Agriculture

20 pages, 1845 KiB

Open AccessArticle

Detection of Rice Leaf SPAD and Blast Disease Using Integrated Aerial and Ground Multiscale Canopy Reflectance Spectroscopy

by Aichen Wang, Zishan Song, Yuwen Xie, Jin Hu, Liyuan Zhang and Qingzhen Zhu

Agriculture 2024, 14(9), 1471; https://doi.org/10.3390/agriculture14091471 (registering DOI) - 28 Aug 2024

Abstract

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in [...] Read more.

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient (

R c v^{2}

) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields. Full article

(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)

22 pages, 3998 KiB

Open AccessArticle

Propagation Laws of Ultrasonic Continuous Signals at the Transmitting Transducer–Soil Interface

by Zhinan Wang, Caiyun Lu, Hongwen Li, Chao Wang, Longbao Wang and Hanyu Yang

Agriculture 2024, 14(9), 1470; https://doi.org/10.3390/agriculture14091470 (registering DOI) - 28 Aug 2024

Abstract

Ultrasonic detection is one of the main methods for information detection and has advantages in soil detection. Ultrasonic signals attenuate in soil, resulting in unique propagation laws. This paper studies the propagation laws of ultrasound in soil, focusing on the propagation characteristics of [...] Read more.

Ultrasonic detection is one of the main methods for information detection and has advantages in soil detection. Ultrasonic signals attenuate in soil, resulting in unique propagation laws. This paper studies the propagation laws of ultrasound in soil, focusing on the propagation characteristics of ultrasonic continuous signals at the transducer–soil interface. This study uses excitation frequency and amplitude as experimental factors and employs the discrete element simulation method to analyze the vibration characteristics of soil particles. It reveals the relationship between changes in soil pressure at the interface and the movement of the transducer. The results show that the motion curve of the transmitting transducer lags behind the soil pressure changes, and the energy of the ultrasonic signal increases with higher excitation frequency and amplitude. Specifically, the peak value of the first wave |H₀| at 40 kHz and 60 kHz is 210% and 263% of that at 20 kHz, respectively. When the excitation amplitude increases from 0.005 mm to 0.015 mm, the value of the peak value of other waves |H| increases by 323%. This paper preliminarily reveals the propagation laws of ultrasonic continuous signals at the transducer–soil interface, providing theoretical support for the development of ultrasonic soil property detection instruments. Full article

(This article belongs to the Section Agricultural Soils)

23 pages, 9057 KiB

Open AccessArticle

Innovative Designs for Cotton Bionic Topping Manipulator

by Yang Xu, Changjie Han, Jing Zhang, Bin Hu, Xu Ma and Hanping Mao

Agriculture 2024, 14(9), 1469; https://doi.org/10.3390/agriculture14091469 - 28 Aug 2024

Abstract

Topping reduces the growing point at the top of cotton plants. This process enables the plant to allocate more energy and nutrients to fruit growth, thereby enhancing both the quantity and quality of the fruit. Current cotton-topping machinery often leads to over-topping, which [...] Read more.

Topping reduces the growing point at the top of cotton plants. This process enables the plant to allocate more energy and nutrients to fruit growth, thereby enhancing both the quantity and quality of the fruit. Current cotton-topping machinery often leads to over-topping, which can affect crop yield and quality. Manual topping is effective in controlling over-topping due to its adherence to agronomic requirements, but it is labor-intensive. This study integrated principles from biology (bionics) to design a manipulator that mimics the action of hand pinching during manual topping. Screening grids of different sizes were designed based on a statistical analysis of the biological parameters of cotton tops to optimize the topping process. A disc cam mechanism was developed to enable the automatic opening and closing of the manipulator. From the results, it was evident that the spring tension must exceed 81.5 N to properly cut the cotton stem near the top. The spacing of the screening grid (40 mm) and the position of the topping manipulator (less than 50 mm) were optimized based on experimental results. Performance testing showed promising results with a 100% topping rate. This study not only identified the challenges with current cotton-topping methods but also proposed a bionics-inspired solution; a bionic manipulator equipped with a screening grid was proposed to achieve high accuracy in cotton topping, which significantly reduced over-topping rates to 6.67%. These findings are crucial for advancing agricultural technology and improving efficiency in cotton cultivation. Full article

(This article belongs to the Section Agricultural Technology)

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20 pages, 12543 KiB

Open AccessArticle

A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images

by Xingyin Duan, Xiaobo Wu, Jie Ge, Li Deng, Liang Shen, Jingwen Xu, Xiaoying Xu, Qin He, Yixin Chen, Xuesong Gao and Bing Li

Agriculture 2024, 14(9), 1468; https://doi.org/10.3390/agriculture14091468 - 28 Aug 2024

Abstract

Timely and accurate mapping of rice distribution is crucial to estimate yield, optimize agriculture spatial patterns, and ensure global food security. Feature selection (FS) methods have significantly improved computational efficiency by reducing redundancy in spectral and temporal feature sets, playing a vital role [...] Read more.

Timely and accurate mapping of rice distribution is crucial to estimate yield, optimize agriculture spatial patterns, and ensure global food security. Feature selection (FS) methods have significantly improved computational efficiency by reducing redundancy in spectral and temporal feature sets, playing a vital role in identifying and mapping paddy rice. However, the optimal feature sets selected by existing methods suffer from issues such as information redundancy or local optimality, limiting their accuracy in rice identification. Moreover, the effects of these FS methods on rice recognition in various machine learning classifiers and regions with different climatic conditions and planting structures is still unclear. To overcome these limitations, we conducted a comprehensive evaluation of the potential applications of major FS methods, including the wrapper method, embedded method, and filter method for rice mapping. A novel hierarchical lustering sequential forward selection (HCSFS) method for precisely extracting the optimal feature set for rice identification is proposed. The accuracy of the HCSFS and other FS methods for rice identification was tested with nine common machine learning classifiers. The results indicated that, among the three FS methods, the wrapper method achieved the best rice mapping performance, followed by the embedded method, and lastly, the filter method. The new HCSFS significantly reduced redundant features compared with eleven typical FS methods, demonstrating higher precision and stability, with user accuracy and producer accuracy exceeding 0.9548 and 0.9487, respectively. Additionally, the spatial distribution of rice maps generated using the optimal feature set selected by HCSFS closely aligned with actual planting patterns, markedly outperforming existing rice products. This research confirms the effectiveness and transferability of the HCSFS method for rice mapping across different climates and cultivation structures, suggesting its enormous potential for classifying other crops using time-series remote sensing images. Full article

(This article belongs to the Section Digital Agriculture)

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2 pages, 151 KiB

Open AccessEditorial

Application of Econometrics in Agricultural Production

by Laura Onofri

Agriculture 2024, 14(9), 1467; https://doi.org/10.3390/agriculture14091467 - 28 Aug 2024

Abstract

This Special Issue on “Applications of Econometrics in Agricultural Production” has aimed to rebuild and extend the approach to agricultural production analysis by including econometric methods for (a) developing a new paradigm for agricultural production analysis that acknowledges and models the relevance of [...] Read more.

This Special Issue on “Applications of Econometrics in Agricultural Production” has aimed to rebuild and extend the approach to agricultural production analysis by including econometric methods for (a) developing a new paradigm for agricultural production analysis that acknowledges and models the relevance of the combined economic and agronomic aspects of the production processes; (b) defining output and input demand and supply in agricultural production from a technical perspective, with the use of production function/or production frontier models; and (c) understanding agricultural market exchange and market distortions and failures from a quantitative perspective [...] Full article

(This article belongs to the Special Issue Application of Econometrics in Agricultural Production)

16 pages, 2075 KiB

Open AccessArticle

A Highland Barley Crop Extraction Method Based on Optimized Feature Combination of Multiple Phenological Sentinel-2 Images

by Xiaogang Wu, Kaiwen Pan, Lin Zhang, Xiulin He, Longhao Wang and Bing Guo

Agriculture 2024, 14(9), 1466; https://doi.org/10.3390/agriculture14091466 - 28 Aug 2024

Abstract

Previous studies have primarily focused on the extraction of highland barley crops using single phenological images, which ignored the selection of the optimal phenological period for classification. Utilizing the multiple phenological images from Sentinel-2 to construct 25 features, including spectral, red edge, vegetation, [...] Read more.

Previous studies have primarily focused on the extraction of highland barley crops using single phenological images, which ignored the selection of the optimal phenological period for classification. Utilizing the multiple phenological images from Sentinel-2 to construct 25 features, including spectral, red edge, vegetation, and texture features, the recursive feature elimination algorithm and the random forest algorithm (RF) were employed to optimize feature datasets for different phenological stages, which were then used for the identification and classification of high-land barley by RF. The main results were as follows: (1) Information extraction based on feature optimization combinations yielded good overall classification accuracy, with classification accuracies for highland barley being 92.56% (jointing stage), 90.90% (heading stage), 90.74% (flowering stage), 91.55% (milk ripening stage), and 90.51% (maturity stage), respectively. (2) NDVIre1 had the highest importance score (0.1792) in the feature selection combination, indicating that the red edge index contributed significantly to crop information extraction and classification. (3) The five feature variables—GLCM_Mean, RVI, homogeneity, MAX, and GLCM_Correlation—showed stability and universality in the extraction of highland barley. These results demonstrated that the images that derived from the jointing and milk ripening phenological stages had the best applicability for highland barley extraction, and the optimized feature datasets that composed of NDVIre1 were conductive to detect and monitor of highland barley crops in the mountainous regions of northwest China. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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14 pages, 1654 KiB

Open AccessReview

Crop Rotation and Diversification in China: Enhancing Sustainable Agriculture and Resilience

by Yuzhu Zou, Zhenshan Liu, Yan Chen, Yin Wang and Shijing Feng

Agriculture 2024, 14(9), 1465; https://doi.org/10.3390/agriculture14091465 - 28 Aug 2024

Viewed by 144

Abstract

Crop rotation and diversification (CRD) are crucial strategies in sustainable agriculture, offering multiple benefits to both farmers and the environment. By alternating crops or introducing diverse plant species, CRD practices improve soil fertility, reduce pest populations, and enhance nutrient availability. For example, legume-based [...] Read more.

Crop rotation and diversification (CRD) are crucial strategies in sustainable agriculture, offering multiple benefits to both farmers and the environment. By alternating crops or introducing diverse plant species, CRD practices improve soil fertility, reduce pest populations, and enhance nutrient availability. For example, legume-based rotations increase soil nitrogen levels through biological nitrogen fixation, reducing the need for synthetic fertilizers. Moreover, these practices promote more efficient water and nutrient use, reducing the reliance on synthetic fertilizers and minimizing the risk of pests and diseases. This review synthesizes findings from recent research on the role of CRD in enhancing sustainable agriculture and resilience, highlighting the potential contributions of these practices towards climate change mitigation and adaptation. Specific crop rotation systems, such as the cereal–legume rotation in temperate regions and the intercropping of maize with beans in tropical environments, are reviewed to provide a comprehensive understanding of their applicability in different agroecological contexts. The review also addresses the challenges related to implementing CRD practices, such as market demand and knowledge transfer, and suggests potential solutions to encourage broader adoption. Lastly, the potential environmental benefits, including carbon sequestration and reduced greenhouse gas emissions, are discussed, highlighting the role of CRD in building resilient agricultural systems. Collectively, this review paper emphasizes the importance of CRD methods as sustainable agricultural practices and provides key insights for researchers and farmers to effectively integrate these practices into farming systems. Full article

(This article belongs to the Special Issue Advancing Sustainable Farming Systems: Innovations, Challenges, and Solutions)

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17 pages, 597 KiB

Open AccessArticle

Effects of Marigold and Paprika Extracts as Natural Pigments on Laying Hen Productive Performances, Egg Quality and Oxidative Stability

by Cristina-Camelia Matache, Gabriela Maria Cornescu, Dumitru Drăgotoiu, Ana Elena Cișmileanu, Arabela Elena Untea, Mihaela Sărăcilă and Tatiana Dumitra Panaite

Agriculture 2024, 14(9), 1464; https://doi.org/10.3390/agriculture14091464 - 28 Aug 2024

Viewed by 154

Abstract

Enhancing the quality of eggs by using natural food sources has become a very important topic in the last decade. The objective of this study was to determine the influence of natural (marigold and paprika extracts) pigments on the shelf life of eggs [...] Read more.

Enhancing the quality of eggs by using natural food sources has become a very important topic in the last decade. The objective of this study was to determine the influence of natural (marigold and paprika extracts) pigments on the shelf life of eggs from laying hens. This research was carried out for a 6-week period on 168 Lohmann Brown laying hens (45 weeks age) divided into four groups (C, E1, E2 and E3) to assess the performances, external and internal egg quality parameters, egg yolk color, and antioxidant profile. The control group (C) was fed a standard diet (16.39% PB, 2750 kcal EM/kg compound feed) and the experimental diets were supplemented with 0.07% marigold extract (E1), 0.07% paprika extract (E2), and a mixture containing 0.07% of both extracts (E3). In summary, the study demonstrated that adding natural pigments from marigold and paprika extract with highly antioxidant lipid capacity into the diets of laying hens improved egg quality when eggs were stored at 28 days, under both storage temperature conditions (4 °C and 20 °C). Full article

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

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13 pages, 1283 KiB

Open AccessArticle

Free Gossypol Removal and Nutritional Value Enhancement of Cottonseed Meal via Solid-State Fermentation with Rhodotorula mucilaginosa TG529

by Bifan Liu, Huanyu Liu, Daohe Liu, Miao Zhou, Qian Jiang, Xiaokang Ma, Jing Wang, Bi’e Tan and Chen Zhang

Agriculture 2024, 14(9), 1463; https://doi.org/10.3390/agriculture14091463 - 27 Aug 2024

Viewed by 343

Abstract

The presence of free gossypol (FG) in cottonseed meal (CSM) greatly limits the use of CSM as a high-quality protein feed. Microbial fermentation is an effective method to simultaneously reduce FG and improve the nutritional value of CSM. In this study, using potato [...] Read more.

The presence of free gossypol (FG) in cottonseed meal (CSM) greatly limits the use of CSM as a high-quality protein feed. Microbial fermentation is an effective method to simultaneously reduce FG and improve the nutritional value of CSM. In this study, using potato dextrose agar containing acetic gossypol as a selective medium and humus soil from cotton fields as the source, we isolated six strains of fungi capable of tolerating FG. With an inoculation ratio of 8% (8 mL × 10⁶ CFU/mL cells or spores in 100 g fermented CSM), 50% moisture content, and a temperature of 30 °C, CSM was fermented for 5 days. The results showed that strain F had the highest FG removal rate at 56.43%, which was identified as Rhodotorula mucilaginosa (R. mucilaginosa) and named R. mucilaginosa TG529. Further optimization revealed that when the fermentation time was extended to 11 days, TG529 achieved a maximum FG removal rate of 73.29%. Compared to the original sample, treatment with TG529 significantly increased the contents of crude protein, acid-soluble protein, and 18 amino acids, while significantly reducing the contents of crude fiber, neutral detergent fiber (NDF), and acid detergent fiber in fermented cottonseed meal (FCSM). Using atmospheric and room temperature plasma for mutagenesis of TG529, it was found that the mutated TG529 significantly increased the contents of acid-soluble protein and phenylalanine in FCSM, significantly reduced the NDF content, and enhanced the FG removal rate to 76.50%. In summary, this study screened and mutagenized a strain of FG detoxifying fungus, R. mucilagnosa TG529, which can effectively reduce the FG content and improve the nutritional value of CSM by solid-state fermentation. Full article

(This article belongs to the Section Farm Animal Production)

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26 pages, 2878 KiB

Open AccessArticle

Governance and Development of Tourism in Rural Areas through the Lens of Media in South Bukovina (Romania)

by Ana-Irina Lequeux-Dincă and Camelia Teodorescu

Agriculture 2024, 14(9), 1462; https://doi.org/10.3390/agriculture14091462 - 26 Aug 2024

Viewed by 287

Abstract

Agritourism and rural tourism represent an essential growing sector in certain EU regions, particularly in restructured and rebranded Central and Eastern European countries (CEECs) like Romania that display important rural areas and face important societal and economic changes. The rapid growth of rural [...] Read more.

Agritourism and rural tourism represent an essential growing sector in certain EU regions, particularly in restructured and rebranded Central and Eastern European countries (CEECs) like Romania that display important rural areas and face important societal and economic changes. The rapid growth of rural tourism activities in the South Bukovina region (historically overlapping most of Suceava County) led, in the new legislative frame, to the establishment of the first regional Destination Management Organization (DMO) in Romania. By an exploratory qualitative, mixed-method case study approach, this study underscores important factors for tourism development in the region, outlining rural and agritourism variables integrated into the public authorities’ discourse. The paper innovatively focuses on the semantic analysis of online newspaper media texts and videos, complementarily analyzed by appropriate software solutions. The main results emphasize the factors for tourism development in the area through a dual cluster centered around the multilevel governance and tourism management structures represented by public authorities on the one hand and the projects, investments, and EU funding on the other. Key stakeholders’ opinion underscored public–private partnerships, supportive administrative structures, tourism events and various natural and cultural resources as sustainable elements that contribute to the successful development of tourism in the region. Full article

(This article belongs to the Special Issue Advances in Sustainable Agritourism Development)

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18 pages, 5113 KiB

Open AccessArticle

The Design and Experimentation of a Wheeled-Chassis Potato Combine Harvester with Integrated Bagging and Ton Bag-Lifting Systems

by Hucun Wang, Wuyun Zhao, Wei Sun, Xiaolong Liu, Ruijie Shi, Hua Zhang, Pengfei Chen and Kuizeng Gao

Agriculture 2024, 14(9), 1461; https://doi.org/10.3390/agriculture14091461 - 26 Aug 2024

Viewed by 193

Abstract

The mechanized harvesting level of potatoes in the arid areas of Northwest China is low and mainly relies on simple machinery to dig the soil surface, and then people manually pick up and bag the potatoes. This harvesting method has the problems of [...] Read more.

The mechanized harvesting level of potatoes in the arid areas of Northwest China is low and mainly relies on simple machinery to dig the soil surface, and then people manually pick up and bag the potatoes. This harvesting method has the problems of a high labor intensity, low operation efficiency, and high labor cost. Based on this, a wheeled-chassis potato combine harvester with integrated bagging and ton bag-lifting systems was developed, which could complete potato digging, potato–soil separation, potato–film separation, automatic bagging, and field ton bag lifting in one go. Firstly, based on the agronomic requirements and unique terrain characteristics of potato planting in this area, the structural design of the whole machine was completed with SOLIDORKS 2019 3D software. Secondly, the dynamic model was established for a numerical analysis, and the core parameters of key components were determined. The field experiments showed that the potato loss rate was 2.1%, the potato damage rate was 1.7%, the skin breaking rate was 2.5%, the impurity content was 1.9%, and the productivity was 0.15~0.23 hm²/h. The above field test indexes met the requirements of national and industrial standards. Full article

(This article belongs to the Section Agricultural Technology)

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16 pages, 6333 KiB

Open AccessArticle

Migration of Microplastics in the Rice–Duckweed System under Different Irrigation Modes

by Cheng Hong, Zhenchang Wang, Minghao Tian, Yuexiong Wang, Jinjing Liu, Xiaoman Qiang, Umidbek Masharifov and Kexin Chen

Agriculture 2024, 14(9), 1460; https://doi.org/10.3390/agriculture14091460 - 26 Aug 2024

Viewed by 311

Abstract

Microplastic (MP) pollution in agriculture is garnering growing concern due to its potential detrimental impact on soil properties and crop growth, particularly affecting staple food crops such as rice. Irrigation plays a crucial role in the migration of MPs. However, limited research has [...] Read more.

Microplastic (MP) pollution in agriculture is garnering growing concern due to its potential detrimental impact on soil properties and crop growth, particularly affecting staple food crops such as rice. Irrigation plays a crucial role in the migration of MPs. However, limited research has focused on how different irrigation modes affect the migration of MPs in paddy fields. To simulate real-world conditions, in this experiment, two different irrigation modes were set: shallow–frequent irrigation (FWI, I₀) and controlled irrigation (CI, I₁). The experiment also included treatments with and without duckweed (D₀ and D₁, respectively), as well as treatments with and without MPs (M₀ and M₁). This resulted in a total of eight treatments: I₀M₀D₀, I₀M₀D₁, I₁M₀D₀, I₁M₀D₁, I₀M₁D₀, I₀M₁D₁, I₁M₁D₀, and I₁M₁D₁. Our findings indicated that compared to CI, FWI significantly increased the MP concentration in the leakage but reduced the numbers of MPs in the first soil layer and adhered by duckweed. Notably, dry–wet cycles under CI induced soil cracking, and the MP concentrations in cracked areas were significantly higher than those of crack-free soil. Moreover, compared with the MP-free treatment, MP treatments significantly influenced rice root growth, such as enhancing the average root diameter by 13.44%, root volume by 46.87%, root surface area by 30.81%, and biomass aboveground by 26.13%, respectively. The abundance of some microorganisms was also significantly influenced by the relative mobility (R_M) of MPs. Furthermore, the root length was positively correlated with Planctomycetota. Meanwhile, Actinobacteriota was negatively correlated with the root surface area, root volume, and branch number, and Bacteroidota was negatively correlated with the number of root tips. However, further research is needed to elucidate how MPs influence microorganisms and, in turn, affect rice root growth. Full article

(This article belongs to the Section Agricultural Water Management)

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21 pages, 17037 KiB

Open AccessArticle

Spring-Finger Motion Law Analysis and Cam Slide Optimal Design of Spring-Finger Cylinder Peanut Pickup Mechanism

by Tao Xu, Lianxing Gao and Zhixia Liu

Agriculture 2024, 14(9), 1459; https://doi.org/10.3390/agriculture14091459 - 26 Aug 2024

Viewed by 234

Abstract

Two-stage harvesting is the main method used for the mechanized harvesting of peanuts in China, in which the pickup device is a core part of the combine harvester. In order to solve the problem of pod loss caused by “stack” and “loss picking” [...] Read more.

Two-stage harvesting is the main method used for the mechanized harvesting of peanuts in China, in which the pickup device is a core part of the combine harvester. In order to solve the problem of pod loss caused by “stack” and “loss picking” from peanut plants when using a traditional spring-finger cylinder pickup device, an optimal spring-finger cylinder peanut pickup mechanism was designed and its picking properties were tested. Based on the picking characteristics and picking force analysis when considering a peanut plant windrow, the ideal picking attitude and swing rule of the spring-finger were determined, and the cam slide, as the core element of the spring-finger cylinder peanut pickup device, was optimized. A mathematical model of the cam cylinder center line was established according to the swing law of the four picking stations utilizing the spring-finger. MATLAB and ADAMS were used to establish a simulation of the pickup mechanism, and kinematics and dynamics simulation analyses of the pickup mechanism were carried out. According to the design results, a prototype was constructed and a running pickup test was carried out. The peanut plant picking experiments indicated that the phenomenon of peanut plant stacking had significantly disappeared. Furthermore, through response surface analysis, the optimal working parameters of the picking device were obtained as follows: the forward speed V_m was 48.0 m/min, the rotational speed N was 50 r/min, and the ground height H was −16.8 mm. The picking rate of peanut plants was 99.21% and the pod loss rate was 1.79% under two harvesting conditions, with a peanut plant moisture content of 15% to 17%. This study provides technical support for the future design of picking devices for two-stage peanut pickup harvesters. Full article

(This article belongs to the Section Agricultural Technology)

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19 pages, 1207 KiB

Open AccessArticle

The Impact of Location of Labor Migration on Rural Households’ Income: Evidence from Jiangxi Province in China

by Lishan Li, Xin Luo, Yanshan Liu, Yuan Liu and Xiaojin Liu

Agriculture 2024, 14(9), 1458; https://doi.org/10.3390/agriculture14091458 - 26 Aug 2024

Viewed by 246

Abstract

With the increasing occurrence of labor migration (LM), off-farm employment has emerged as a crucial means to augment the income of agricultural households, bridge the urban-rural divide, and achieve rural regeneration. This study utilized a multiple linear regression model and quantile regression model [...] Read more.

With the increasing occurrence of labor migration (LM), off-farm employment has emerged as a crucial means to augment the income of agricultural households, bridge the urban-rural divide, and achieve rural regeneration. This study utilized a multiple linear regression model and quantile regression model to examine the effect of LM location on rural households’ income. The analysis is based on research data from Jiangxi Province in 2018. The outcomes reveal that both intra-country LM and outside-of-county LM could make a substantial contribution to the increase of overall household income. However, the coefficient of impact for outside-of-county LM is greater. The findings of this study successfully passed the rigorous tests for robustness and endogeneity. Furthermore, the quantile regression analysis indicates that the greatest income-generating impact of intra-county LM occurred at the 90% quantile, whereas the highest income-generating impact of outside-of-county LM appeared at the 75% quantile. The study aims to determine if there is a variation in the income impact of LM in samples with distinct features. Specifically, it investigated the scale of forestland management and the LM of the household head. The results show that the promotion effect of intra-county LM on the total income of rural households was only observed in the sample group with a forestland area larger than 50 mu. Additionally, outside-of-county LM could only promote the growth of the total income of rural households in the sample group in which the head of household has not experienced labor migration. Hence, to enhance the growth of income for rural households amidst China’s urbanization, policymakers should facilitate the controlled migration of labor from rural areas to urban areas while also encouraging the migration of labor within rural areas. Full article

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

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13 pages, 4145 KiB

Open AccessArticle

Characteristics of the Soil Organic Carbon Pool in Paddy Fields in Guangdong Province, South China

by Lijiang Hu, Ruikun Zeng, Jianwu Yao, Ziwei Liang, Zhaobing Zeng, Wenying Li, Ronghui Wang, Xianjiang Shu, Yong Chen and Jianfeng Ning

Agriculture 2024, 14(9), 1457; https://doi.org/10.3390/agriculture14091457 - 26 Aug 2024

Viewed by 267

Abstract

To understand the role of paddy soils in the global carbon cycle, it is necessary to analyze the characteristics of the organic carbon pool at different soil depths. It was hypothesized that soil organic carbon fractions including labile organic carbon fraction I (LOCF-I), [...] Read more.

To understand the role of paddy soils in the global carbon cycle, it is necessary to analyze the characteristics of the organic carbon pool at different soil depths. It was hypothesized that soil organic carbon fractions including labile organic carbon fraction I (LOCF-I), labile organic carbon fraction II (LOCF-II), and recalcitrant organic carbon (ROC) distributed differently within the soil profile. In this study, soil was collected from 27 typical rice fields in Guangdong Province, south China. The carbon fractions of the paddy field soils were analyzed and compared over a 0–60 cm depth profile. The relationship between carbon content and the physical and chemical properties of the soils was further analyzed using correlation analysis and structural equation modeling. The results showed that soil total organic carbon concentration in paddy fields was increased by 22.1% during the last four decades. In the soil organic carbon pool of 0–60 cm profile, the proportion of 67.31 to 70.31% in ROC, 21.75 to 22.06% in LOCF-I, and 7.7 to 10.63% was recorded, respectively, indicating that ROC was the dominating fraction. Storage of soil total organic carbon and fractions all decreased with the increase in soil depth. Correlation and path analysis showed that total nitrogen was the main driving factor affecting the soil carbon fractions, whereas pH and soil bulk density indirectly affected the content of carbon fractions by influencing total nitrogen. The results imply the importance of soil total nitrogen in paddy carbon management of rice cultivation. Full article

(This article belongs to the Section Agricultural Soils)

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22 pages, 5491 KiB

Open AccessArticle

Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

by Guanghe Han, Jiahui Xu, Xin Zhang and Xin Pan

Agriculture 2024, 14(9), 1454; https://doi.org/10.3390/agriculture14091454 - 26 Aug 2024

Viewed by 431

Abstract

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across [...] Read more.

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture. Full article

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

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16 pages, 2620 KiB

Open AccessArticle

Effect of Climate Change on Identification of Delayed Chilling Damage of Rice in China’s Cold Region

by Lixia Jiang, Junjie Han, Hongtao Cui, Zheng Chu, Shuling Li, Yining Zhang, Yanghui Ji, Qiujing Wang, Xiufen Li and Ping Wang

Agriculture 2024, 14(9), 1456; https://doi.org/10.3390/agriculture14091456 - 25 Aug 2024

Viewed by 405

Abstract

This study analyzed temperature and yield data from 34 meteorological stations in Heilongjiang Province during 1961–2020. Four climate averages (P₁, P₂, P₃, and P₄) were determined based on their respective time distributions (1961–1990, 1971–2000, [...] Read more.

This study analyzed temperature and yield data from 34 meteorological stations in Heilongjiang Province during 1961–2020. Four climate averages (P₁, P₂, P₃, and P₄) were determined based on their respective time distributions (1961–1990, 1971–2000, 1981–2010, and 1991–2020). The national standard temperature anomaly index was used to identify delayed chilling damage in rice cultivation compared to these climate averages. Climate tendency rate analysis, Mann–Kendall detection, and linear regression methods were employed to examine the relationship between temperature anomaly and rice yield from May to September. The results showed that there were noticeable differences in recognizing delayed chilling damage across different climate averages from 1961 to 2020. The average duration of chilling damage under P₁, P₂, P₃, and P₄ was, respectively, estimated as 8.5 years, 13.3 years, 21.4 years, and 30.9 years, with severe cold damage accounting for a significant portion (68.2–76.0%) of the total chilling damage period. The occurrence of severe cold damage increased significantly over time while light and moderate cold damage did not show a clear increasing or decreasing trend. Based on the test results, P₃ was found to be the most suitable climate average for identifying delayed chilling damage in rice cultivation from 1961 to 2020. Moreover, the incidence of chilling damage revealed declining trend over time. There was a high incidence of chilling damage in the 1960s and 1970s, followed by a decrease from the 1980s to the mid 1990s, and finally a low-incidence period after the mid-1990s. Spatially, the western regions experienced greater occurrence of chilling damage than the eastern regions. Additionally, there was a highly significant positive correlation (p < 0.01) between temperature anomalies from May to September and relative meteorological yield of rice. As temperature anomalies decreased during this period, there was an observed downward trend in relative meteorological yield of rice, indicating that delayed cold injury had a negative impact on rice production. Full article

(This article belongs to the Special Issue Monitoring, Modelling and Management of Agricultural Air Pollutants and Greenhouse Gases)

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26 pages, 23908 KiB

Open AccessArticle

Dual-Source Cooperative Optimized Energy Management Strategy for Fuel Cell Tractor Considering Drive Efficiency and Power Allocation

by Junjiang Zhang, Mingyue Shi, Mengnan Liu, Hanxiao Li, Bin Zhao and Xianghai Yan

Agriculture 2024, 14(9), 1455; https://doi.org/10.3390/agriculture14091455 - 25 Aug 2024

Viewed by 428

Abstract

To solve the problems of the low driving efficiency of a fuel cell tractor power source and the high hydrogen consumption caused by the irrational power allocation of the energy source, the power system was divided into two parts, power source and energy [...] Read more.

To solve the problems of the low driving efficiency of a fuel cell tractor power source and the high hydrogen consumption caused by the irrational power allocation of the energy source, the power system was divided into two parts, power source and energy source, and a dual-source cooperative optimization energy management strategy was proposed. Firstly, a general energy efficiency optimization method was designed for the power source composed of a traction motor and PTO motor, and the energy source was composed of a fuel cell and power battery. Secondly, the unified objective function and constraint conditions were established, and the instantaneous optimization algorithm was used to construct the weight factor. The instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were designed, respectively. Finally, with the demand power as the transfer parameter, the instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were integrated to form a dual-source collaborative optimal energy management strategy. In order to verify the effectiveness of the proposed strategy, a rule-based energy management strategy was developed as a comparison strategy and tested in an HIL test under plowing and rotary plowing conditions. The results show that the average fuel cell efficiency of the proposed strategy increased by 7.86% and 8.17%, respectively, and the proposed strategy’s equivalent hydrogen consumption decreased by 24.21% and 9.82%, respectively, compared with the comparison strategy under the two conditions. It can significantly reduce the SOC fluctuation of the power battery and extend the service life of the power battery. Full article

(This article belongs to the Topic Emerging Agricultural Engineering Sciences, Technologies, and Applications—2nd Edition)

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21 pages, 10573 KiB

Open AccessArticle

Spatial Mapping of Soil CO₂ Flux in the Yellow River Delta Farmland of China Using Multi-Source Optical Remote Sensing Data

by Wenqing Yu, Shuo Chen, Weihao Yang, Yingqiang Song and Miao Lu

Agriculture 2024, 14(9), 1453; https://doi.org/10.3390/agriculture14091453 - 25 Aug 2024

Viewed by 403

Abstract

The spatial prediction of soil CO₂ flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO₂ flux is limited by climatic conditions and land cover, resulting in low prediction [...] Read more.

The spatial prediction of soil CO₂ flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO₂ flux is limited by climatic conditions and land cover, resulting in low prediction accuracy. To this end, this study proposed a strategy of multi-source spectral satellite coordination and selected seven optical satellite remote sensing data sources (i.e., GF1-WFV, GF6-WFV, GF4-PMI, CB04-MUX, HJ2A-CCD, Sentinel 2-L2A, and Landsat 8-OLI) to extract auxiliary variables (i.e., vegetation indices and soil texture features). We developed a tree-structured Parzen estimator (TPE)-optimized extreme gradient boosting (XGBoost) model for the prediction and spatial mapping of soil CO₂ flux. SHapley additive explanation (SHAP) was used to analyze the driving effects of auxiliary variables on soil CO₂ flux. A scatter matrix correlation analysis showed that the distributions of auxiliary variables and soil CO₂ flux were skewed, and the linear correlations between them (r < 0.2) were generally weak. Compared with single-satellite variables, the TPE-XGBoost model based on multiple-satellite variables significantly improved the prediction accuracy (RMSE = 3.23 kg C ha⁻¹ d⁻¹, R² = 0.73), showing a stronger fitting ability for the spatial variability of soil CO₂ flux. The spatial mapping results of soil CO₂ flux based on the TPE-XGBoost model revealed that the high-flux areas were mainly concentrated in eastern and northern farmlands. The SHAP analysis revealed that PC2 and the TCARI of Sentinel 2-L2A and the TVI of HJ2A-CCD had significant positive driving effects on the prediction accuracy of soil CO₂ flux. The above results indicate that the integration of multiple-satellite data can enhance the reliability and accuracy of spatial predictions of soil CO₂ flux, thereby supporting regional agricultural sustainable development and climate change response strategies. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Soil and Crop Mapping)

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18 pages, 9929 KiB

Open AccessArticle

Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

by Bingquan Tian, Hailin Yu, Shuailing Zhang, Xiaoli Wang, Lei Yang, Jingqian Li, Wenhao Cui, Zesheng Wang, Liqun Lu, Yubin Lan and Jing Zhao

Agriculture 2024, 14(9), 1452; https://doi.org/10.3390/agriculture14091452 - 25 Aug 2024

Viewed by 385

Abstract

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images [...] Read more.

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R² of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R² of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels. Full article

(This article belongs to the Special Issue Application of UAVs in Precision Agriculture—2nd Edition)

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18 pages, 6717 KiB

Open AccessArticle

Parametric Analysis and Numerical Optimization of Root-Cutting Shovel of Cotton Stalk Harvester Using Discrete Element Method

by Hua Liu, Silin Cao, Dalong Han, Lei He, Yuanze Li, Jialin Cai, Hewei Meng and Shilong Wang

Agriculture 2024, 14(9), 1451; https://doi.org/10.3390/agriculture14091451 - 25 Aug 2024

Viewed by 328

Abstract

Aiming at solving the problems of the high cost of manual pulling, the low reliability of existing pulling devices, and the high breaking rates and high leakage rates in the process of cotton stalk reuse after removal from the field in the Xinjiang [...] Read more.

Aiming at solving the problems of the high cost of manual pulling, the low reliability of existing pulling devices, and the high breaking rates and high leakage rates in the process of cotton stalk reuse after removal from the field in the Xinjiang cotton area, a soil-loosening and root-cutting cotton stalk pulling and gathering machine was researched and designed; a root-cutting force model was established; the key parameters of the V-shaped root-cutting knife were calculated and optimized; and the ranges of the slide cutting angle, the cutting-edge angle, and the soil entry angle were determined. A shoveling process simulation of the V-shaped root-cutting knife and the root–soil complex was constructed, and the working mechanism of the V-shaped root-cutting knife was clarified. In order to verify the reliability and operation performance of the V-shaped root-cutting knife, the slide cutting angle, the cutting-edge angle, and the soil entry angle were used as the test factors, and a response surface test with three factors and three levels was carried out with the root-breaking force and the mean value of the cutting resistance as the test indices. The test results were analyzed by variance analysis, and the significant factors influencing the root-breaking force in descending order were the slide cutting angle, cutting-edge angle, and soil entry angle. The degrees of influence on the mean value of the cutting resistance were ordered as follows: slide cutting angle, soil entry angle, and cutting-edge angle. In order to make the V-shaped root-cutting knife achieve the optimal working state, the parameters of the test indices were optimized, and the optimal design parameters of the V-shaped root-cutting knife were set as follows: the slide cutting angle was 48.3°, the cutting-edge angle was 43.4°, and the soil entry angle was 26.2°. The field uprooting test showed that the average pass rate of root breakage was 94.8% and the average pull-out rate of cotton stalks was 93.2%. This study provides theoretical guidance for the development of a root-breaking mechanism for cotton straw harvesters. Full article

(This article belongs to the Section Agricultural Technology)

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26 pages, 4675 KiB

Open AccessArticle

Non-Destructive Detection Method of Apple Watercore: Optimization Using Optical Property Parameter Inversion and MobileNetV3

by Zihan Chen, Haoyun Wang, Jufei Wang, Huanliang Xu, Ni Mei and Sixu Zhang

Agriculture 2024, 14(9), 1450; https://doi.org/10.3390/agriculture14091450 - 25 Aug 2024

Viewed by 567

Abstract

Current methods for detecting apple watercore are expensive and potentially damaging to the fruit. To determine whether different batches of apples are suitable for long-term storage or long-distance transportation, and to classify the apples according to quality level to enhance the economic benefits [...] Read more.

Current methods for detecting apple watercore are expensive and potentially damaging to the fruit. To determine whether different batches of apples are suitable for long-term storage or long-distance transportation, and to classify the apples according to quality level to enhance the economic benefits of the apple industry, it is essential to conduct non-destructive testing for watercore. This study proposes an innovative detection method based on optical parameter inversion and the MobileNetV3 model. Initially, a three-layer plate model of apples was constructed using the Monte Carlo method to simulate the movement of photons inside the apple, generating a simulated brightness map of photons on the apple’s surface. This map was then used to train the MobileNetV3 network with dilated convolution, resulting in a pre-trained model. Through transfer learning, this model was applied to measured spectral data to detect the presence of watercore. Comparative experiments were conducted to determine the optimal transfer strategy for the frozen layers, achieving model accuracy rates of 99.13%, 97.60%, and 95.32% for two, three, and four classifications, respectively. Furthermore, the model parameters were low at 7.52 M. Test results of this study confirmed the effectiveness and lightweight characteristics of the method that combines optical property parameter inversion, the DC-MobileNetV3 model, and transfer learning for detecting apple watercore. This model provides technical support to detect watercore and other internal diseases in apples. Full article

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

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19 pages, 15752 KiB

Open AccessArticle

Research on a Trellis Grape Stem Recognition Method Based on YOLOv8n-GP

by Tong Jiang, Yane Li, Hailin Feng, Jian Wu, Weihai Sun and Yaoping Ruan

Agriculture 2024, 14(9), 1449; https://doi.org/10.3390/agriculture14091449 - 25 Aug 2024

Viewed by 305

Abstract

Grapes are an important cash crop that contributes to the rapid development of the agricultural economy. The harvesting of ripe fruits is one of the crucial steps in the grape production process. However, at present, the picking methods are mainly manual, resulting in [...] Read more.

Grapes are an important cash crop that contributes to the rapid development of the agricultural economy. The harvesting of ripe fruits is one of the crucial steps in the grape production process. However, at present, the picking methods are mainly manual, resulting in wasted time and high costs. Therefore, it is particularly important to implement intelligent grape picking, in which the accurate detection of grape stems is a key step to achieve intelligent harvesting. In this study, a trellis grape stem detection model, YOLOv8n-GP, was proposed by combining the SENetV2 attention module and CARAFE upsampling operator with YOLOv8n-pose. Specifically, this study first embedded the SENetV2 attention module at the bottom of the backbone network to enhance the model’s ability to extract key feature information. Then, we utilized the CARAFE upsampling operator to replace the upsampling modules in the neck network, expanding the sensory field of the model without increasing its parameters. Finally, to validate the detection performance of YOLOv8n-GP, we examined the effectiveness of the various keypoint detection models constructed with YOLOv8n-pose, YOLOv5-pose, YOLOv7-pose, and YOLOv7-Tiny-pose. Experimental results show that the precision, recall, mAP, and mAP-kp of YOLOv8n-GP reached 91.6%, 91.3%, 97.1%, and 95.4%, which improved by 3.7%, 3.6%, 4.6%, and 4.0%, respectively, compared to YOLOv8n-pose. Furthermore, YOLOv8n-GP exhibits superior detection performance compared with the other keypoint detection models in terms of each evaluation indicator. The experimental results demonstrate that YOLOv8n-GP can detect trellis grape stems efficiently and accurately, providing technical support for advancing intelligent grape harvesting. Full article

(This article belongs to the Section Digital Agriculture)

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20 pages, 513 KiB

Open AccessArticle

Production Process Outsourcing, Farmers’ Operation Capability, and Income-Enhancing Effects

by Chengze Li, Dianwei Zhang, Qian Lu, Jiajing Wei and Qingsong Zhang

Agriculture 2024, 14(9), 1448; https://doi.org/10.3390/agriculture14091448 - 25 Aug 2024

Viewed by 272

Abstract

Production process outsourcing not only enhances farmers’ operation capability but also contributes to income growth. Utilizing field survey data from five provinces—Inner Mongolia, Gansu, Ningxia, Henan, and Shaanxi—this study employs an endogenous switching regression model to analyze the impact of production process outsourcing [...] Read more.

Production process outsourcing not only enhances farmers’ operation capability but also contributes to income growth. Utilizing field survey data from five provinces—Inner Mongolia, Gansu, Ningxia, Henan, and Shaanxi—this study employs an endogenous switching regression model to analyze the impact of production process outsourcing on the enhancement of farmers’ operation capability and the income-enhancing effect. The results reveal the following: (1) Production process outsourcing significantly improves farmers’ operation capability and increases income. (2) A higher degree of adoption of production process outsourcing correlates with greater improvements in farmers’ operation capability. (3) The impact of production process outsourcing on farmers’ operation capability varies with individual endowments; farmers with higher education levels, a larger number of laborers, and smaller planting areas experience more pronounced improvements in management capabilities when participating in outsourcing. (4) Production process outsourcing partially mediates the income-enhancing effect through its influence on farmers’ operation capability. To further promote income growth, it is essential to enhance the agricultural outsourcing market supply system, expand farmers’ access to production service information, and prioritize the development of farmers’ operation capability. Full article

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

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14 pages, 278 KiB

Open AccessArticle

Internet-Based Information Acquisition, Technical Knowledge and Farmers’ Pesticide Use: Evidence from Rice Production in China

by Shanshan Li, Shengyang Sun and Chao Zhang

Agriculture 2024, 14(9), 1447; https://doi.org/10.3390/agriculture14091447 - 25 Aug 2024

Viewed by 286

Abstract

The overuse of pesticides has led to serious ecological and environmental degradation, largely due to the lack of effectiveness of agricultural-extension services. In recent years, an increasing number of farmers have tended to acquire technical information from the Internet. In this context, the [...] Read more.

The overuse of pesticides has led to serious ecological and environmental degradation, largely due to the lack of effectiveness of agricultural-extension services. In recent years, an increasing number of farmers have tended to acquire technical information from the Internet. In this context, the present study analyzes the impact of Internet-based information acquisition on pesticide use and the mediating role of farmers’ technical knowledge. For this purpose, the treatment-effects model and survey data covering 1113 rice farmers in Guizhou, Hubei, Jiangsu, and Zhejiang provinces in China were utilized. The results indicate that Internet-based information acquisition could significantly reduce the intensity of pesticide use by 2.036 kg/ha, accounting for the self-selection issue. Further analysis illustrates that farmers’ technical knowledge plays a significant mediating role in the negative impact of Internet-based information acquisition on pesticide-use intensity. In addition, the impacts of Internet-based information acquisition on pesticide-use intensity are heterogeneous across different education levels or rice-sown areas. Thus, the present study suggests that efforts should be made to accelerate the construction of rural information infrastructure networks to broaden smallholder farmers’ access to technical information from the Internet platforms, promote “Internet plus” agricultural-extension services, and improve farmers’ skills in using the Internet. Full article

(This article belongs to the Special Issue Agricultural Strategies for Food and Environmental Security)

20 pages, 14870 KiB

Open AccessArticle

SN-CNN: A Lightweight and Accurate Line Extraction Algorithm for Seedling Navigation in Ridge-Planted Vegetables

by Tengfei Zhang, Jinhao Zhou, Wei Liu, Rencai Yue, Jiawei Shi, Chunjian Zhou and Jianping Hu

Agriculture 2024, 14(9), 1446; https://doi.org/10.3390/agriculture14091446 - 24 Aug 2024

Viewed by 334

Abstract

In precision agriculture, after vegetable transplanters plant the seedlings, field management during the seedling stage is necessary to optimize the vegetable yield. Accurately identifying and extracting the centerlines of crop rows during the seedling stage is crucial for achieving the autonomous navigation of [...] Read more.

In precision agriculture, after vegetable transplanters plant the seedlings, field management during the seedling stage is necessary to optimize the vegetable yield. Accurately identifying and extracting the centerlines of crop rows during the seedling stage is crucial for achieving the autonomous navigation of robots. However, the transplanted ridges often experience missing seedling rows. Additionally, due to the limited computational resources of field agricultural robots, a more lightweight navigation line fitting algorithm is required. To address these issues, this study focuses on mid-to-high ridges planted with double-row vegetables and develops a seedling band-based navigation line extraction model, a Seedling Navigation Convolutional Neural Network (SN-CNN). Firstly, we proposed the C2f_UIB module, which effectively reduces redundant computations by integrating Network Architecture Search (NAS) technologies, thus improving the model’s efficiency. Additionally, the model incorporates the Simplified Attention Mechanism (SimAM) in the neck section, enhancing the focus on hard-to-recognize samples. The experimental results demonstrate that the proposed SN-CNN model outperforms YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s in terms of the model parameters and accuracy. The SN-CNN model has a parameter count of only 2.37 M and achieves an [email protected] of 94.6%. Compared to the baseline model, the parameter count is reduced by 28.4%, and the accuracy is improved by 2%. Finally, for practical deployment, the SN-CNN algorithm was implemented on the NVIDIA Jetson AGX Xavier, an embedded computing platform, to evaluate its real-time performance in navigation line fitting. We compared two fitting methods: Random Sample Consensus (RANSAC) and least squares (LS), using 100 images (50 test images and 50 field-collected images) to assess the accuracy and processing speed. The RANSAC method achieved a root mean square error (RMSE) of 5.7 pixels and a processing time of 25 milliseconds per image, demonstrating a superior fitting accuracy, while meeting the real-time requirements for navigation line detection. This performance highlights the potential of the SN-CNN model as an effective solution for autonomous navigation in field cross-ridge walking robots. Full article

(This article belongs to the Section Agricultural Technology)

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22 pages, 5324 KiB

Open AccessArticle

Evaluation of the Impact of Flutriafol on Soil Culturable Microorganisms and on Soil Enzymes Activity

by Diana-Larisa Roman, Mariana Adina Matica, Bianca-Vanesa Boros, Constantina-Bianca Vulpe and Adriana Isvoran

Agriculture 2024, 14(9), 1445; https://doi.org/10.3390/agriculture14091445 - 24 Aug 2024

Viewed by 424

Abstract

Fungicides play a role in managing plant diseases but raise concerns about environmental impact, emphasizing the need to understand and minimize their effects on non-target ecosystems. Flutriafol is a fungicide used to combat fungal diseases in crops. It has two enantiomers that exhibit [...] Read more.

Fungicides play a role in managing plant diseases but raise concerns about environmental impact, emphasizing the need to understand and minimize their effects on non-target ecosystems. Flutriafol is a fungicide used to combat fungal diseases in crops. It has two enantiomers that exhibit different levels of efficacy and environmental impact. This study focuses on evaluating the effects of different doses of flutriafol on soil microorganism populations and enzyme activity and the possible specificity of enantiomer interactions with soil enzymes by combining experimental and computational approaches. The effects of different doses of flutriafol on the population of microorganism and on the activity of soil enzymes were experimentally assessed. Molecular docking of the enantiomers with soil enzymes was used to assess the possible stereoselectivity of the interactions. Regardless of the dose used (normal dose recommended by the manufacturer for cereal crops, half this dose, and double dose), flutriafol had no significant impact on soil microbial communities or on catalase activity. The half dose of flutriafol produced increases in the activity of dehydrogenases (8%), phosphatases (26%), and urease (33%) during the first 7 days of incubation. Molecular docking showed that both enantiomers were able to bind to the active sites of dehydrogenases and phosphatases. The average value of the interaction energy observed for (R)-flutriafol with dehydrogenases was −7.85 kcal/mol, compared to −7.45 kcal/mol for the interaction of (S)-flutriafol with these enzymes. Similarly, the interaction energy obtained for the interaction of (R)-flutriafol with phosphatase was −9.16 kcal/mol, compared to −9.04 kcal/mol for the interaction of (S)-flutriafol with this enzyme. This study confirms the need to implement optimized application practices when using flutriafol by considering the enantiomer that is most effective on the target organism and less toxic to non-target ecosystems. Full article

(This article belongs to the Special Issue Chemical Pesticides and Soil Health: The Urgent Need for a New Concept in Agriculture)

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13 pages, 476 KiB

Open AccessReview

Principles and Significance of Nitrogen Management for Blackberry Production

by Nurjahan Sriti, Jeffrey Williamson, Steven Sargent, Zhanao Deng and Guodong Liu

Agriculture 2024, 14(9), 1444; https://doi.org/10.3390/agriculture14091444 - 24 Aug 2024

Viewed by 437

Abstract

Blackberry cultivation presents significant opportunities for fruit growers in subtropical regions, where nitrogen (N) is identified as a crucial macronutrient for optimal production. Given the variability in climate and soil conditions, determining the ideal N fertilizer amount can be complex. Effective blackberry cultivation [...] Read more.

Blackberry cultivation presents significant opportunities for fruit growers in subtropical regions, where nitrogen (N) is identified as a crucial macronutrient for optimal production. Given the variability in climate and soil conditions, determining the ideal N fertilizer amount can be complex. Effective blackberry cultivation requires careful attention to the principles of nutrient stewardship, including the selection of appropriate N sources, application rates, timing, and placement. Recommended N rates generally range from 25–45 kg/ha in the first year and 45–70 kg/ha in subsequent years, with adjustments based on plant type and regional conditions. The choice of fertilizer, particularly NH₄⁺, is beneficial for blackberry plants, which thrive in acidic soils and show improved biomass and chlorophyll levels with this form of N. Research on N-cycling reveals its importance in supporting new plant growth, such as primocane development. However, improper N management, either excessive or insufficient, can negatively impact flower bud production and, consequently, fruit setting and yield. By using databases such as Google Scholar, Scopus, and Web of Science, this review synthesizes existing research on the role of N in blackberry cultivation, emphasizing the importance of precise fertilization practices tailored to regional climate and soil conditions. By highlighting variations in recommended N amounts and underscoring the principles of nutrient stewardship, this review aims to guide growers in achieving sustainable and high-quality blackberry production. Full article

(This article belongs to the Section Crop Production)

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20 pages, 17564 KiB

Open AccessArticle

Spatiotemporal Dynamics and Evolution of Grain Cropping Patterns in Northeast China: Insights from Remote Sensing and Spatial Overlay Analysis

by Guoming Du, Le Han, Longcheng Yao and Bonoua Faye

Agriculture 2024, 14(9), 1443; https://doi.org/10.3390/agriculture14091443 - 24 Aug 2024

Viewed by 396

Abstract

Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns’ evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract [...] Read more.

Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns’ evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract the spatial distribution data of major grain crops in northeast China for the year 2022. Using crop classification data from 2000 to 2022, the spatial overlay analysis method identified cropping pattern types based on spatial and temporal changes. The primary cropping patterns identified were continuous maize cropping, maize–soybean rotation, mixed cropping, and continuous soybean cropping. Simultaneously, this research constructed three distinct crop periods: Period I (2000–2002), Period II (2010–2012), and Period III (2020–2022). Over three periods, these patterns covered 94.73%, 88.76%, and 86.39% of the area, respectively. The evolution of the dominant cropping pattern from Period I to Period II involved the transition from continuous soybean cropping to continuous maize cropping, while from Period II to Period III, the main shift was from continuous maize cropping to maize–soybean mixed cropping. From a spatial perspective, since Period I, maize has increasingly replaced soybean as the dominant crop, with continuous maize cropping expanding northward and continuous soybean cropping contracting. The maize–soybean rotation area also migrated northward, particularly in the core area of the Songnen Plain, evolving mostly into continuous maize cropping. Maize cropping areas exhibited significant regional characteristics, being densely distributed in the Sanjiang Plain and Liaohe Plain, and along major tributaries in northeast China. Consequently, the interplay of the natural environment, economic policies, and agricultural technologies drove these changes. The findings offer valuable insights for optimizing cropping patterns and developing rotation systems in northeast China. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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20 pages, 12454 KiB

Open AccessArticle

Research on Distributed Dual-Wheel Electric-Drive Fuzzy PI Control for Agricultural Tractors

by Qian Zhang, Caiqi Hu and Rui Li

Agriculture 2024, 14(9), 1442; https://doi.org/10.3390/agriculture14091442 - 24 Aug 2024

Viewed by 297

Abstract

In order to solve the problem that, when the vehicle speed of an agricultural distributed dual-wheel electric-drive tractor changes or the system is disturbed by off-load, the traditional PI control cannot be adjusted in time, resulting in the overshoot of steering control or [...] Read more.

In order to solve the problem that, when the vehicle speed of an agricultural distributed dual-wheel electric-drive tractor changes or the system is disturbed by off-load, the traditional PI control cannot be adjusted in time, resulting in the overshoot of steering control or control delay, meaning it then cannot travel along the target trajectory quickly and accurately, a parameter-adaptive dual-dimensional fuzzy PI speed and steering adjustment controller was proposed, which can adjust the PI parameters in real time based on the deviation between vehicle speed, steering, and reference value, as well as the rate of deviation change. Firstly, based on the operational characteristics of agricultural tractors, a dynamic model of a distributed dual-wheel tractor was established, and a hardware-in-the-loop (HIL) test bench was set up. Fuzzy PI controller algorithms for vehicle speed and steering were designed and developed. In addition, simulations and tests were carried out under no-load and off-load tractor operating conditions with MATLAB/Simulink, respectively. The results indicate that, compared with a traditional PI controller, the fuzzy PI controller exhibits a faster control response and better robustness, reducing overshoot by approximately 60% and the steady-state response time by approximately 25%. When subjected to off-load disturbances, the maximum trajectory offset is controlled within 0.08 m, and the maximum trajectory offset is reduced by 45% compared with a traditional PI controller; therefore, the fuzzy PI control algorithm proposed in this paper makes the tractor’s running trajectory more stable and has stronger anti-interference ability towards off-load disturbances. Full article

(This article belongs to the Section Agricultural Technology)

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Agriculture, Volume 14, Issue 9 (September 2024) – 37 articles

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