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YOLO11-ARAF: An Accurate and Lightweight Method for Apple Detection in Real-World Complex Orchard Environments
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Preparation and Characterization of Liquid Fertilizers Produced by Anaerobic Fermentation
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Employment of Biodegradable, Short-Life Mulching Film on High-Density Cropping Lettuce in a Mediterranean Environment: Potentials and Prospects
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Emerging Trends in AI-Based Soil Contamination Monitoring and Prevention
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The Influence of Weather Conditions and Available Soil Water on Vitis vinifera L. Albillo Mayor in Ribera del Duero DO (Spain) and Potential Changes Under Climate Change: A Preliminary Analysis
Journal Description
Agriculture
Agriculture
is an international, scientific peer-reviewed open access journal published semimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, RePEc, and other databases.
- Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q1 (Plant Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18 days after submission; acceptance to publication is undertaken in 1.9 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Agriculture include: Poultry, Grasses and Crops.
Impact Factor:
3.6 (2024);
5-Year Impact Factor:
3.8 (2024)
Latest Articles
Design and Experiment of Drying Equipment for Alfalfa Bales
Agriculture 2025, 15(19), 2000; https://doi.org/10.3390/agriculture15192000 (registering DOI) - 24 Sep 2025
Abstract
Inefficient drying of alfalfa round bales causes significant nutrient loss (up to 50%) and quality degradation due primarily to uneven drying in existing processing methods. To address this challenge requiring dedicated equipment and optimized processes, this study developed a specialized hot-air drying test
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Inefficient drying of alfalfa round bales causes significant nutrient loss (up to 50%) and quality degradation due primarily to uneven drying in existing processing methods. To address this challenge requiring dedicated equipment and optimized processes, this study developed a specialized hot-air drying test bench (CGT-1). A coupled heat and mass transfer model was established, and 3D dynamic simulations of temperature, humidity, and wind speed distributions within bales were performed using COMSOL multiphysics to evaluate drying inhomogeneity. Single-factor experiments and multi-factor response surface methodology (RSM) based on Box–Behnken design were employed to investigate the effects of hot air temperature (50–65 °C), wind speed (2–5 m/s), and air duct opening diameter (400–600 mm) on moisture content, drying rate, and energy consumption. Results demonstrated that larger duct diameters (600 mm) and higher wind speeds (5 m/s) significantly enhanced field uniformity. RSM optimization identified optimal parameters: temperature at 65 °C, wind speed of 5 m/s, and duct diameter of 600 mm, achieving a drying time of 119.2 min and a drying rate of 0.62 kg/(kg·min). Validation experiments confirmed the model’s accuracy. These findings provide a solid theoretical foundation and technical support for designing and optimizing alfalfa round-bale drying equipment. Future work should explore segmented drying strategies to enhance energy efficiency.
Full article
(This article belongs to the Section Agricultural Technology)
Open AccessReview
Basil Downy Mildew (Peronospora belbahrii): A Major Threat to Ocimum basilicum L. Production
by
Massimo Pugliese, Giovanna Gilardi, Angelo Garibaldi and Maria Lodovica Gullino
Agriculture 2025, 15(19), 1999; https://doi.org/10.3390/agriculture15191999 - 24 Sep 2025
Abstract
Basil (Ocimum basilicum L.), a key herb in Mediterranean cuisine, holds substantial economic and cultural value due to its aromatic and medicinal properties. Cultivated globally, particularly in Italy’s Liguria region, basil is consumed both fresh and processed, with pesto sauce as its
[...] Read more.
Basil (Ocimum basilicum L.), a key herb in Mediterranean cuisine, holds substantial economic and cultural value due to its aromatic and medicinal properties. Cultivated globally, particularly in Italy’s Liguria region, basil is consumed both fresh and processed, with pesto sauce as its most notable derivative. Despite its commercial success, basil production is significantly constrained by a broad spectrum of fungal pathogens, with Peronospora belbahrii, the causal agent of downy mildew, posing the most severe threat. This study aims to provide a comprehensive overview of basil’s disease susceptibility and control. Special emphasis is placed on the biology, epidemiology, global spread, and diagnosis of P. belbahrii, which has become a critical challenge for both conventional and organic farming systems. Disease management strategies, including cultural practices, genetic resistance, fungicide applications, resistance inducers, and biocontrol agents, are reviewed in detail. The development of downy mildew-resistant cultivars—although limited for PDO-designated Genovese basil—has emerged as the most sustainable control measure; however, the increasing genetic variability in P. belbahrii underscores the ongoing need for integrated pest management and resistant cultivar development. Seed health and quality remain the starting points of any fully integrated approach, although the suggested management measures for basil production should be combined with appropriate cultivation techniques aimed at reducing the relative humidity of the environment, while taking into account whether basil production takes place in open fields or under protection.
Full article
(This article belongs to the Special Issue Agronomic Practices for Enhancing Quality and Yield of Aromatic and Medicinal Crops)
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Open AccessArticle
Diurnal Behaviour, Health and Hygiene of Dairy Cows in Compost Barn Systems Under Different Climates in Argentina: A Bayesian Approach
by
Gabriela Marcela Martinez, Pablo Viretto, Georgina Frossasco, Víctor Humberto Suarez, Ayoola Olawole Jongbo, Edgar de Souza Vismara and Frederico Márcio Corrêa Vieira
Agriculture 2025, 15(19), 1998; https://doi.org/10.3390/agriculture15191998 - 23 Sep 2025
Abstract
Compost barn systems are relevant alternatives to discussing production efficiency, welfare, and sustainability in dairy farming. However, studies evaluating these systems in different climates are still scarce, especially in subtropical climate zones. Here, we assess whether dairy cows’ behaviour, health and hygiene in
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Compost barn systems are relevant alternatives to discussing production efficiency, welfare, and sustainability in dairy farming. However, studies evaluating these systems in different climates are still scarce, especially in subtropical climate zones. Here, we assess whether dairy cows’ behaviour, health and hygiene in compost barn systems are influenced by different climatic conditions and calving orders in Argentina’s central and extra-Pampean basins from the perspective of Bayesian inference. We evaluated dairy cows (n = 40) in a compost barn system simultaneously at two locations in Argentina: Rafaela and Salta. The following variables were evaluated: environmental factors, animal behaviour, respiratory rate, udder and hock hygiene, and locomotion degree of milking cows. There was a total of 10 primiparous cows and 10 multiparous cows at each location, randomly selected, which were in the first third of lactation (<90 DIM). Using Bayesian inference, we observed that Rafaela had a temperature-humidity index (THI) above 70, and Salta had a milder environment, with lower average temperature and higher relative humidity. Thus, climatic interference is evident in behaviour, triggering more behavioural and physiological mechanisms for heat abatement in primiparous females in Rafaela. At the same time, the mild conditions in Salta led to better thermal energy transfer by multiparous females compared to primiparous cows. This shows that the microclimate could interfere with the social hierarchy of cows when they are under heat stress. These findings highlight the importance of considering both calving orders and climate when designing management strategies for dairy systems.
Full article
(This article belongs to the Special Issue Livestock Building Environment Improvements and Their Influence on the Animal’s Welfare)
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Open AccessArticle
Consumer Perceptions of Greenwashing in Local Agri-Food Systems and Rural Tourism
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Gunta Grinberga-Zalite, Ksenija Furmanova, Sandija Zeverte-Rivza, Liga Paula and Inita Kindzule
Agriculture 2025, 15(19), 1997; https://doi.org/10.3390/agriculture15191997 - 23 Sep 2025
Abstract
The current article examines how Latvian consumers perceive the sustainability of rural tourism services and locally produced food, with particular attention paid to their views on misleading environmental claims. For small-scale agricultural producers and rural tourism providers, sustainability communication has become common, yet
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The current article examines how Latvian consumers perceive the sustainability of rural tourism services and locally produced food, with particular attention paid to their views on misleading environmental claims. For small-scale agricultural producers and rural tourism providers, sustainability communication has become common, yet formal regulation and consumer clarity issues often remain uncertain. The study is based on a mixed-methods approach that contains a comprehensive, multi-dimensional literature analysis and quantitative nation-wide survey data analysis (SPSS 27) with a thematic interpretation of consumer attitudes towards sustainability, trust, and greenwashing. The research findings show that while consumers generally support sustainable and ethically produced goods and services, their trust depends heavily on the transparency and credibility of the information provided. Official certifications and clear communication were seen as trustworthy, while vague promotional claims, especially in digital media, were often met with scepticism. The study also reveals how different factors such as education level, income, and place of residence influence the ability to recognize potential greenwashing. Given the growing global concern about false environmental claims, this article provides valuable insights not only for Latvia but also for other countries facing similar challenges in promoting sustainable rural development while preserving consumer trust in the green economy.
Full article
(This article belongs to the Special Issue Strategies for Resilient and Sustainable Agri-Food Systems—2nd Edition)
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Open AccessArticle
DOSA-YOLO: Improved Model Research for the Detection of Common Chicken Diseases Using Phenotypic Features
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Xiaofeng Guo, Yun Wang, Jianhui Li, Qin Li, Zhenhuan Zuo and Zhenyu Liu
Agriculture 2025, 15(19), 1996; https://doi.org/10.3390/agriculture15191996 - 23 Sep 2025
Abstract
Chicken farming plays a crucial role in the global food supply; however, the frequent occurrence of chicken diseases presents a substantial challenge to the industry’s sustainable development. This study introduces an enhanced YOLOv11 model, DOSA-YOLO, designed to detect four prevalent chicken diseases: avian
[...] Read more.
Chicken farming plays a crucial role in the global food supply; however, the frequent occurrence of chicken diseases presents a substantial challenge to the industry’s sustainable development. This study introduces an enhanced YOLOv11 model, DOSA-YOLO, designed to detect four prevalent chicken diseases: avian pox, coccidiosis, Mycoplasma gallisepticum, and Newcastle disease. The research team developed an intelligent inspection robot to capture multi-angle images in intensive farming environments, constructing a five-class dataset comprising 8052 images. These images were categorized based on phenotypic features such as comb, eyes, and wattles, as well as pathological anatomical characteristics. To address challenges such as complex backgrounds, multi-scale lesions, and occlusion interference, three attention-enhancement modules—MSDA, MDJA, and SEAM—were integrated into the YOLOv11. The model was trained and validated using the constructed dataset and compared against seven other algorithms, including YOLOv5n, YOLOv7tiny, YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, YOLOv12n, and Faster R-CNN. Experimental results demonstrated that DOSA-YOLO achieved a mean Average Precision (mAP) of 97.2% and an F1-score of 95.0%, outperforming the seven other algorithms while maintaining a balance between lightweight design and performance with GFLOPs of 6.9 and 2.87 M parameters. The model provides strong support for real-time chicken health monitoring in intensive farming environments.
Full article
(This article belongs to the Section Farm Animal Production)
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Open AccessArticle
Distribution Characteristics of Rotor Airflow and Droplet Deposition of Plant Protection UAVs Under Varying Rotor–Nozzle Distances
by
Xiaojie Xu, Shengde Chen, Zhihong Li, Zehong Wu, Yuxiang Tan, Shimin Huang and Yubin Lan
Agriculture 2025, 15(19), 1995; https://doi.org/10.3390/agriculture15191995 - 23 Sep 2025
Abstract
The rotor airflow intensity and distribution characteristics of plant protection UAVs vary significantly with spatial positions below the rotor. Consequently, changes in the rotor–nozzle distance directly affect droplet motion and deposition patterns. To optimize the spraying effect of UAVs, this study combined a
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The rotor airflow intensity and distribution characteristics of plant protection UAVs vary significantly with spatial positions below the rotor. Consequently, changes in the rotor–nozzle distance directly affect droplet motion and deposition patterns. To optimize the spraying effect of UAVs, this study combined a numerical simulation of rotor airflow and droplet deposition at different vertical distances between rotor and nozzle with field validation tests. The simulation results revealed that airflow intensity initially increases and then decreases with greater rotor–nozzle distance, peaking at 300–400 mm below the rotor with a maximum airflow velocity of 8.1 m/s. At 360 mm, the droplet swarm achieved its highest average velocity, corresponding to optimal deposition effect. Field tests confirmed a non-linear relationship between rotor–nozzle distance and droplet deposition. Droplet deposition first increased but declined sharply beyond the optimal range. When the distance was 360 mm, the target area exhibited the highest droplet deposition of 0.766 μL·cm−2 and the lowest drift rate of 23.31%. Although a certain deviation existed between numerical simulation results and field test values, both methods consistently identified 360 mm as the ideal distance for balancing deposition efficiency and drift control. These findings provide actionable insights for field trial design and advance precision spraying strategies for plant protection UAVs.
Full article
(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)
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Open AccessArticle
Molecular Identification, Pathogenicity, and Fungicide Sensitivity of Sclerotinia spp. Isolates Associated with Sclerotinia Stem Rot in Rapeseed in Germany
by
Nazanin Zamani-Noor, Dorsa Daneshbakhsh and Beatrice Berger
Agriculture 2025, 15(19), 1994; https://doi.org/10.3390/agriculture15191994 - 23 Sep 2025
Abstract
(1) Background: Sclerotinia sclerotiorum is the main causal agent of Sclerotinia stem rot in rapeseed, while the related species S. subarctica has also been reported. However, its prevalence and impact in Germany remain unclear. Understanding the pathogenicity and fungicide sensitivity of Sclerotinia spp.
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(1) Background: Sclerotinia sclerotiorum is the main causal agent of Sclerotinia stem rot in rapeseed, while the related species S. subarctica has also been reported. However, its prevalence and impact in Germany remain unclear. Understanding the pathogenicity and fungicide sensitivity of Sclerotinia spp. is important for effective and sustainable disease management. (2) Methods: Isolates were collected from symptomatic rapeseed plants across Germany. Molecular identification was performed via ITS rRNA sequencing. Pathogenicity was assessed by stem inoculation of five rapeseed cultivars at the flowering stage. Fungicide sensitivity was tested in vitro against seven active substances, including azoles, boscalid, azoxystrobin, and fludioxonil. (3) Results: All isolates were identified as S. sclerotiorum; S. subarctica was not detected. Of the tested isolates, 23 showed low aggressiveness (relative lesion length < 15% of total plant length), 29 were moderately aggressive (15–20%), and 10 were highly aggressive (>20%). Azole fungicides were highly effective (EC50 < 1.6 μg a.s. mL−1), while reduced sensitivity was observed for boscalid, azoxystrobin, and fludioxonil (EC50 > 4.0). (4) Conclusions: This study provides insight into the molecular identity, pathogenicity, and fungicide sensitivity of Sclerotinia isolates. The observed variability in aggressiveness and mycelial growth to fungicide emphasize the need for integrated management strategies to ensure Sclerotinia stem rot control.
Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
Open AccessArticle
Tritrophic Interactions Among Fruit Flies (Diptera: Tephritidae), Its Parasitoids and Cultivated and Wild Hosts in the Pampa Biome, Rio Grande do Sul, Brazil
by
Emily S. Araujo, Alexandra P. Krüger, Maria V. Calvo, Marcos H. F. Telles, Alexandre M. Neumann, Iris B. Scatoni, Valmir A. Costa, Dori E. Nava, José M. Mirás-Avalos and Flávio R. M. Garcia
Agriculture 2025, 15(19), 1993; https://doi.org/10.3390/agriculture15191993 - 23 Sep 2025
Abstract
Fruit fly (Diptera: Tephritidae) species are a serious threat for fruit-growers worldwide. The parasitoids (Hymenoptera) are natural enemies of these flies. In this context, the aim of this work was to assess fruit infestation by tephritid flies, both in native and exotic fruit
[...] Read more.
Fruit fly (Diptera: Tephritidae) species are a serious threat for fruit-growers worldwide. The parasitoids (Hymenoptera) are natural enemies of these flies. In this context, the aim of this work was to assess fruit infestation by tephritid flies, both in native and exotic fruit trees, in the Southern region of Rio Grande do Sul (Brazil). Moreover, the incidence of native parasitoids on fly larvae was estimated. Fruits with signals of attack by fruit flies were collected randomly both in the trees and on the ground. From 2013 to 2015, a total of 5729 fruits (194.48 kg) were collected, corresponding to 34 tree species from 16 botanical families. Fruits were taken to the laboratory, individualized, weighted and kept in vermiculite for pupae emergence. Pupae were counted and emerged adults were counted and identified. The association between fruit flies, hosts and parasitoids was determined when only a given species of tephritid emerged. Half of the sampled fruit tree species presented infestation by flies. The main species of tephritid fly was Anastrepha fraterculus. This study showed that natural parasitism rates of fruit flies were low; however, several parasitoid species from the Figitidae and Braconidae families were detected, including Aganaspis pelleranoi, Doryctobracon areolatus, Doryctobracon brasiliensis, Opius bellus, Utetes anastrephae, and Cerchysiella insularis.
Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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Open AccessArticle
Molecular and Phytopathological Characterization of Fusarium Wilt-Resistant Chickpea Genotypes for Breeding Applications
by
Raushan Yerzhebayeva, Alfiya Abekova, Kuralay Baitarakova, Mukhtar Kudaibergenov, Aydarkhan Yesserkenov, Bekzhan Maikotov and Svetlana Didorenko
Agriculture 2025, 15(19), 1992; https://doi.org/10.3390/agriculture15191992 - 23 Sep 2025
Abstract
Fusarium wilt, caused by Fusarium oxysporum f. sp. ciceris (Foc), is a devastating disease of chickpea (Cicer arietinum L.), leading to vascular necrosis and plant death. This study evaluated 120 chickpea genotypes under natural infection field conditions during spring sowing
[...] Read more.
Fusarium wilt, caused by Fusarium oxysporum f. sp. ciceris (Foc), is a devastating disease of chickpea (Cicer arietinum L.), leading to vascular necrosis and plant death. This study evaluated 120 chickpea genotypes under natural infection field conditions during spring sowing in southeastern Kazakhstan, assessing disease incidence (DI) and severity (DS) to identify resistant germplasm. Molecular screening using eight SSR markers linked to Foc-1, Foc-2, Foc-3, and Foc-5 loci detected resistant alleles in 18, 26, 19, and 42 genotypes, respectively. The correlation between molecular marker data and phenotypic resistance evaluations confirmed UBC-170 (Foc-2) and TA-194 (Foc-5) as the most predictive diagnostic markers (p < 0.01). Ten genotypes showed complete disease resistance (DI < 5%, R), corresponding to the resistant control (cultivar “WR-315”), with confirmed presence of multiple Foc resistance genes. The results of this study revealed valuable genetic resources for marker-assisted breeding programs aimed at developing Fusarium wilt-resistant chickpea cultivars adapted to Central Asian agroclimatic conditions.
Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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Open AccessArticle
Design and Experiment of Air–Fertilizer Separator for Pneumatic Deep Fertilization in Paddy Fields
by
Mingjin Xin, Wenrui Ding, Duo Chen, Man Zhang, Yujue Ao, Bowen Chi, Zhiwen Jiang, Yuqiu Song and Yunlong Guo
Agriculture 2025, 15(18), 1991; https://doi.org/10.3390/agriculture15181991 - 22 Sep 2025
Abstract
Supplemental fertilizer application is critical for improving rice yield. Pneumatic deep fertilization effectively improves fertilizer utilization, but high-speed airflow may disturb the soil and affect the location of the fertilizer particles. An air–fertilizer separator was developed in this study to separate the fertilizer
[...] Read more.
Supplemental fertilizer application is critical for improving rice yield. Pneumatic deep fertilization effectively improves fertilizer utilization, but high-speed airflow may disturb the soil and affect the location of the fertilizer particles. An air–fertilizer separator was developed in this study to separate the fertilizer from the airflow before the two-phase flow rushes into the soil, and the airflow is directed away from the surface of the paddy soil. The structural and operating parameters of the air–fertilizer separator are determined in this paper. A quadratic orthogonal rotation combination experiment was conducted, taking structural parameters of the device as variables, and fertilizer injection speed, separation loss rate, and outlet airflow speed as performance indicators, to optimize the design parameters of the air–fertilizer separator. The variance analysis and surface response analysis of the experimental data are conducted, and the mathematical models between the indicators and the influencing factors are established. The optimal parameters were determined using multi-objective optimization, and the experimental verification was carried out. The optimal parameters for the air–fertilizer separator were obtained as an arc radius of the AFAST of 380 mm, central angle of arc trough of 45°, and depth of primary separation arc-trough of 12.5 mm. The validation experimental results show that the fertilizer injection speed is 21.45 m/s, the fertilizer separation loss rate is 10.22%, and the outlet airflow speed is 42.54 m/s. The experimental values are close to the predicted values, with errors of 1.2%, 1.7%, and 1.3%. The results of the study may provide a reference for the development of an air–fertilizer separator for pneumatic deep fertilization in paddy fields.
Full article
(This article belongs to the Section Agricultural Technology)
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Developing Native Fish to Control Spirogyra in Paddy Fields for Improving the Growth, Nutrient Uptake, and Physiological Characteristics of Oryza sativa L.
by
Mei Zhang, Runhai Jiang, Xiaorong Yang, Shaofu Wen, Zexiang Hua, Xiuli Hou and Xuexiu Chang
Agriculture 2025, 15(18), 1990; https://doi.org/10.3390/agriculture15181990 - 22 Sep 2025
Abstract
Oryza sativa L. is the largest food crop in the world. The harmful filamentous green algae Spirogyra in paddy fields poses a serious threat to O. sativa yield. Therefore, biological control for Spirogyra is important for sustainable agricultural development. The native fish species
[...] Read more.
Oryza sativa L. is the largest food crop in the world. The harmful filamentous green algae Spirogyra in paddy fields poses a serious threat to O. sativa yield. Therefore, biological control for Spirogyra is important for sustainable agricultural development. The native fish species Acrossocheilus yunnanensis can graze on Spirogyra and exhibits strong environmental adaptability, providing a novel approach to the biological control of Spirogyra. Therefore, we designed the O. sativa+Spirogyra+A. yunnanensis co-culture system to study the effects of A. yunnanensis on O. sativa growth and physiological characteristics. The results indicated that Spirogyra stress significantly inhibited O. sativa biomass accumulation, root length and plant height development, reduced photosynthetic efficiency, and increased the contents of oxidative stress markers including malondialdehyde (MDA) and hydrogen peroxide (H2O2). Interestingly, grazing of A. yunnanensis on Spirogyra increased the biomass of Oryza sativa by 58.60%, the root–shoot ratio by 78.01%, and the root length and plant height by 49.83% and 25.85%, respectively. Meanwhile, the soil nitrate nitrogen (NO3−-N), ammonium nitrogen (NH4+-N), and available phosphorus (AP) were enhanced, which improved O. sativa nutrient uptake and promoted photosynthetic pigment accumulation. This was manifested by an increase in chlorophyll content, net photosynthetic (Pn), transpiration rate, stomatal conductance (Gs), and intercellular CO2 concentration (Ci). Grazing of A. yunnanensis on Spirogyra alleviated the oxidative damage to O. sativa induced by Spirogyra, as evidenced by decreased malondialdehyde (MDA) and hydrogen peroxide (H2O2) level in both leaves and roots, along with increased protein content. This provides a new strategy for constructing a rice–fish symbiotic system by using indigenous fish species, achieving Spirogyra control and sustainable agricultural development.
Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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Open AccessArticle
Super-Resolution Point Cloud Completion for Large-Scale Missing Data in Cotton Leaves
by
Hui Geng, Zhiben Yin, Mingdeng Shi, Junzhang Pan and Chunjing Si
Agriculture 2025, 15(18), 1989; https://doi.org/10.3390/agriculture15181989 - 22 Sep 2025
Abstract
Point cloud completion for cotton leaves is critical for accurately reconstructing complete shapes from sparse and significantly incomplete data. Traditional methods typically assume small missing ratios (≤25%), which limits their effectiveness for morphologically complex cotton leaves with severe sparsity (50–75%), large geometric distortions,
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Point cloud completion for cotton leaves is critical for accurately reconstructing complete shapes from sparse and significantly incomplete data. Traditional methods typically assume small missing ratios (≤25%), which limits their effectiveness for morphologically complex cotton leaves with severe sparsity (50–75%), large geometric distortions, and extensive point loss. To overcome these challenges, we introduce an end-to-end neural network that combines PF-Net and PointNet++ to effectively reconstruct dense, uniform point clouds from incomplete inputs. The model initially uses a multiresolution encoder to extract multiscale features from locally incomplete point clouds at different resolutions. By capturing both low-level and high-level attributes, these features significantly enhance the network’s ability to represent semantic content and geometric structure. Next, a point pyramid decoder generates missing point clouds hierarchically from layers at different depths, effectively reconstructing the fine details of the original structure. PointNet++ is then used to fuse and reshape the incomplete input point clouds with the generated missing points, yielding a fully reconstructed and uniformly distributed point cloud. To ensure effective task completion at different training stages, a loss function freezing strategy is employed, optimizing the network’s performance throughout the training process. Experimental evaluation on the cotton leaf dataset demonstrated that the proposed model outperformed PF-Net, reducing the Chamfer distance by 80.15% and the Earth Mover distance by 54.35%. These improvements underscore the model’s robustness in reconstructing sparse point clouds for precise agricultural phenotyping.
Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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Open AccessArticle
Performance of Georgian Grapevine Varieties in a Vineyard Infected by Flavescence Dorée Phytoplasma in Piedmont, Northwestern Italy
by
Letizia Portaccio, Maria Alessandra Paissoni, Simone Giacosa, Alessandro Passera, Camilla Barbieri, David Maghradze, Luca Rolle, Vincenzo Gerbi, Osvaldo Failla, Piero Attilio Bianco and Fabio Quaglino
Agriculture 2025, 15(18), 1988; https://doi.org/10.3390/agriculture15181988 - 21 Sep 2025
Abstract
In Europe, Flavescence dorée (FD), the only epidemic disease within the phytoplasma-associated grapevine yellows complex (GY), reduces productivity and has a negative impact on berry composition and wine quality. Recent studies have shown that Georgian Vitis vinifera L. varieties have low susceptibility to
[...] Read more.
In Europe, Flavescence dorée (FD), the only epidemic disease within the phytoplasma-associated grapevine yellows complex (GY), reduces productivity and has a negative impact on berry composition and wine quality. Recent studies have shown that Georgian Vitis vinifera L. varieties have low susceptibility to Bois noir (BN), another GY disease. This study investigated the performance of some Georgian grapevine varieties in a highly FD-affected area in Piedmont (northwestern Italy), exploring their susceptibility to FD and testing their oenological potential through berry and wine quality analyses. Activities, conducted in a case-study vineyard containing central–western European, Georgian, and PIWI (fungus-resistant grape varieties) varieties, included field surveys and molecular analyses. Mortality and infection percentage index were significantly higher in Georgian and central–western European varieties, respectively. All Georgian varieties exhibited none or mild symptoms without a reduction in the number of symptomless berries. Only the FD phytoplasma (FDp) genotype M54 was identified in infected grapevines, suggesting that differences in symptom severity were related to a variety-specific response to infection. Despite infection, Georgian varieties maintained stable berry and wine quality parameters, showing no significant changes in acidity, sugar content, and flavor profile. Thus, Georgian varieties had great oenological potential and responded well to both FDp infection and local agroecosystem conditions.
Full article
(This article belongs to the Special Issue Strategies to Improve the Security and Nutritional Quality of Crop Species—2nd Edition)
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Open AccessArticle
Simulation and Experimental Study on Vibration Separation of Residual Film and Soil Based on EDEM
by
Xinzhong Wang, Yapeng Li and Jing Bai
Agriculture 2025, 15(18), 1987; https://doi.org/10.3390/agriculture15181987 - 21 Sep 2025
Abstract
Due to the complexity of impurity removal from the residual film, there is currently no better impurity removal equipment. To improve the screening performance of the residual film mixture, the vibrating screen was designed. In this paper, the key factors A, B
[...] Read more.
Due to the complexity of impurity removal from the residual film, there is currently no better impurity removal equipment. To improve the screening performance of the residual film mixture, the vibrating screen was designed. In this paper, the key factors A, B, C, and D were identified through mechanical analysis of the mixture (where they represented the screen aperture diameter, vibration amplitude, vibration frequency, and screen mesh inclination angle, respectively). The soil screen rate (Y1) and screening loss rate (Y2) were evaluated. And the optimal ranges for these factors were determined by single-factor experiments. Based on the EDEM, the discrete element model was established to simulate the interaction between residual film and soil. And the motion characteristics of the residual film mixture were analyzed within the screen body through a combination of simulation and bench tests. The vibrating screen’s structural parameters were optimized using Box-Behnken experiments. The most suitable combination of settings was as shown below: A = 6.5 mm, B = 25 mm, C = 3.8 Hz, and D = 4°. Following the optimization of these parameters, the screening performance was optimized. Results of bench tests showed that the soil screening rate was 80.33% and the screening loss rate was 19.31%. This study was expected to offer theoretical and simulation-based methods for optimizing the parameters of residual film-soil vibrating screening devices.
Full article
(This article belongs to the Section Agricultural Technology)
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Open AccessArticle
Genetic Diversity and Population Structure of Wheat Germplasm for Grain Nutritional Quality Using Haplotypes and KASP Markers
by
Qunxiang Yan, Zhankui Zeng, Chunping Wang, Jiachuang Li, Junqiao Song, Qiong Li, Yue Zhao, Chang Liu and Xueyan Jing
Agriculture 2025, 15(18), 1986; https://doi.org/10.3390/agriculture15181986 - 21 Sep 2025
Abstract
Wheat germplasm resources are an important material foundation for genetic improvement. In this study, 170 wheat germplasm resources were used from China, the International Maize and Wheat Improvement Center (CIMMYT), Europe (France, Finland, and Sweden), the United States, Canada, and Australia. Seven nutritional
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Wheat germplasm resources are an important material foundation for genetic improvement. In this study, 170 wheat germplasm resources were used from China, the International Maize and Wheat Improvement Center (CIMMYT), Europe (France, Finland, and Sweden), the United States, Canada, and Australia. Seven nutritional quality traits were evaluated for the 2019–2020 and 2020–2021 cropping seasons. The coefficient of variability for seven nutritional quality traits ranged from 6.99% to 30.65%. The average of genetic diversity (Shannon–Wiener diversity index, H′) was 1.87. The results showed that the average frequency of high-throughput competitive allele-specific PCR (KASP) markers was 69.4% on 17 KASP markers related to seven nutritional quality traits, the average of polymorphic information content (PIC) was 0.308, and the genetic effects were from 0.01% to 18.46%. One hundred and seventy wheat germplasm resources were classified into five groups at ΔK = 5 by genetic structure analysis. The first group comprised 62 germplasm resources (36.47%), the second group included 41 germplasm resources (24.11%), the third group contained 20 germplasm resources (11.76%), the fourth group contained 20 germplasm resources (11.76%), and the fifth group had 29 germplasm resources (17.06%). Germplasm resources from CIMMYT and China were found in the first group and the second group, accounting for 56.45% and 65.85%, respectively, while European germplasm resources constituted 50% of those within the fourth group. Five favorable haplotypes were identified, which were located on chromosomes 4A, 6A, 6B, and 7A: G4A1, G4A2, G6A, G6B, and G7A. Their genetic effects were 8.71%, 8.41%, 1.00%, 18.20%, and 1.16%, respectively. In the meantime, we found 12 significant SNPs of seven nutritional quality traits using haplotype analysis. The frequency of favorable haplotypes in the population ranged from 3.53% to 62.35%. Five haplotypes, G4A1, G4A2, G6A, G6B, and G7A, were beneficial, and their genetic effects were positive. Furthermore, the results offered favorable haplotypes and germplasm resources for enhancing nutritional quality.
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(This article belongs to the Section Crop Genetics, Genomics and Breeding)
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Open AccessArticle
A Novel Framework for Predicting Daily Reference Evapotranspiration Using Interpretable Machine Learning Techniques
by
Elsayed Ahmed Elsadek, Mosaad Ali Hussein Ali, Clinton Williams, Kelly R. Thorp and Diaa Eldin M. Elshikha
Agriculture 2025, 15(18), 1985; https://doi.org/10.3390/agriculture15181985 - 20 Sep 2025
Abstract
Accurate estimation of daily reference evapotranspiration (ETo) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for
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Accurate estimation of daily reference evapotranspiration (ETo) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for commercial growers. This study developed 35 ETo models to predict daily ETo across Coolidge, Maricopa, and Queen Creek in Pinal County, Arizona. Seven input combinations of daily meteorological variables were used for training and testing five machine learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Four statistical indicators, coefficient of determination (R2), the normalized root-mean-squared error (RMSEn), mean absolute error (MAE), and simulation error (Se), were used to evaluate the ML models’ performance in comparison with the FAO-56 PM standardized method. The SHapley Additive exPlanations (SHAP) method was used to interpret each meteorological variable’s contribution to the model predictions. Overall, the 35 ETo-developed models showed an excellent to fair performance in predicting daily ETo over the three weather stations. Employing ANN10, RF10, XGBoost10, CatBoost10, and SVM10, incorporating all ten meteorological variables, yielded the highest accuracies during training and testing periods (0.994 ≤ R2 ≤ 1.0, 0.729 ≤ RMSEn ≤ 3.662, 0.030 ≤ MAE ≤ 0.181 mm·day−1, and 0.833 ≤ Se ≤ 2.295). Excluding meteorological variables caused a gradual decline in ET-developed models’ performance across the stations. However, 3-variable models using only maximum, minimum, and average temperatures (Tmax, Tmin, and Tave) predicted ETo well across the three stations during testing (17.655 ≤ RMSEn ≤ 13.469 and Se ≤ 15.45%). Results highlighted that Tmax, solar radiation (Rs), and wind speed at 2 m height (U2) are the most influential factors affecting ETo at the central Arizona sites, followed by extraterrestrial solar radiation (Ra) and Tave. In contrast, humidity-related variables (RHmin, RHmax, and RHave), along with Tmin and precipitation (Pr), had minimal impact on the model’s predictions. The results are informative for assisting growers and policymakers in developing effective water management strategies, especially for arid regions like central Arizona.
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(This article belongs to the Section Agricultural Water Management)
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Open AccessArticle
Harvest Date Monitoring in Cereal Fields at Large Scale Using Dense Stacks of Sentinel-2 Imagery Validated by Real Time Kinematic Positioning Data
by
Fernando Sedano, Daniele Borio, Martin Claverie, Guido Lemoine, Philippe Loudjani, David Alfonso Nafría, Vanessa Paredes-Gómez, Francisco Javier Rojo-Revilla, Ferdinando Urbano and Marijn Van der Velde
Agriculture 2025, 15(18), 1984; https://doi.org/10.3390/agriculture15181984 - 20 Sep 2025
Abstract
This study presents an operational and robust method for detecting and dating cereal harvest events using temporal stacks of Copernicus Sentinel-2 imagery and crop and fields border information from ancillary records. The proposed approach is exempt from training data, thereby enabling its application
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This study presents an operational and robust method for detecting and dating cereal harvest events using temporal stacks of Copernicus Sentinel-2 imagery and crop and fields border information from ancillary records. The proposed approach is exempt from training data, thereby enabling its application across diverse geographical contexts. The method was used to generate 10 m resolution maps of harvest dates for all wheat and barley fields in 2021, 2022, and 2023 in Castilla y León, a major cereal-producing region of Spain. This work also investigates the use of a reference dataset derived from real time kinematic records (RTK) in agricultural machinery as an alternative source of large-scale in situ data reference as for Earth observation-based agricultural products. The initial comparison of annual harvest date maps with the RTK-based reference datasets revealed that the temporal lag in the detection of harvest events between Earth observation-derived maps and reference harvest dates was less than 10 days for 65.7% of fields, while the temporal lag was between 10 and 30 days for 26.1% of the fields. The 3-year average root mean square error of the lag between harvest dates in the reference dataset and maps was 16.1 days. An in-depth visual analysis of the Sentinel-2 temporal series was carried out to understand and evaluate the potential and limitations of the RTK-based reference dataset. The visual inspection of a representative sample of 668 fields with large temporal lags revealed that the date of harvest of 41.11% of these fields had been correctly identified in the Sentinel-2 based maps and 16.43% of them had been incorrectly identified. The visual inspection could not find evidence of harvest in 10.52% of the analyzed fields. Monte Carlo simulations were parameterized using the findings of the visual inspection to build a series of synthetic reference datasets. Accuracy metrics calculated from synthetic datasets revealed that the quality of the harvest maps was higher than what the initial comparison against the RTK-based reference dataset suggested. The date of harvest was registered within 10 days in both the maps and the synthetic reference datasets for 90.5% of the fields, the root mean squared error of the comparison was 9.5 days, and harvest dates were registered in the Sentinel-2 based maps 2 days (median) after the dates registered in the reference dataset. These results highlight the feasibility of mapping harvest dates in cereal fields with time series of high-resolution satellite imagery and expose the potential use of alternative sources of calibration and validation datasets for Earth observation products. More generally, these results contribute to defining plausible targets for monitoring of agricultural practices with Earth observation data.
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(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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Open AccessArticle
Climatic and Topographic Controls on Soil Organic Matter Heterogeneity in Northeast China’s Black Soil Region: Implications for Sustainable Management
by
Depiao Kong, Nanchen Chu and Chong Luo
Agriculture 2025, 15(18), 1983; https://doi.org/10.3390/agriculture15181983 - 20 Sep 2025
Abstract
Soil organic matter (SOM) plays a critical role in maintaining soil fertility, sustaining ecosystem stability, and mitigating climate change impacts, making its conservation essential for agricultural sustainability. However, systematic county-level assessments of SOM spatial heterogeneity and its drivers across Northeast China remain limited,
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Soil organic matter (SOM) plays a critical role in maintaining soil fertility, sustaining ecosystem stability, and mitigating climate change impacts, making its conservation essential for agricultural sustainability. However, systematic county-level assessments of SOM spatial heterogeneity and its drivers across Northeast China remain limited, constraining region-specific soil management strategies. Understanding the spatial distribution and drivers of SOM is therefore vital for effective black soil protection in Northeast China. This study investigated the spatial heterogeneity and driving mechanisms of SOM in Northeast China, covering 289 counties across Heilongjiang, Jilin, and Liaoning Provinces. High-resolution (10 m) SOM data combined with 15 natural, climatic, soil, vegetation, and socioeconomic variables were analyzed using spatial autocorrelation (global and local Moran’s I) and the Geodetector model. Results showed that SOM exhibited a clear spatial pattern of “higher in the north and east, lower in the south and west,” with significant spatial clustering (Moran’s I = 0.730, p < 0.001). At the regional scale, climate factors were the dominant drivers, with potential evapotranspiration (q = 0.810) and mean annual temperature (q = 0.794) exerting the strongest explanatory power. At the provincial scale, dominant factors varied: topographic controls in Liaoning, climate–topography interactions in Jilin, and climate dominance in Heilongjiang. Anthropogenic footprint had limited overall influence but showed amplifying effects in certain local areas. These findings highlight the multi-scale, multi-factor nature of SOM heterogeneity and underscore the need for region-specific management strategies.
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(This article belongs to the Section Agricultural Soils)
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Open AccessArticle
RFA-YOLOv8: A Robust Tea Bud Detection Model with Adaptive Illumination Enhancement for Complex Orchard Environments
by
Qiuyue Yang, Jinan Gu, Tao Xiong, Qihang Wang, Juan Huang, Yidan Xi and Zhongkai Shen
Agriculture 2025, 15(18), 1982; https://doi.org/10.3390/agriculture15181982 - 19 Sep 2025
Abstract
Accurate detection of tea shoots in natural environments is crucial for facilitating intelligent tea picking, field management, and automated harvesting. However, the detection performance of existing methods in complex scenes remains limited due to factors such as the small size, high density, severe
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Accurate detection of tea shoots in natural environments is crucial for facilitating intelligent tea picking, field management, and automated harvesting. However, the detection performance of existing methods in complex scenes remains limited due to factors such as the small size, high density, severe overlap, and the similarity in color between tea shoots and the background. Consequently, this paper proposes an improved target detection algorithm, RFA-YOLOv8, based on YOLOv8, which aims to enhance the detection accuracy and robustness of tea shoots in natural environments. First, a self-constructed dataset containing images of tea shoots under various lighting conditions is created for model training and evaluation. Second, the multi-scale feature extraction capability of the model is enhanced by introducing RFCAConv along with the optimized SPPFCSPC module, while the spatial perception ability is improved by integrating the RFAConv module. Finally, the EIoU loss function is employed instead of CIoU to optimize the accuracy of the bounding box positioning. The experimental results demonstrate that the improved model achieves 84.1% and 58.7% in mAP@0.5 and mAP@0.5:0.95, respectively, which represent increases of 3.6% and 5.5% over the original YOLOv8. Robustness is evaluated under strong, moderate, and dim lighting conditions, yielding improvements of 6.3% and 7.1%. In dim lighting, mAP@0.5 and mAP@0.5:0.95 improve by 6.3% and 7.1%, respectively. The findings of this research provide an effective solution for the high-precision detection of tea shoots in complex lighting environments and offer theoretical and technical support for the development of smart tea gardens and automated picking.
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(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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Open AccessArticle
Grapevine Phenology, Vegetative and Reproductive Characteristics of Vitis vinifera L. cv Chardonnay in the Cape South Coast Region in South Africa
by
Erna Hailey Blancquaert, Emile Tomas Majewski, Sam Crauwels, Zhanwu Dai and Daniel Schorn-García
Agriculture 2025, 15(18), 1981; https://doi.org/10.3390/agriculture15181981 - 19 Sep 2025
Abstract
Climate change necessitates the exploration of new, cooler viticultural regions globally. Chardonnay is an early ripening variety which is subjected to temperature extremes. This study aimed to investigate the response of Chardonnay in cool climatic regions in the Cape South Coast region of
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Climate change necessitates the exploration of new, cooler viticultural regions globally. Chardonnay is an early ripening variety which is subjected to temperature extremes. This study aimed to investigate the response of Chardonnay in cool climatic regions in the Cape South Coast region of South Africa over two growing seasons in 2021–2022 and 2022–2023 in three commercial vineyards. An evaluation of the climatic, vegetative and reproductive characteristics was performed. Seasonal variation was the biggest driver of the Growing Degree Days (GDD) at the sites. Overall, the 2021–2022 season was warmer than the 2022–2023 season, but the microclimatic conditions were impacted by the cultivation practices which were applied. The canopy density and total leaf surface varied between the different sites (p < 0.01) and by season × site (p < 0.05). Site and the site × season interaction were the main drivers of the environmental conditions and cultivation practices. Canopy characteristics impacted the sugar accumulation rate over the two seasons. Grape berry transpiration was impacted by the environmental conditions at the sites. Chemical composition varied with soil depth. From the results of our study, although Chardonnay is suitable for cultivation in the Cape South region, site-specific conditions impact fruit development and the quality at harvest.
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(This article belongs to the Special Issue Climate Change and Plant Phenology: Challenges for Fruit Production)
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