerignola) and Malta (Valletta and Floriana) have preserved [...] Read more.

In every era, climate variability and frequent food shortages have made it necessary to store harvested grains for more than one season. Underground grain storage has been used since ancient times throughout the world. Italy (Cerignola) and Malta (Valletta and Floriana) have preserved rare examples of more recent (from the 16th century onward) large concentrations of grain pits, capable of accumulating substantial reserves to cope with famine or siege. No longer in operation, they represent an important part of the cultural heritage of the agricultural economy. The purpose of this narrative review was, after a geographical framing of grain pits in the Eurasian and African macro-areas, to take the Italian and Maltese grain pits as historical case studies to draw attention to the reevaluation of underground grain storage in the context of climate change and food insecurity. Today, as in the past, grain reserves play a significant role in food security in developing countries and, due to climate change and geopolitical events that can cause disruptions in grain supplies, are also increasingly important for developed countries. A comparison of traditional and modern underground storage systems reveals the great flexibility of this technology, ranging from basic pits of different sizes to large underground granaries equipped with a support structure. The advantages of underground storage, such as environmental sustainability due to thermal insulation of the soil and airtight conditions that make high energy inputs for grain cooling and pesticide use unnecessary, are still useful today, perhaps more so than in the past. Prospects for development include technical solutions involving the application of innovative information technology-based monitoring systems and the use of modern materials to ensure the performance of waterproofing, seepage control, and static safety, all tools for further evolution of this ancient storage system. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

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22 pages, 7886 KB

Open AccessEditor’s ChoiceArticle

Design and Analysis of Sowing Depth Detection and Control Device for Multi-Row Wheat Seeders Adapted to Different Terrain Variations

by Yueyue Li, Bing Qi, Encai Bao, Zhong Tang, Yi Lian and Meiyan Sun

Agriculture 2025, 15(3), 290; https://doi.org/10.3390/agriculture15030290 - 29 Jan 2025

Cited by 4 | Viewed by 1742

Abstract

To address the issue of reduced sowing depth detection accuracy caused by varying soil topography during the operation of wheat row drills, an indoor bench test device suitable for wheat row drills was developed. The device integrates a laser sensor and an array [...] Read more.

To address the issue of reduced sowing depth detection accuracy caused by varying soil topography during the operation of wheat row drills, an indoor bench test device suitable for wheat row drills was developed. The device integrates a laser sensor and an array sensor for terrain and sowing depth detection. The laser sensor provides the detected sowing depth values, while the array sensor captures different terrain features. The actual sowing depth values are obtained through the indoor experimental setup. The experiment includes three types of terrain: convex, concave, and flat. The terrain slope matrix is obtained using the array sensor, and terrain feature values are extracted. The laser sensor is then used to obtain the detected sowing depth, and the actual sowing depth is manually measured. PCA analysis is conducted to correlate terrain feature values with sowing depth deviations. Results indicate that under different terrain conditions, the slope mean and slope standard deviation are the main components affecting sowing depth deviations. Compared to using a single sensor, this system enables more accurate sowing depth measurement by analyzing terrain features. The device provides valuable data support for controlling sowing depth under varying terrain conditions in subsequent operations. Full article

(This article belongs to the Section Agricultural Technology)

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14 pages, 264 KB

Open AccessEditor’s ChoiceArticle

Effects of Lameness on Milk Yield, Milk Quality Indicators, and Rumination Behaviour in Dairy Cows

by Karina Džermeikaitė, Justina Krištolaitytė, Lina Anskienė, Greta Šertvytytė, Gabija Lembovičiūtė, Samanta Arlauskaitė, Akvilė Girdauskaitė, Arūnas Rutkauskas, Walter Baumgartner and Ramūnas Antanaitis

Agriculture 2025, 15(3), 286; https://doi.org/10.3390/agriculture15030286 - 28 Jan 2025

Cited by 6 | Viewed by 5492

Abstract

This study investigates the relationship between lameness, milk composition, and rumination behaviour in dairy cows by leveraging sensor-based data for automated monitoring. Lameness was found to significantly impact both rumination and milk production. Lameness was assessed in 24 multiparous Holstein dairy cows throughout [...] Read more.

This study investigates the relationship between lameness, milk composition, and rumination behaviour in dairy cows by leveraging sensor-based data for automated monitoring. Lameness was found to significantly impact both rumination and milk production. Lameness was assessed in 24 multiparous Holstein dairy cows throughout early lactation (up to 100 days postpartum), utilising a 1-to-5 scale. Lameness was found to significantly impact both rumination and milk production. On the day of diagnosis, rumination time decreased by 26.64% compared to the pre-diagnosis period (p < 0.01) and by 26.06% compared to healthy cows, indicating the potential of rumination as an early health indicator. The milk yield on the day of diagnosis was 28.10% lower compared to pre-diagnosis levels (p < 0.01) and 40.46% lower than healthy cows (p < 0.05). These findings suggest that lameness manifests prior to clinical signs, affecting productivity and welfare. Milk composition was also influenced, with lame cows exhibiting altered fat (+0.68%, p < 0.05) and lactose (−2.15%, p < 0.05) content compared to healthy cows. Positive correlations were identified between rumination time and milk yield (r = 0.491, p < 0.001), while negative correlations were observed between milk yield and milk fat, protein, and the fat-to-protein ratio (p < 0.001). Additionally, lameness was associated with elevated somatic cell counts in the milk, although sample size limitations necessitate further validation. This study highlights the critical role of rumination and milk performance metrics in identifying subclinical lameness, emphasising the utility of automated systems in advancing dairy cow welfare and productivity. The findings underscore the importance of early detection and management strategies to mitigate the economic and welfare impacts of lameness in dairy farming. Full article

(This article belongs to the Section Farm Animal Production)

30 pages, 1316 KB

Open AccessEditor’s ChoiceReview

Melatonin: An Overview on the Synthesis Processes and on Its Multiple Bioactive Roles Played in Animals and Humans

by Vasile-Cosmin Andronachi, Cristina Simeanu, Mădălina Matei, Răzvan-Mihail Radu-Rusu and Daniel Simeanu

Agriculture 2025, 15(3), 273; https://doi.org/10.3390/agriculture15030273 - 27 Jan 2025

Cited by 8 | Viewed by 13114

Abstract

Melatonin is a natural hormone synthesized mainly by the pineal gland of vertebrates, and, secondarily, by other tissues and organs as well. It is deemed a bioactive molecule due to the multiple roles and functions it performs in animals and humans. Research conducted [...] Read more.

Melatonin is a natural hormone synthesized mainly by the pineal gland of vertebrates, and, secondarily, by other tissues and organs as well. It is deemed a bioactive molecule due to the multiple roles and functions it performs in animals and humans. Research conducted up to 2024 has reported the presence of melatonin in a wide variety of plants and bacteria, as well. This review aims to collect some of the scientific data to identify and describe the main sources of melatonin, and to document the functions and roles it plays in animal organisms. It also includes a description of the main technological and nutritional factors that can positively or negatively influence the synthesis and secretion process of melatonin, which is subsequently transported from the animal body into some food products, such as milk. This paper also includes information on the interaction between melatonin and other bioactive compounds present in animal and human bodies, with the aim of identifying what other functions and roles this hormone performs, and whether it interacts with other substances present in the vertebrate organism. Full article

(This article belongs to the Special Issue Farming Factors’ Influence on Animal Productions)

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20 pages, 262 KB

Open AccessEditor’s ChoiceArticle

The Impact of Agricultural Machinery Services on Food Loss at the Producer Level: Evidence from China

by Yan Xu, Jie Lyu, Dandan Yuan, Guanqiu Yin and Junyan Zhang

Agriculture 2025, 15(3), 263; https://doi.org/10.3390/agriculture15030263 - 26 Jan 2025

Cited by 3 | Viewed by 1826

Abstract

Reducing food loss can improve environmental sustainability, resource use, and food security. Agricultural machinery services have considerable advantages in enhancing the adaptability and competitiveness of farms, but little is known about its potential for addressing food loss. Here, this work attempts to reveal [...] Read more.

Reducing food loss can improve environmental sustainability, resource use, and food security. Agricultural machinery services have considerable advantages in enhancing the adaptability and competitiveness of farms, but little is known about its potential for addressing food loss. Here, this work attempts to reveal a strong yet under-discussed connection between agricultural machinery services and food loss. Using survey data covering 483 corn farmers in the Heilongjiang, Jilin, and Liaoning provinces of China from October to December 2024, this study examined the extent to which participation in agricultural machinery services reduced food loss. Our results confirmed the existence of this significant causal effect and estimated 0.864% and 0.862% reductions in weight and value losses in response to a 1% increase in the purchase of agricultural machinery services. The possible mechanisms driving this relationship, including factor allocation optimization and technology introduction, were further verified. A variety of robustness tests were conducted to validate the strength and reliability of the empirical results and address endogeneity issues. Also, to better contextualize the heterogeneous effects of agricultural machinery services on food loss, the differences across production links, land fragmentation, and service quality were explored. By highlighting the important roles of agricultural machinery services in reducing food loss, our analysis contributed to contemporary debates about the long-term linkage between the wide popularization of agricultural machinery services and achieving food security, particularly providing insights for developing countries. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

19 pages, 14103 KB

Open AccessEditor’s ChoiceArticle

DCFA-YOLO: A Dual-Channel Cross-Feature-Fusion Attention YOLO Network for Cherry Tomato Bunch Detection

by Shanglei Chai, Ming Wen, Pengyu Li, Zhi Zeng and Yibin Tian

Agriculture 2025, 15(3), 271; https://doi.org/10.3390/agriculture15030271 - 26 Jan 2025

Cited by 8 | Viewed by 2032

Abstract

To better utilize multimodal information for agriculture applications, this paper proposes a cherry tomato bunch detection network using dual-channel cross-feature fusion. It aims to improve detection performance by employing the complementary information of color and depth images. Using the existing YOLOv8_n as the [...] Read more.

To better utilize multimodal information for agriculture applications, this paper proposes a cherry tomato bunch detection network using dual-channel cross-feature fusion. It aims to improve detection performance by employing the complementary information of color and depth images. Using the existing YOLOv8_n as the baseline framework, it incorporates a dual-channel cross-fusion attention mechanism for multimodal feature extraction and fusion. In the backbone network, a ShuffleNetV2 unit is adopted to optimize the efficiency of initial feature extraction. During the feature fusion stage, two modules are introduced by using re-parameterization, dynamic weighting, and efficient concatenation to strengthen the representation of multimodal information. Meanwhile, the CBAM mechanism is integrated at different feature extraction stages, combined with the improved SPPF_CBAM module, to effectively enhance the focus and representation of critical features. Experimental results using a dataset obtained from a commercial greenhouse demonstrate that DCFA-YOLO excels in cherry tomato bunch detection, achieving an mAP50 of 96.5%, a significant improvement over the baseline model, while drastically reducing computational complexity. Furthermore, comparisons with other state-of-the-art YOLO and other object detection models validate its detection performance. This provides an efficient solution for multimodal fusion for real-time fruit detection in the context of robotic harvesting, running at 52fps on a regular computer. Full article

(This article belongs to the Special Issue Computational, AI and IT Solutions Helping Agriculture)

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18 pages, 8528 KB

Open AccessEditor’s ChoiceArticle

Agricultural Machinery Path Tracking with Varying Curvatures Based on an Improved Pure-Pursuit Method

by Jiawei Zhou, Junhao Wen, Liwen Yao, Zidong Yang, Lijun Xu and Lijian Yao

Agriculture 2025, 15(3), 266; https://doi.org/10.3390/agriculture15030266 - 26 Jan 2025

Cited by 9 | Viewed by 1463

Abstract

The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. To address the issue of low tracking accuracy caused by variable-curvature paths in automatic navigation within agricultural environments, this [...] Read more.

The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. To address the issue of low tracking accuracy caused by variable-curvature paths in automatic navigation within agricultural environments, this study proposes a fuzzy control-based path-tracking method. Firstly, a pure-pursuit model and a kinematic model were established based on a Four-Wheel Independent Steering and Four-Wheel Independent Driving (4WIS-4WID) structure. Secondly, a fuzzy controller with three inputs and one output was designed, using the lateral deviation, d_e; heading deviation, θ_e; and bending degree, c, of the look-ahead path as the input variables. Through multiple simulations and adjustments, 75 control rules were developed. The look-ahead distance, Ld, was obtained through fuzzification, fuzzy inference, and defuzzification processes. Next, a speed-control function was constructed based on the agricultural machinery’s pose deviations and the bending degree of the look-ahead path to achieve variable speed control. Finally, field tests were conducted to verify the effectiveness of the proposed path-tracking method. The tracking experiment results for the two types of paths indicate that under the variable-speed dynamic look-ahead distance strategy, the average lateral deviations for the variable-curvature paths were 1.8 cm and 3.3 cm while the maximum lateral deviations were 10.1 cm and 10.5 cm, respectively. Compared to the constant-speed fixed look-ahead pure-pursuit model, the average lateral deviation was reduced by 56.1% and the maximum lateral deviation by 50.4% on the U-shaped path. On the S-shaped path, the average lateral deviation was reduced by 56.0% and the maximum lateral deviation by 58.9%. The proposed method effectively improves the path-tracking accuracy of agricultural machinery on variable-curvature paths, meeting the production requirements for curved operations in agricultural environments. Full article

(This article belongs to the Section Agricultural Technology)

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39 pages, 3037 KB

Open AccessEditor’s ChoiceReview

Red Beetroot and Its By-Products: A Comprehensive Review of Phytochemicals, Extraction Methods, Health Benefits, and Applications

by Florina Stoica, Gabriela Râpeanu, Roxana Nicoleta Rațu, Nicoleta Stănciuc, Constantin Croitoru, Denis Țopa and Gerard Jităreanu

Agriculture 2025, 15(3), 270; https://doi.org/10.3390/agriculture15030270 - 26 Jan 2025

Cited by 10 | Viewed by 18437

Abstract

Beetroot (Beta vulgaris), a root vegetable known for its vivid natural color and nutritional profile, is a source of a wide range of bioactive compounds, including betalains, phenolics, vitamins, and antioxidants. These bioactive compounds are associated with many health-promoting properties, including [...] Read more.

Beetroot (Beta vulgaris), a root vegetable known for its vivid natural color and nutritional profile, is a source of a wide range of bioactive compounds, including betalains, phenolics, vitamins, and antioxidants. These bioactive compounds are associated with many health-promoting properties, including antihypertensive, antioxidant, anti-inflammatory, and anticancer effects. The beetroot processing industry produces substantial by-products abundant in phytochemicals and betalains, presenting valuable opportunities for utilization. Therefore, it can replace synthetic additives and enhance the nutritional value of foods. By reducing waste and supporting a circular economy, beetroot by-products improve resource efficiency, cut production costs, and lessen the food industry’s environmental impact. Beetroot and its by-products are rich in phytochemicals that provide various wellness advantages. They support cardiovascular health, inhibit microbe-induced food spoiling, aid liver function, and reduce inflammation and oxidative stress. This paper presents a detailed review of current knowledge on beetroot and its by-products, focusing on their biochemical components, extraction and stabilization techniques, health benefits, and potential applications in the food industry. It underscores the versatility and importance of red beetroot and its derivatives, advocating for further research into optimized processing methods and innovative uses to enhance their industrial and nutritional value. By providing valuable insights, this review aims to inspire food scientists, nutritionists, and the agricultural sector to integrate beetroot and its by-products into more sustainable and health-oriented food systems. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

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25 pages, 67333 KB

Open AccessEditor’s ChoiceArticle

Spray Quality Assessment on Water-Sensitive Paper Comparing AI and Classical Computer Vision Methods

by Inês Simões, Armando Jorge Sousa, André Baltazar and Filipe Santos

Agriculture 2025, 15(3), 261; https://doi.org/10.3390/agriculture15030261 - 25 Jan 2025

Cited by 3 | Viewed by 1919

Abstract

Precision agriculture seeks to optimize crop yields while minimizing resource use. A key challenge is achieving uniform pesticide spraying to prevent crop damage and environmental contamination. Water-sensitive paper (WSP) is a common tool used for assessing spray quality, as it visually registers droplet [...] Read more.

Precision agriculture seeks to optimize crop yields while minimizing resource use. A key challenge is achieving uniform pesticide spraying to prevent crop damage and environmental contamination. Water-sensitive paper (WSP) is a common tool used for assessing spray quality, as it visually registers droplet impacts through color change. This work introduces a smartphone-based solution for capturing WSP images within vegetation, offering a tool for farmers to assess spray quality in real-world conditions. To achieve this, two approaches were explored: classical computer vision techniques and machine learning (ML) models (YOLOv8, Mask-RCNN, and Cellpose). Addressing the challenges of limited real-world data and the complexity of manual annotation, a programmatically generated synthetic dataset was employed to enable sim-to-real transfer learning. For the task of WSP segmentation within vegetation, YOLOv8 achieved an average Intersection over Union of 97.76%. In the droplet detection task, which involves identifying individual droplets on WSP, Cellpose achieved the highest precision of 96.18%, in the presence of overlapping droplets. While classical computer vision techniques provided a reliable baseline, they struggled with complex cases. Additionally, ML models, particularly Cellpose, demonstrated accurate droplet detection even without fine-tuning. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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31 pages, 2770 KB

Open AccessEditor’s ChoiceArticle

Digital Revolution in Agriculture: Using Predictive Models to Enhance Agricultural Performance Through Digital Technology

by Anca Antoaneta Vărzaru

Agriculture 2025, 15(3), 258; https://doi.org/10.3390/agriculture15030258 - 24 Jan 2025

Cited by 11 | Viewed by 5040

Abstract

Digital innovation in agriculture has become a powerful force in the modern world as it revolutionizes the agricultural sector and improves the sustainability and efficacy of farming practices. In this context, the study examines the effects of digital technology, as reflected by the [...] Read more.

Digital innovation in agriculture has become a powerful force in the modern world as it revolutionizes the agricultural sector and improves the sustainability and efficacy of farming practices. In this context, the study examines the effects of digital technology, as reflected by the digital economy and society index (DESI), on key agricultural performance metrics, including agricultural output and real labor productivity per person. The paper develops a strong analytical method for quantifying these associations using predictive models, such as exponential smoothing, ARIMA, and artificial neural networks. The method fully illustrates how economic and technological components interact, including labor productivity, agricultural output, and GDP per capita. The results demonstrate that digital technologies significantly impact agricultural output and labor productivity. These findings illustrate the importance of digital transformation in modernizing and improving agriculture’s overall efficacy. The study’s conclusion highlights the necessity of integrating digital technology into agricultural policy to address productivity problems and nurture sustainable growth in the sector. Full article

(This article belongs to the Special Issue Feature Papers in the Fields of Agricultural Economics, Policies and/or Rural Management)

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16 pages, 3162 KB

Open AccessEditor’s ChoiceArticle

Metabolomics Combined with Transcriptomics Reveals the Formation Mechanism of Different Colored Flowers of Cosmos bipinnata Cav.

by Yuxi Wang, Xiaodong Yang, Qi Zhou, Xiaohua Meng, Jialin Peng and Yueheng Hu

Agriculture 2025, 15(3), 255; https://doi.org/10.3390/agriculture15030255 - 24 Jan 2025

Cited by 3 | Viewed by 1258

Abstract

In nature, plants have rich and vivid colors. Flower color can confer economic and ornamental value to ornamental plants, and is one of the target traits for current directed breeding. Therefore, it is essential to understand the molecular regulatory mechanisms behind flower color [...] Read more.

In nature, plants have rich and vivid colors. Flower color can confer economic and ornamental value to ornamental plants, and is one of the target traits for current directed breeding. Therefore, it is essential to understand the molecular regulatory mechanisms behind flower color formation in ornamental plants. However, in Cosmos bipinnata Cav., one of the most important ornamental plants, the metabolic pathways and molecular regulatory mechanisms underlying the formation of different flower colors are not yet clear, which greatly restricts the molecular breeding of flower color varieties. We selected three varieties of Cosmos bipinnata Cav. with white, pink, and red flowers as research materials, and identified significantly different metabolites among them through ultra performance liquid chromatography mass spectrometry (UPLC-MS/MS) analysis and principal component analysis (PCA). Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and transcriptome sequencing analysis in different colors flowers were used to reveal that the differential metabolites were enriched in flavonoid metabolic pathways and related structural genes were differentially expressed. Furthermore, we identified differentially expressed members of the MYB and bHLH transcription factor families, which play key roles in regulating the anthocyanin biosynthesis. By constructing a phylogenetic tree and performing a joint analysis of transcriptome and metabolome data, we further elucidated the molecular regulatory network underlying the formation of flower colors in Cosmos bipinnata Cav. This study not only provides a theoretical basis and gene resources for color-oriented breeding and the creation of new color varieties, but also offers new insights into the molecular mechanisms of flower color formation in plants. Full article

(This article belongs to the Special Issue Genetics, Breeding and Transcriptomic Analysis of Chrysanthemum)

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31 pages, 4673 KB

Open AccessEditor’s ChoiceReview

Conservation Soil Tillage: Bridging Science and Farmer Expectations—An Overview from Southern to Northern Europe

by Danijel Jug, Irena Jug, Bojana Brozović, Srdjan Šeremešić, Željko Dolijanović, Jozsef Zsembeli, Apolka Ujj, Jana Marjanovic, Vladimir Smutny, Soňa Dušková, Lubomír Neudert, Milan Macák, Edward Wilczewski and Boris Đurđević

Agriculture 2025, 15(3), 260; https://doi.org/10.3390/agriculture15030260 - 24 Jan 2025

Cited by 7 | Viewed by 5522

Abstract

Soil degradation and climate change are the most destructive (human- and/or naturally induced) processes, making agricultural production more challenging than ever before. Traditional tillage methods, characterized by intensive mechanical soil disturbance (dominantly using a plow), have come under question for their role in [...] Read more.

Soil degradation and climate change are the most destructive (human- and/or naturally induced) processes, making agricultural production more challenging than ever before. Traditional tillage methods, characterized by intensive mechanical soil disturbance (dominantly using a plow), have come under question for their role in exacerbating soil erosion, depleting organic matter, and contributing to the decline in soil biodiversity and other soil devastating processes. These practices, while effective in the short term for crop production, undermine the sustainability of agricultural systems, posing a threat to food security and environmental stability. This review examines the adoption and implementation of Conservation Soil Tillage (CST) across six European countries: Croatia, Serbia, Hungary, Slovakia, Czech Republic, and Poland. The main objective is to analyze the historical development, current status, and future prospects of CST in these countries, highlighting the challenges and opportunities in transitioning from conventional tillage methods. Conservation Soil Tillage (CST) emerges as a promising alternative platform to still dominant conventional plowing tillage approach. By reducing the intensity and frequency of tillage, CST practices aim to maintain adequate soil cover, minimize erosion, and encourage biological activity and organic matter accumulation, thus, ensuring soil productivity and resilience against additional degradation and climate variation. Efforts made by scientists and the government to go over it sometimes are not sufficient. Farmers’ expectations of benefits are the final keystone for the integration of CST as a dominant sustainable practice. Analyses from six European countries pointed to a high level of diversity in readiness and willingness to accept, as well as different levels of knowledge about the adoption of CST. Our study suggested that the adoption of CST is increasing, and it represents a key strategy for soil degradation prevention and climate change mitigation. Full article

(This article belongs to the Special Issue The Role of Agriculture in Climate Change Adaptation and Mitigation)

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29 pages, 436 KB

Open AccessEditor’s ChoiceReview

Unlocking the Power of Eggs: Nutritional Insights, Bioactive Compounds, and the Advantages of Omega-3 and Omega-6 Enriched Varieties

by Marius Giorgi Usturoi, Roxana Nicoleta Rațu, Ioana Cristina Crivei, Ionuț Dumitru Veleșcu, Alexandru Usturoi, Florina Stoica and Răzvan-Mihail Radu Rusu

Agriculture 2025, 15(3), 242; https://doi.org/10.3390/agriculture15030242 - 23 Jan 2025

Cited by 16 | Viewed by 19390

Abstract

This study explores the nutritional benefits and health implications of omega-3- and omega-6-enriched eggs, positioning them within the context of functional foods aimed at improving public health outcomes. With rising consumer interest in nutritionally fortified foods, omega-enriched eggs have emerged as a viable [...] Read more.

This study explores the nutritional benefits and health implications of omega-3- and omega-6-enriched eggs, positioning them within the context of functional foods aimed at improving public health outcomes. With rising consumer interest in nutritionally fortified foods, omega-enriched eggs have emerged as a viable source of essential fatty acids, offering potential benefits for cardiovascular health, inflammation reduction, and cognitive function. This research examines enrichment techniques, particularly dietary modifications for laying hens, such as the inclusion of flaxseed and algae, to enhance omega-3 content and balance the omega-6-to-omega-3 ratio in eggs. The findings indicate that enriched eggs provide significantly higher levels of essential fatty acids and bioactive compounds than conventional eggs, aligning with dietary needs in populations with limited access to traditional omega-3 sources like fish. This study further addresses consumer perception challenges, regulatory constraints, and environmental considerations related to sustainable production practices. The conclusions underscore the value of omega-enriched eggs as a functional food that aligns with health-conscious dietary trends and recommend ongoing research to refine enrichment methods and expand market accessibility. Full article

(This article belongs to the Special Issue Farming Factors’ Influence on Animal Productions)

23 pages, 7919 KB

Open AccessEditor’s ChoiceArticle

Interpretable LAI Fine Inversion of Maize by Fusing Satellite, UAV Multispectral, and Thermal Infrared Images

by Yu Yao, Hengbin Wang, Xiao Yang, Xiang Gao, Shuai Yang, Yuanyuan Zhao, Shaoming Li, Xiaodong Zhang and Zhe Liu

Agriculture 2025, 15(3), 243; https://doi.org/10.3390/agriculture15030243 - 23 Jan 2025

Cited by 3 | Viewed by 1723

Abstract

Leaf area index (LAI) serves as a crucial indicator for characterizing the growth and development process of maize. However, the LAI inversion of maize based on unmanned aerial vehicles (UAVs) is highly susceptible to various factors such as weather conditions, light intensity, and [...] Read more.

Leaf area index (LAI) serves as a crucial indicator for characterizing the growth and development process of maize. However, the LAI inversion of maize based on unmanned aerial vehicles (UAVs) is highly susceptible to various factors such as weather conditions, light intensity, and sensor performance. In contrast to satellites, the spectral stability of UAV-based data is relatively inferior, and the phenomenon of “spectral fragmentation” is prone to occur during large-scale monitoring. This study was designed to solve the problem that maize LAI inversion based on UAVs is difficult to achieve both high spatial resolution and spectral consistency. A two-stage remote sensing data fusion method integrating coarse and fine fusion was proposed. The SHapley Additive exPlanations (SHAP) model was introduced to investigate the contributions of 20 features in 7 categories to LAI inversion of maize, and canopy temperature extracted from thermal infrared images was one of them. Additionally, the most suitable feature sampling window was determined through multi-scale sampling experiments. The grid search method was used to optimize the hyperparameters of models such as Gradient Boosting, XGBoost, and Random Forest, and their accuracy was compared. The results showed that, by utilizing a 3 × 3 feature sampling window and 9 features with the highest contributions, the LAI inversion accuracy of the whole growth stage based on Random Forest could reach R² = 0.90 and RMSE = 0.38 m²/m². Compared with the single UAV data source mode, the inversion accuracy was enhanced by nearly 25%. The R² in the jointing, tasseling, and filling stages were 0.87, 0.86, and 0.62, respectively. Moreover, this study verified the significant role of thermal infrared data in LAI inversion, providing a new method for fine LAI inversion of maize. Full article

(This article belongs to the Topic Advances in Smart Agriculture with Remote Sensing as the Core and Its Applications in Crops Field)

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25 pages, 2148 KB

Open AccessEditor’s ChoiceArticle

Isolation of Acetic Acid-Producing Bacterial Strains and Utilization as Microbial Inoculants in Sorghum Silages

by Guilherme Medeiros Leite, Edson Mauro Santos, Juliana Silva de Oliveira, Danillo Marte Pereira, Celso José Bruno de Oliveira, Jorge Vinicius Fernandes Lima Cavalcanti, Vanessa Maria Rodrigues de Lima, João Paulo Vieira de Melo Fernandes de Lima, Paloma Gabriela Batista Gomes, Ricardo Loiola Edvan, Rafael de Souza Miranda, Daniele de Jesus Ferreira, Fagton de Mattos Negrão and Anderson de Moura Zanine

Agriculture 2025, 15(3), 241; https://doi.org/10.3390/agriculture15030241 - 23 Jan 2025

Cited by 4 | Viewed by 1606

Abstract

This study aimed to isolate, characterize, and identify acetic acid-producing lactic acid bacteria from fresh sorghum plants and silage, and to evaluate the effect of the isolates as microbial inoculants on taxonomic diversity and silage fermentation quality. For the first experimental stage, eight [...] Read more.

This study aimed to isolate, characterize, and identify acetic acid-producing lactic acid bacteria from fresh sorghum plants and silage, and to evaluate the effect of the isolates as microbial inoculants on taxonomic diversity and silage fermentation quality. For the first experimental stage, eight experimental silos were prepared, and the fresh sorghum plant cv. BRS Ponta Negra (Sorghum bicolor (L.) Moench.) was sampled to characterize and identify the bacteria. Five strains were chosen to be inoculated in the second experimental stage, in a 7 × 2 factorial design, with seven treatments and two opening times, in four replications. Four types of species were identified, with Lactiplantibacillus plantarum predominating at 72.73%. There was an interaction effect between treatments and opening times on effluent losses, gas losses, the population of lactic acid bacteria, yeasts, and lactic acid content. The aerobic stability treatments that stood out were Lactiplantibacillus plantarum (GML 66) and Weissella cibaria, which showed 71.75 and 68.87 h of stability. The use of Lactiplantibacillus plantarum (GML 66) as a microbial inoculant in sorghum silage increased the dry matter content, reduced effluent losses, and improved dry matter recovery. It also reduced the yeast population in the silage, promoting greater aerobic stability in the silage. Full article

(This article belongs to the Special Issue Current Challenges in Microbiology and Chemistry of Animal Feed)

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22 pages, 1920 KB

Open AccessEditor’s ChoiceArticle

Balancing Tradition and Innovation: The Role of Environmental Conservation Agriculture in the Sustainability of the Ifugao Rice Terraces

by Keshav Lall Maharjan, Clarisse Mendoza Gonzalvo and Jude Cadingpal Baggo

Agriculture 2025, 15(3), 246; https://doi.org/10.3390/agriculture15030246 - 23 Jan 2025

Cited by 4 | Viewed by 8632

Abstract

This study investigates the continuation of Environmental Conservation Agriculture (ECA) practices among farmers in the Ifugao Rice Terraces, a Globally Important Agricultural Heritage System (GIAHS) in the Philippines. Through a cross-sectional survey of ECA farmers in the municipality of Banaue, this research explores [...] Read more.

This study investigates the continuation of Environmental Conservation Agriculture (ECA) practices among farmers in the Ifugao Rice Terraces, a Globally Important Agricultural Heritage System (GIAHS) in the Philippines. Through a cross-sectional survey of ECA farmers in the municipality of Banaue, this research explores the socio-demographic, environmental, and economic factors influencing the adoption and persistence of ECA. The findings reveal that while access to resources such as high-yielding seeds, modern farming equipment, and financial support is important for the adoption of ECA, the shift toward high-yielding varieties has contributed to a decline in the cultivation of Tinawon rice, which is vital for maintaining the ecological balance and cultural heritage of the terraces. This study underscores the importance of balancing modern agricultural practices with the continued cultivation of Tinawon rice to preserve biodiversity, soil health, and cultural identity, while also enhancing agricultural productivity. Additionally, the roles of community-based support systems, market access, and financial incentives are highlighted as key factors in sustaining ECA practices. Climate change presents both challenges and opportunities for adaptation, making it essential to integrate traditional knowledge with modern techniques to build resilience. Understanding the factors that shape ECA continuation is crucial for refining initiatives that address both the economic and cultural contexts. By emphasizing the importance of tailored, community-driven interventions, this study provides critical insights for enhancing ECA adoption in the Ifugao Rice Terraces, contributing to climate resilience and the long-term sustainability of this significant agricultural heritage system. Full article

(This article belongs to the Special Issue Economic Development of Rural Areas in Border Territories: Threats and Opportunities)

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24 pages, 41622 KB

Open AccessEditor’s ChoiceArticle

Picking-Point Localization Algorithm for Citrus Fruits Based on Improved YOLOv8 Model

by Yun Liang, Weipeng Jiang, Yunfan Liu, Zihao Wu and Run Zheng

Agriculture 2025, 15(3), 237; https://doi.org/10.3390/agriculture15030237 - 22 Jan 2025

Cited by 7 | Viewed by 1972

Abstract

The citrus picking-point localization is critical for automatic citrus harvesting. Due to the complex citrus growing environment and the limitations of devices, the efficient citrus picking-point localization method becomes a hot research topic. This study designs a novel and efficient workflow for citrus [...] Read more.

The citrus picking-point localization is critical for automatic citrus harvesting. Due to the complex citrus growing environment and the limitations of devices, the efficient citrus picking-point localization method becomes a hot research topic. This study designs a novel and efficient workflow for citrus picking-point localization, named as CPPL. The CPPL is achieved based on two stages, namely the detection stage and the segmentation stage. For the detection stage, we define the KD-YOLOP to accurately detect citrus fruits to quickly localize the initial picking region. The KD-YOLOP is defined based on a knowledge distillation learning and a model pruning to reduce the computational cost while having a competitive accuracy. For the segmentation stage, we define the RG-YOLO-seg to efficiently segment the citrus branches to compute the picking points. The RG-YOLO-seg is proposed by introducing the RGNet to extract efficient features and using the GSNeck to fuse multi-scale features. Therefore, by using knowledge distillation, model pruning, and a lightweight model for branch segmentation, the proposed CPPL achieves accurate real-time localization of citrus picking points. We conduct extensive experiments to evaluate our method; many results show that the proposed CPPL outperforms the current methods and achieves adequate accuracy. It provides an efficient and robust novel method for real-time citrus harvesting in practical agricultural applications. Full article

(This article belongs to the Special Issue Research Advances in Perception for Agricultural Robots)

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33 pages, 18193 KB

Open AccessEditor’s ChoiceArticle

Research on Traversal Path Planning and Collaborative Scheduling for Corn Harvesting and Transportation in Hilly Areas Based on Dijkstra’s Algorithm and Improved Harris Hawk Optimization

by Huanyu Liu, Jiahao Luo, Lihan Zhang, Hao Yu, Xiangnan Liu and Shuang Wang

Agriculture 2025, 15(3), 233; https://doi.org/10.3390/agriculture15030233 - 22 Jan 2025

Cited by 9 | Viewed by 1964

Abstract

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in the collaborative harvesting of corn by harvesters and grain transport vehicles in hilly areas. A path-planning and collaborative scheduling method is proposed, combining Dijkstra’s algorithm with [...] Read more.

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in the collaborative harvesting of corn by harvesters and grain transport vehicles in hilly areas. A path-planning and collaborative scheduling method is proposed, combining Dijkstra’s algorithm with the Improved Harris Hawk Optimization (IHHO) algorithm. A field model based on Digital Elevation Model (DEM) data is created for full coverage path planning, reducing traversal path length. A field transfer road network is established, and Dijkstra’s algorithm is used to calculate distances between fields. A multi-objective collaborative scheduling model is then developed to minimize fuel consumption, scheduling costs, and time. The IHHO algorithm enhances search performance by introducing quantum initialization to improve the initial population, integrating the slime mold algorithm for better exploration, and applying an average differential mutation strategy and nonlinear energy factor updates to strengthen both global and local search. Non-dominated sorting and crowding distance techniques are incorporated to enhance solution diversity and quality. The results show that compared to traditional HHO and HHO algorithms, the IHHO algorithm reduces average scheduling costs by 4.2% and 14.5%, scheduling time by 4.5% and 8.1%, and fuel consumption by 3.5% and 3.2%, respectively. This approach effectively reduces transfer path costs, saves energy, and improves operational efficiency, providing valuable insights for path planning and collaborative scheduling in multi-field harvesting and transportation in hilly areas. Full article

(This article belongs to the Special Issue New Energy-Powered Agricultural Machinery and Equipment)

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22 pages, 9769 KB

Open AccessEditor’s ChoiceArticle

Soil Enzyme Activities and Microbial Carbon Pump Promote Carbon Storage by Influencing Bacterial Communities Under Nitrogen-Rich Conditions in Tea Plantation

by Qi Shu, Shenghua Gao, Xinmiao Liu, Zengwang Yao, Hailong Wu, Lianghua Qi and Xudong Zhang

Agriculture 2025, 15(3), 238; https://doi.org/10.3390/agriculture15030238 - 22 Jan 2025

Cited by 4 | Viewed by 2123

Abstract

Carbon–nitrogen (C-N) coupling is a fundamental concept in ecosystem ecology. Long-term excessive fertilization in tea plantations has caused soil C-N imbalance, leading to ecological issues. Understanding soil C-N coupling under nitrogen loading is essential for sustainable management, yet the mechanisms remain unclear. This [...] Read more.

Carbon–nitrogen (C-N) coupling is a fundamental concept in ecosystem ecology. Long-term excessive fertilization in tea plantations has caused soil C-N imbalance, leading to ecological issues. Understanding soil C-N coupling under nitrogen loading is essential for sustainable management, yet the mechanisms remain unclear. This study examined C-N coupling in tea plantation soils under five fertilization regimes: no fertilization, chemical fertilizer, chemical + organic cake fertilizer, chemical + microbial fertilizer, and chemical + biochar. Fertilization mainly increased particulate organic carbon (POC) and inorganic nitrogen, driven by changes in bacterial community composition and function. Mixed fertilization treatments enhanced the association between bacterial communities and soil properties, increasing ecological complexity without altering overall trends. Fungal communities had a minor influence on soil C-N dynamics. Microbial necromass carbon (MNC) and microbial carbon pump (MCP) efficacy, representing long-term carbon storage potential, showed minimal responses to short-term fertilization. However, the microbial necromass accumulation coefficient (NAC) was nitrogen-sensitive, indicating short-term responses. PLS-PM analysis revealed consistent C-N coupling across the treatments, where soil nitrogen influenced carbon through enzyme activity and MCP, while bacterial communities directly affected carbon storage. These findings provide insights for precise soil C-N management and sustainable tea plantation practices under climate change. Full article

(This article belongs to the Section Agricultural Soils)

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18 pages, 4117 KB

Open AccessEditor’s ChoiceArticle

Consumption of Nitrogen Fertilizers in the EU—External Costs of Their Production by Country of Application

by Agnieszka Sobolewska and Marcin Bukowski

Agriculture 2025, 15(3), 224; https://doi.org/10.3390/agriculture15030224 - 21 Jan 2025

Cited by 5 | Viewed by 3965

Abstract

The production of nitrogen fertilizers results in multiple negative environmental impacts. A particularly important aspect is its energy consumption. Analyses covering the product’s life cycle indicate that the greatest environmental harm is generated at the stage of production due to the resulting nitrogen [...] Read more.

The production of nitrogen fertilizers results in multiple negative environmental impacts. A particularly important aspect is its energy consumption. Analyses covering the product’s life cycle indicate that the greatest environmental harm is generated at the stage of production due to the resulting nitrogen dioxide emissions. The aim of this study was to assess the economic value of the environmental harm caused by the production of the nitrogen fertilizers used in EU farming. The assessment of the environmental damage resulting from the production of mineral nitrogen fertilizers was conducted through a life cycle assessment (LCA). A ‘gate-to-gate’ approach was applied using Sima Pro 7.1.0.2 software, with the ecoinvent 3 and agri-footprint 5 databases. The value of the external costs for the production of nitrogen fertilizers was determined by applying the environmental prices method. The analysis conducted covered the years 2012–2021. The results indicated a decrease in the environmental damage caused by the production of mineral nitrogen fertilizers used in EU agriculture. There was considerable disparity between individual EU member countries, both in terms of trends concerning the amounts of applied nitrogen fertilizer and the efficacy of their use. In the years 2012–2021 in 18 EU countries, the amount of mineral nitrogen fertilizers used in farming grew, with the greatest increases in Romania, Spain, and Hungary, whereas in 9 countries, their use dropped, with the greatest decreases recorded in Germany, France, and Poland. Marked differences were also found in the efficacy of the use of mineral nitrogen fertilizers, as measured based on the value of the environmental harm caused by the production of the applied fertilizers in relation to the value of the field crop produced. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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31 pages, 7647 KB

Open AccessEditor’s ChoiceSystematic Review

Applications of Raspberry Pi for Precision Agriculture—A Systematic Review

by Astina Joice, Talha Tufaique, Humeera Tazeen, C. Igathinathane, Zhao Zhang, Craig Whippo, John Hendrickson and David Archer

Agriculture 2025, 15(3), 227; https://doi.org/10.3390/agriculture15030227 - 21 Jan 2025

Cited by 8 | Viewed by 8724

Abstract

Precision agriculture (PA) is a farm management data-driven technology that enhances production with efficient resource usage. Existing PA methods rely on data processing, highlighting the need for a portable computing device for real-time, infield decisions. Raspberry Pi, a cost-effective multi-OS single-board computer, addresses [...] Read more.

Precision agriculture (PA) is a farm management data-driven technology that enhances production with efficient resource usage. Existing PA methods rely on data processing, highlighting the need for a portable computing device for real-time, infield decisions. Raspberry Pi, a cost-effective multi-OS single-board computer, addresses this gap. However, information on Raspberry Pi’s use in PA remains limited. This review consolidates details on Raspberry Pi versions, sensors, devices, algorithm deployment, and PA applications. A systematic literature review of three academic databases (Scopus, Web of Science, IEEE Xplore) yielded 84 (as of 22 November 2024) articles based on four research questions and screening criteria (exclusion and inclusion). Narrative synthesis and subgroup analysis were used to synthesize the results. Findings suggest Raspberry Pi can be a central unit to control sensors, enabling cost-effective automated decision support for PA, particularly in plant disease detection, site-specific weed management, plant phenotyping, biomass estimation, and irrigation systems. Despite focusing on these areas, further research is essential on other PA applications such as livestock monitoring, UAV-based applications, and farm management software. Additionally, Raspberry Pi can be used as a valuable learning tool for students, researchers, and farmers and can promote PA adoption globally, helping stakeholders realize its potential. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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20 pages, 1243 KB

Open AccessEditor’s ChoiceArticle

Impact of On-Farm Demonstrations on Technology Adoption, Yield, and Profitability Among Small Farmers of Wheat in Pakistan—An Experimental Study

by Nadia Hussain and Keshav Lall Maharjan

Agriculture 2025, 15(2), 214; https://doi.org/10.3390/agriculture15020214 - 20 Jan 2025

Cited by 5 | Viewed by 4859

Abstract

Do the intensive demonstrations result in consistent technology adoption and yield enhancement? While extension methods show significant immediate effects of an intervention, their impact may fade over time. In a government-led natural experiment in Pakistan, a long-lasting adoption of certified seeds, fertilizers, and [...] Read more.

Do the intensive demonstrations result in consistent technology adoption and yield enhancement? While extension methods show significant immediate effects of an intervention, their impact may fade over time. In a government-led natural experiment in Pakistan, a long-lasting adoption of certified seeds, fertilizers, and pesticides/herbicides in post-treatment years were observed by employing difference–indifferences with a fixed effect method on panel data. The intervention increased the technology adoption in terms of certified seeds by 34%, fertilizers by 15 kg/ha, and pesticides/herbicides by 0.22 L/ha among adopters for four years. Similarly, the wheat yield increased by 0.41 tons per hectare, and profit increased by 12% among the treatment group compared to the control group. In view of these findings, this study suggests continuing this supervised method of extension to other crops in Pakistan. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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22 pages, 8463 KB

Open AccessEditor’s ChoiceArticle

Pre-Season Precipitation and Temperature Have a Larger Influence on Vegetation Productivity than That of the Growing Season in the Agro-Pastoral Ecotone in Northern China

by Yuanyuan Zhang, Qingtao Wang, Xueyuan Zhang, Zecheng Guo, Xiaonan Guo, Changhui Ma, Baocheng Wei and Lei He

Agriculture 2025, 15(2), 219; https://doi.org/10.3390/agriculture15020219 - 20 Jan 2025

Cited by 4 | Viewed by 1640

Abstract

Climate change and human activities are reshaping the structure and function of terrestrial ecosystems, particularly in vulnerable regions such as agro-pastoral ecotones. However, the extent to which climate change impacts vegetation growth in these areas remains poorly understood, largely due to the modifying [...] Read more.

Climate change and human activities are reshaping the structure and function of terrestrial ecosystems, particularly in vulnerable regions such as agro-pastoral ecotones. However, the extent to which climate change impacts vegetation growth in these areas remains poorly understood, largely due to the modifying effects of human-induced land cover changes on vegetation sensitivity to climatic variations. This study utilizes satellite-derived vegetation indices, land cover datasets, and climate data to investigate the influence of both land cover and climate changes on vegetation growth in the agro-pastoral ecotone of northern China (APENC) from 2001 to 2022. The results reveal that the sensitivity of vegetation productivity, as indicated by the kernel Normalized Difference Vegetation Index (kNDVI), varies depending on the land cover type to climate change in the APENC. Moreover, ridge regression modeling shows that pre-season climate conditions (i.e., pre-season precipitation and temperature) have a stronger positive impact on growing-season vegetation productivity than growing season precipitation and temperature, while the effect of vapor pressure deficit (VPD) is negative. Notably, the kNDVI exhibits significant positive sensitivity (p < 0.05) to precipitation in 34.12% of the region and significant negative sensitivity (p < 0.05) to VPD in 38.80%. The ridge regression model explained 89.10% of the total variation (R² = 0.891). These findings not only emphasize the critical role of both historical and contemporary climate conditions in shaping vegetation growth but also provide valuable insights into how to adjust agricultural and animal husbandry management strategies to improve regional climate adaptation based on climate information from previous seasons in fragile regions. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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19 pages, 4867 KB

Open AccessEditor’s ChoiceArticle

Transcriptomic and Metabolomic Analyses Reveal Differences in Flavonoid Synthesis During Fruit Development of Capsicum frutescens pericarp

by Yinxin Yang, Qihang Cai, Xuan Wang, Yanbo Yang, Liping Li, Zhenghai Sun and Weiwei Li

Agriculture 2025, 15(2), 222; https://doi.org/10.3390/agriculture15020222 - 20 Jan 2025

Cited by 5 | Viewed by 1606

Abstract

Capsicum frutescens is a valuable economic crop that is widely cultivated for its unique flavor and rich nutritional content. While some studies have shown differences in flavonoid content among different chili species, the mechanism by which changes in flavonoid composition lead to fruit [...] Read more.

Capsicum frutescens is a valuable economic crop that is widely cultivated for its unique flavor and rich nutritional content. While some studies have shown differences in flavonoid content among different chili species, the mechanism by which changes in flavonoid composition lead to fruit color variations in C. frutescens remains underreported. We performed transcriptomics and widely targeted metabolome sequencing on three different growth stages of the C. frutescens fruit and analyzed the data to better understand the mechanism of color change. Based on previous research on the genes that regulate flavonoid compounds and the MBW complex, we have identified a total of 28 core genes related to flavonoid biosynthesis and 8 genes that may be related to flavonoid synthesis. Through extensive targeted metabolomic analysis, 581 differential metabolites were identified, including 43 flavonoids. Most anthocyanins, flavonols, and flavonoids were found to be more abundant during the immature fruit stage, which we presume is associated with the differential expression of genes involved in flavonoid biosynthesis and regulation. These findings provide a useful reference for understanding flavonoid synthesis and the accumulation of fruits in C. frutescens. Full article

(This article belongs to the Section Crop Genetics, Genomics and Breeding)

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18 pages, 4423 KB

Open AccessEditor’s ChoiceArticle

A Compliant Active Roller Gripper with High Positional Offset Tolerance for Delicate Spherical Fruit Handling

by Haoran Zhu, Huanhuan Qin, Zicheng Qiu, Xinwen Chen, Jinlin Xue, Xingjian Gu and Mingzhou Lu

Agriculture 2025, 15(2), 220; https://doi.org/10.3390/agriculture15020220 - 20 Jan 2025

Cited by 5 | Viewed by 1893

Abstract

In the field of agricultural robotics, robotic grippers play an indispensable role, directly influencing the rate of fruit damage and handling efficiency. Currently, traditional agricultural robotic grippers face challenges such as high damage rates and high requirements for position control. A robotic gripper [...] Read more.

In the field of agricultural robotics, robotic grippers play an indispensable role, directly influencing the rate of fruit damage and handling efficiency. Currently, traditional agricultural robotic grippers face challenges such as high damage rates and high requirements for position control. A robotic gripper for stable spherical fruit handling with high positional offset tolerance and a low fruit damage rate is proposed in this paper. It adopts a three-finger structure. A flexible active roller is configured at the end of each finger, allowing fruit translation with just a gentle touch. An integrated pressure sensor within the active roller further enhances the gripper’s compliance. To describe the effect of the gripper on the fruit, the interaction model was derived. Taking the tomato as a typical soft and fragile spherical fruit, three experiments were conducted to evaluate the performance of the proposed gripper. The experimental results demonstrated the handling capability of the gripper and the maximum graspable weight reached 2077 g. The average failure rate for the unilateral offset of 9 mm was only 1.33%, and for the bilateral offset of 6-6 mm was 4%, indicating the high positional offset tolerance performance and a low fruit damage rate of the gripper. The preliminary tomato-picking capability of the proposed gripper was also validated in a simplified laboratory scenario. Full article

(This article belongs to the Section Agricultural Technology)

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36 pages, 25401 KB

Open AccessEditor’s ChoiceArticle

Analysis of Spatiotemporal Dynamics and Driving Factors of China’s Nationally Important Agricultural Heritage Systems

by Fei Ju, Rui Yang and Chun Yang

Agriculture 2025, 15(2), 221; https://doi.org/10.3390/agriculture15020221 - 20 Jan 2025

Cited by 5 | Viewed by 2067

Abstract

China’s Nationally Important Agricultural Heritage Systems (China-NIAHS) are agricultural systems with deep historical and cultural roots that exhibit temporal continuity and spatial heterogeneity in their formation and distribution. As modern and industrialized agriculture have developed, traditional agricultural systems are facing unprecedented challenges and [...] Read more.

China’s Nationally Important Agricultural Heritage Systems (China-NIAHS) are agricultural systems with deep historical and cultural roots that exhibit temporal continuity and spatial heterogeneity in their formation and distribution. As modern and industrialized agriculture have developed, traditional agricultural systems are facing unprecedented challenges and pressures. This study investigates the spatiotemporal distribution and influencing factors of 196 China-NIAHS sites, categorized into five categories. Using spatial analysis techniques and Geographical Detectors, this study identifies key natural, socioeconomic, and cultural drivers shaping their distribution. The results reveal a predominantly clustered spatial distribution of China-NIAHS, centered around the Yangtze River Basin, with significant influences from population density, tourism development, and industrialization. Historical analysis highlights a west-to-east and northward migration of agricultural activity, driven by political stability and technological advancements. Further findings indicate that the spatial distribution of China-NIAHS is primarily determined by population density, tourism development, and river network density. Population density plays a pivotal role in heritage preservation, tourism development generates economic benefits and facilitates cultural dissemination, and river network density supports the formation and sustainability of heritage sites. Conversely, urbanization and economic development have limited influence, emphasizing the need to prioritize socioeconomic and natural factors in conservation strategies. This study provides a comprehensive understanding of the spatial and temporal dynamics of China-NIAHS, offering valuable insights for sustainable heritage conservation and the strategic integration of natural and socioeconomic factors into modern agricultural policies. These findings deepen the understanding of China-NIAHS, highlighting their role in ecological and cultural sustainability while supporting value assessment, region-specific protection, and sustainable utilization strategies. Full article

(This article belongs to the Special Issue Exploring, Assessing and Utilizing the Multifunctional Value of Agricultural Heritage Systems)

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20 pages, 998 KB

Open AccessEditor’s ChoiceArticle

Effects of Dietary Starch Concentration on Milk Production, Nutrient Digestibility, and Methane Emissions in Mid-Lactation Dairy Cows

by Rebecca L. Culbertson, Fabian A. Gutiérrez-Oviedo, Pinar Uzun, Nirosh Seneviratne, Ananda B. P. Fontoura, Brianna K. Yau, Josie L. Judge, Amanda N. Davis, Diana C. Reyes and Joseph W. McFadden

Agriculture 2025, 15(2), 211; https://doi.org/10.3390/agriculture15020211 - 19 Jan 2025

Cited by 4 | Viewed by 3815

Abstract

Our objective was to evaluate the effects of dietary starch concentration on milk production, nutrient digestibility, and methane emissions in lactating dairy cows. Thirty mid-lactation cows were randomly assigned to either a high-neutral-detergent-fiber, low-starch diet (LS; 20.2% starch) or a low-neutral-detergent-fiber, high-starch diet [...] Read more.

Our objective was to evaluate the effects of dietary starch concentration on milk production, nutrient digestibility, and methane emissions in lactating dairy cows. Thirty mid-lactation cows were randomly assigned to either a high-neutral-detergent-fiber, low-starch diet (LS; 20.2% starch) or a low-neutral-detergent-fiber, high-starch diet (HS; 25.2% starch) following a 3-week acclimation. The study lasted 8 weeks, with milk sampling and gas measurements conducted weekly during acclimation and at weeks 2, 4, 6, and 8. Blood and fecal samples were collected during acclimation and week 8. Compared with LS cows, HS cows produced 1.9 kg/d more energy-corrected milk (4.45% increase), with higher yields of true protein (+0.13 kg/day), lactose (+0.10 kg/day), and total solids (+0.24 kg/day). Dry matter and organic matter digestibility was 4.2 and 4.3% higher, respectively, in the HS group. The milk fatty acid (FA) profile differed, with LS cows having greater mixed FA content and HS cows showing higher de novo FA content and yield. Although methane production tended to be higher in HS cows (+25 g/day), methane yield decreased by 8.8%. Overall, the HS diet improved milk production, nutrient digestibility, and environmental efficiency by reducing methane yield in dairy cows. Full article

(This article belongs to the Section Farm Animal Production)

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19 pages, 16555 KB

Open AccessEditor’s ChoiceArticle

WED-YOLO: A Detection Model for Safflower Under Complex Unstructured Environment

by Zhenguo Zhang, Yunze Wang, Peng Xu, Ruimeng Shi, Zhenyu Xing and Junye Li

Agriculture 2025, 15(2), 205; https://doi.org/10.3390/agriculture15020205 - 18 Jan 2025

Cited by 10 | Viewed by 1597

Abstract

Accurate safflower recognition is a critical research challenge in the field of automated safflower harvesting. The growing environment of safflowers, including factors such as variable weather conditions in unstructured environments, shooting distances, and diverse morphological characteristics, presents significant difficulties for detection. To address [...] Read more.

Accurate safflower recognition is a critical research challenge in the field of automated safflower harvesting. The growing environment of safflowers, including factors such as variable weather conditions in unstructured environments, shooting distances, and diverse morphological characteristics, presents significant difficulties for detection. To address these challenges and enable precise safflower target recognition in complex environments, this study proposes an improved safflower detection model, WED-YOLO, based on YOLOv8n. Firstly, the original bounding box loss function is replaced with the dynamic non-monotonic focusing mechanism Wise Intersection over Union (WIoU), which enhances the model’s bounding box fitting ability and accelerates network convergence. Then, the upsampling module in the network’s neck is substituted with the more efficient and versatile dynamic upsampling module, DySample, to improve the precision of feature map upsampling. Meanwhile, the EMA attention mechanism is integrated into the C2f module of the backbone network to strengthen the model’s feature extraction capabilities. Finally, a small-target detection layer is incorporated into the detection head, enabling the model to focus on small safflower targets. The model is trained and validated using a custom-built safflower dataset. The experimental results demonstrate that the improved model achieves Precision (P), Recall (R), mean Average Precision (mAP), and F₁ score values of 93.15%, 86.71%, 95.03%, and 89.64%, respectively. These results represent improvements of 2.9%, 6.69%, 4.5%, and 6.22% over the baseline model. Compared with Faster R-CNN, YOLOv5, YOLOv7, and YOLOv10, the WED-YOLO achieved the highest mAP value. It outperforms the module mentioned by 13.06%, 4.85%, 4.86%, and 4.82%, respectively. The enhanced model exhibits superior precision and lower miss detection rates in safflower recognition tasks, providing a robust algorithmic foundation for the intelligent harvesting of safflowers. Full article

(This article belongs to the Special Issue Cutting-Edge Technology in Agricultural Robotics: Sensing and Actuation)

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20 pages, 2661 KB

Open AccessEditor’s ChoiceReview

Progress in Elucidating the Mechanism of Selenium in Mitigating Heavy Metal Stress in Crop Plants

by Shuqing Jia, Qing Guan, Yulong Niu, Ye Wang, Linling Li and Hua Cheng

Agriculture 2025, 15(2), 204; https://doi.org/10.3390/agriculture15020204 - 18 Jan 2025

Cited by 9 | Viewed by 2216

Abstract

In the context of rapid industrialization and agricultural modernization, the issue of heavy metal (HM) pollution has surfaced as a critical concern, posing a substantial threat to human health and having a profound impact on agricultural cultivation. Selenium (Se), a beneficial micronutrient for [...] Read more.

In the context of rapid industrialization and agricultural modernization, the issue of heavy metal (HM) pollution has surfaced as a critical concern, posing a substantial threat to human health and having a profound impact on agricultural cultivation. Selenium (Se), a beneficial micronutrient for crop growth and development, exerts numerous beneficial effects, including facilitating photosynthesis, enhancing physiological attributes, improving nutritional quality, strengthening antioxidant systems, and modulating the expression of stress-responsive genes. Notably, Se plays a pivotal role in alleviating HM stress in crops and effectively mitigating the accumulation of HMs in edible plant parts. This study investigates the physiological and molecular mechanisms underlying Se’s capacity to alleviate HM stress in crops. Additionally, we discuss the application of Se-enriched fertilizers in agricultural practices, as well as the influence of environmental factors on their effectiveness. Our objective is to contribute to sustainable agricultural development and the production of safe, high-quality agricultural products, thereby providing valuable insights for the development of Se-functional industries and guiding agricultural practices in regions affected by HM pollution. Full article

(This article belongs to the Special Issue Biostimulants for Crop Growth and Abiotic Stress Mitigation)

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34 pages, 8155 KB

Open AccessEditor’s ChoiceReview

Raman Spectroscopy and Its Application in Fruit Quality Detection

by Yong Huang, Haoran Wang, Huasheng Huang, Zhiping Tan, Chaojun Hou, Jiajun Zhuang and Yu Tang

Agriculture 2025, 15(2), 195; https://doi.org/10.3390/agriculture15020195 - 17 Jan 2025

Cited by 8 | Viewed by 4179

Abstract

Raman spectroscopy is a spectral analysis technique based on molecular vibration. It has gained widespread acceptance as a practical tool for the non-invasive and rapid characterization or identification of multiple analytes and compounds in recent years. In fruit quality detection, Raman spectroscopy is [...] Read more.

Raman spectroscopy is a spectral analysis technique based on molecular vibration. It has gained widespread acceptance as a practical tool for the non-invasive and rapid characterization or identification of multiple analytes and compounds in recent years. In fruit quality detection, Raman spectroscopy is employed to detect organic compounds, such as pigments, phenols, and sugars, as well as to analyze the molecular structures of specific chemical bonds or functional groups, providing valuable insights into fruit disease detection, pesticide residue analysis, and origin identification. Consequently, Raman spectroscopy techniques have demonstrated significant potential in agri-food analysis across various domains. Notably, the frontier of Raman spectroscopy is experiencing a surge in machine learning applications to enhance the resolution and quality of the resulting spectra. This paper reviews the fundamental principles and recent advancements in Raman spectroscopy and explores data processing techniques that use machine learning in Raman spectroscopy, with a focus on its applications in detecting fruit diseases, analyzing pesticide residues, and identifying origins. Finally, it highlights the challenges and future prospects of Raman spectroscopy, offering an effective reference for fruit quality detection. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

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17 pages, 2041 KB

Open AccessEditor’s ChoiceArticle

LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11

by Ameer Tamoor Khan and Signe Marie Jensen

Agriculture 2025, 15(2), 196; https://doi.org/10.3390/agriculture15020196 - 17 Jan 2025

Cited by 14 | Viewed by 2179

Abstract

Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus [...] Read more.

Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and 0.7 for winter crops. Segmentation tasks, however, reveal mixed results, with individual models occasionally excelling in recall for winter crops. These findings highlight the benefits of dataset diversity in improving generalization, while emphasizing the need for larger annotated datasets to address variability in real-world conditions. While the combined dataset improves generalization, the unique characteristics of individual crops may still benefit from specialized training. Full article

(This article belongs to the Special Issue Single Leaf Area Models and Leaf Area Index in Agriculture and Its Related Ecosystems)

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25 pages, 24423 KB

Open AccessEditor’s ChoiceArticle

A Landscape-Clustering Zoning Strategy to Map Multi-Crops in Fragmented Cropland Regions Using Sentinel-2 and Sentinel-1 Imagery with Feature Selection

by Guanru Fang, Chen Wang, Taifeng Dong, Ziming Wang, Cheng Cai, Jiaqi Chen, Mengyu Liu and Huanxue Zhang

Agriculture 2025, 15(2), 186; https://doi.org/10.3390/agriculture15020186 - 16 Jan 2025

Cited by 6 | Viewed by 1625

Abstract

Crop mapping using remote sensing is a reliable and efficient approach to obtaining timely and accurate crop information. Previous studies predominantly focused on large-scale regions characterized by simple cropping structures. However, in complex agricultural regions, such as China’s Huang-Huai-Hai region, the high crop [...] Read more.

Crop mapping using remote sensing is a reliable and efficient approach to obtaining timely and accurate crop information. Previous studies predominantly focused on large-scale regions characterized by simple cropping structures. However, in complex agricultural regions, such as China’s Huang-Huai-Hai region, the high crop diversity and fragmented cropland in localized areas present significant challenges for accurate crop mapping. To address these challenges, this study introduces a landscape-clustering zoning strategy utilizing multi-temporal Sentinel-1 and Sentinel-2 imagery. First, crop heterogeneity zones (CHZs) are delineated using landscape metrics that capture crop diversity and cropland fragmentation. Subsequently, four types of features (spectral, phenological, textural and radar features) are combined in various configurations to create different classification schemes. These schemes are then optimized for each CHZ using a random forest classifier. The results demonstrate that the landscape-clustering zoning strategy achieves an overall accuracy of 93.52% and a kappa coefficient of 92.67%, outperforming the no-zoning method by 2.9% and 3.82%, respectively. Furthermore, the crop mapping results from this strategy closely align with agricultural statistics at the county level, with an R² value of 0.9006. In comparison with other traditional zoning strategies, such as topographic zoning and administrative unit zoning, the proposed strategy proves to be superior. These findings suggest that the landscape-clustering zoning strategy offers a robust reference method for crop mapping in complex agricultural landscapes. Full article

(This article belongs to the Topic Advances in Smart Agriculture with Remote Sensing as the Core and Its Applications in Crops Field)

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14 pages, 292 KB

Open AccessEditor’s ChoiceArticle

Performance, Carcass Traits and Meat Quality of Lambs Fed with Increasing Levels of High-Oleic Sunflower Cake

by Daviane M. Costa, Tharcilla I. R. C. Alvarenga, Isabela J. dos Santos, Paulo C. G. Dias Junior, Flavio A. P. Alvarenga, Nadja G. Alves and Iraides F. Furusho-Garcia

Agriculture 2025, 15(2), 191; https://doi.org/10.3390/agriculture15020191 - 16 Jan 2025

Cited by 3 | Viewed by 1401

Abstract

The aim of this study was to evaluate the effect of sunflower cake from high-oleic seeds on performance, carcass characteristics, meat quality, and intramuscular fatty acid composition of finishing lambs. Thirty-six crossbred ewe lambs were assigned to four treatments (nine lambs/treatment) in a [...] Read more.

The aim of this study was to evaluate the effect of sunflower cake from high-oleic seeds on performance, carcass characteristics, meat quality, and intramuscular fatty acid composition of finishing lambs. Thirty-six crossbred ewe lambs were assigned to four treatments (nine lambs/treatment) in a completely randomized design: 0 (control), 150, 300 and 450 g/kg DM of high-oleic sunflower cake. The lambs were weighed weekly and slaughtered with 42.3 ± 0.18 kg body weight and 270 ± 10.8 days of old. The inclusion of sunflower cake did not affect weight gain, dry matter intake and metabolizable energy intake (p > 0.05). There was an increase in neutral detergent fiber and EE intake (p < 0.01) with the inclusion of sunflower cake in the diet of the lambs. The inclusion of sunflower cake reduced hot and cold carcass yields (p < 0.01). Intramuscular fat content, L*, oleic acid, rumenic acid and EPA fatty acids linearly increased (p < 0.01) with the inclusion of high-oleic sunflower cake. The inclusion of high-oleic sunflower cake reduced saturated fatty acids (p < 0.01), except stearic acid, which linearly increased (p < 0.01). Up to 450 g/kg DM of high-oleic sunflower cake in the diet of lambs did not affect animal performance while providing a higher deposition of fat with better fatty acid composition for human consumption. Full article

(This article belongs to the Special Issue Rational Use of Feed to Promote Animal Healthy Feeding)

16 pages, 6327 KB

Open AccessEditor’s ChoiceArticle

Bacillus velezensis TCS001 Enhances the Resistance of Hickory to Phytophthora cinnamomi and Reshapes the Rhizosphere Microbial Community

by Chenshun Xie, Yuntian Wu, Zhonghao Wu, Hao Cao, Xiaohui Huang, Feng Cui, Shuai Meng and Jie Chen

Agriculture 2025, 15(2), 193; https://doi.org/10.3390/agriculture15020193 - 16 Jan 2025

Cited by 5 | Viewed by 1554

Abstract

Phytophthora cinnamomi causes significant root rot in hickory, leading to substantial yield losses. While Bacillus spp. are recognized as beneficial rhizosphere microorganisms, their application against hickory root rot and their impact on rhizosphere microbial communities remain under-investigated. This study demonstrated that Bacillus velezensis [...] Read more.

Phytophthora cinnamomi causes significant root rot in hickory, leading to substantial yield losses. While Bacillus spp. are recognized as beneficial rhizosphere microorganisms, their application against hickory root rot and their impact on rhizosphere microbial communities remain under-investigated. This study demonstrated that Bacillus velezensis TCS001 significantly inhibited P. cinnamomi ST402 growth in vitro, and achieved 71% efficacy in root rot disease management. Scanning electron microscopy (SEM) revealed that TCS001 fermentation filtrate induced mycelial deformities in P. cinnamomi. An analysis of α and β diversity indicated a significant impact of TCS001 on rhizosphere bacterial community richness and diversity, with minimal effects on the fungal community. Moreover, TCS001 altered the hickory rhizosphere microbiome co-occurrence network. The differential abundance analysis suggests that TCS001 promotes the recruitment of beneficial microbes associated with disease resistance, thereby suppressing disease development. These findings underscore the influence of TCS001 on the hickory rhizosphere microbiome in the presence of pathogens, providing valuable data for future research and the development of effective biocontrol strategies for hickory root rot. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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18 pages, 5355 KB

Open AccessEditor’s ChoiceArticle

Modified SWAT Model for Agricultural Watershed in Karst Area of Southwest China

by Junfeng Dai, Linyan Pan, Yan Deng, Zupeng Wan and Rui Xia

Agriculture 2025, 15(2), 192; https://doi.org/10.3390/agriculture15020192 - 16 Jan 2025

Cited by 3 | Viewed by 1778

Abstract

The Soil and Water Assessment Tool (SWAT) model is extensively used globally for hydrological and water quality assessments but encounters challenges in karst regions due to their complex surface and groundwater hydrological environments. This study aims to refine the delineation of hydrological response [...] Read more.

The Soil and Water Assessment Tool (SWAT) model is extensively used globally for hydrological and water quality assessments but encounters challenges in karst regions due to their complex surface and groundwater hydrological environments. This study aims to refine the delineation of hydrological response units within the SWAT model by combining geomorphological classification and to enhance the model with an epikarst zone hydrological process module, exploring the accuracy improvement of SWAT model simulations in karst regions of Southwest China. Compared with the simulation results of the original SWAT model, we simulated runoff and nutrient concentrations in the Mudong watershed from January 2017 to December 2021 using the improved SWAT model. The simulation results indicated that the modified SWAT model responded more rapidly to precipitation events, particularly in bare karst landform, aligning more closely with the actual hydrological processes in Southwest China’s karst regions. In terms of the predictive accuracy for monthly loads of total nitrogen (TN) and total phosphorus (TP), the coefficient of determination (R²) value of the modified model increased by 10.3% and 9.7%, respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) increased by 11.3% and 9.9%, respectively. The modified SWAT model improves prediction accuracy in karst areas and holds significant practical value for guiding non-point source pollution control in agricultural watersheds. Full article

(This article belongs to the Section Agricultural Soils)

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