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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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36 pages, 74051 KB  
Review
ObjectDetection in Agriculture: A Comprehensive Review of Methods, Applications, Challenges, and Future Directions
by Zohaib Khan, Yue Shen and Hui Liu
Agriculture 2025, 15(13), 1351; https://doi.org/10.3390/agriculture15131351 - 24 Jun 2025
Cited by 37 | Viewed by 8250
Abstract
Object detection is revolutionizing precision agriculture by enabling advanced crop monitoring, weed management, pest detection, and autonomous field operations. This comprehensive review synthesizes object detection methodologies, tracing their evolution from traditional feature-based approaches to cutting-edge deep learning architectures. We analyze key agricultural applications, [...] Read more.
Object detection is revolutionizing precision agriculture by enabling advanced crop monitoring, weed management, pest detection, and autonomous field operations. This comprehensive review synthesizes object detection methodologies, tracing their evolution from traditional feature-based approaches to cutting-edge deep learning architectures. We analyze key agricultural applications, leveraging datasets like PlantVillage, DeepWeeds, and AgriNet, and introduce a novel framework for evaluating algorithm performance based on mean Average Precision (mAP), inference speed, and computational efficiency. Through a comparative analysis of leading algorithms, including Faster R-CNN, YOLO, and SSD, we identify critical trade-offs and highlight advancements in real-time detection for resource-constrained environments. Persistent challenges, such as environmental variability, limited labeled data, and model generalization, are critically examined, with proposed solutions including multi-modal data fusion and lightweight models for edge deployment. By integrating technical evaluations, meaningful insights, and actionable recommendations, this work bridges technical innovation with practical deployment, paving the way for sustainable, resilient, and productive agricultural systems. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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24 pages, 9205 KB  
Article
Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images
by Juxia Wang, Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang and Qingliang Cui
Agriculture 2025, 15(12), 1308; https://doi.org/10.3390/agriculture15121308 - 18 Jun 2025
Cited by 8 | Viewed by 1779
Abstract
The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status [...] Read more.
The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R2 = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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28 pages, 3163 KB  
Review
Review on Key Technologies for Autonomous Navigation in Field Agricultural Machinery
by Hongxuan Wu, Xinzhong Wang, Xuegeng Chen, Yafei Zhang and Yaowen Zhang
Agriculture 2025, 15(12), 1297; https://doi.org/10.3390/agriculture15121297 - 17 Jun 2025
Cited by 14 | Viewed by 6422
Abstract
Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path [...] Read more.
Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path planning, and path-tracking control. This paper presents a comprehensive review of recent advancements in these core technologies, systematically analyzing their methodologies, advantages, and application scenarios. Despite notable progress, considerable challenges persist, primarily due to the unstructured nature of farmland, varying terrain conditions, and the demand for robust and adaptive control strategies. This review also discusses current limitations and outlines prospective research directions, aiming to provide valuable insights for the future development and practical deployment of autonomous navigation systems in agricultural machinery. Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. Furthermore, tighter integration among perception, planning, and control modules will be crucial for improving system-level intelligence and coordination in real-world agricultural environments. Full article
(This article belongs to the Section Agricultural Technology)
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38 pages, 11189 KB  
Article
Evaluating Sustainability of Water–Energy–Food–Ecosystems Nexus in Water-Scarce Regions via Coupled Simulation Model
by Huanyu Chang, Yong Zhao, Yongqiang Cao, Guohua He, Qingming Wang, Rong Liu, He Ren, Jiaqi Yao and Wei Li
Agriculture 2025, 15(12), 1271; https://doi.org/10.3390/agriculture15121271 - 12 Jun 2025
Cited by 10 | Viewed by 3215
Abstract
Complex feedback mechanisms and interdependencies exist among the water–energy–food–ecosystems (WEFE) nexus. In water-scarce regions, fluctuations in the supply or demand of any single subsystem can destabilize the others, with water shortages intensifying conflicts among food production, energy consumption, and ecological sustainability. Balancing the [...] Read more.
Complex feedback mechanisms and interdependencies exist among the water–energy–food–ecosystems (WEFE) nexus. In water-scarce regions, fluctuations in the supply or demand of any single subsystem can destabilize the others, with water shortages intensifying conflicts among food production, energy consumption, and ecological sustainability. Balancing the synergies and trade-offs within the WEFE system is therefore essential for achieving sustainable development. This study adopts the natural–social water cycle as the core process and develops a coupled simulation model of the WEFE (CSM-WEFE) system, integrating food production, ecological water replenishment, and energy consumption associated with water supply and use. Based on three performance indices—reliability, coupling coordination degree, and equilibrium—a coordinated sustainable development index (CSD) is constructed to quantify the performance of WEFE system under different scenarios. An integrated evaluation framework combining the CSM-WEFE and the CSD index is then proposed to assess the sustainability of WEFE systems. The framework is applied to the Beijing–Tianjin–Hebei (BTH) region, a representative water-scarce area in China. Results reveal that the current balance between water supply and socio-economic demand in the BTH region relies heavily on excessive groundwater extraction and the appropriation of ecological water resources. Pursuing food security goals further exacerbates groundwater overexploitation and ecological degradation, thereby undermining system coordination. In contrast, limiting groundwater use improves ecological conditions but increases regional water scarcity and reduces food self-sufficiency. Even with the full operation of the South-to-North Water Diversion Project (Middle Route), the region still experiences a 16.4% water shortage. By integrating the CSM-WEFE model with the CSD evaluation approach, the proposed framework not only provides a robust tool for assessing WEFE system sustainability but also offers practical guidance for alleviating water shortages, enhancing food security, and improving ecological health in water-scarce regions. Full article
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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23 pages, 1557 KB  
Article
The Effect of Agricultural Mechanization Services on the Technical Efficiency of Cotton Production
by Yaxue Zhu, Guangyao Wang, Huijuan Du, Jiajia Liu and Qingshan Yang
Agriculture 2025, 15(11), 1233; https://doi.org/10.3390/agriculture15111233 - 5 Jun 2025
Cited by 13 | Viewed by 3732
Abstract
As the process of agricultural modernization accelerates, exploring the impact of agricultural mechanization services on production technology efficiency has become a key issue for enhancing agricultural productivity and promoting sustainable agricultural development. The study focuses on cotton growers in the Tarim River Basin [...] Read more.
As the process of agricultural modernization accelerates, exploring the impact of agricultural mechanization services on production technology efficiency has become a key issue for enhancing agricultural productivity and promoting sustainable agricultural development. The study focuses on cotton growers in the Tarim River Basin and systematically explores the impact and driving mechanisms of agricultural mechanization services (AMSs) on cotton production’s technical efficiency within the framework of the social–ecological system (SES). By employing a combination of stochastic frontier analysis (SFA) and propensity score matching (PSM), the research indicates that the adoption of AMSs significantly enhances the production technical efficiency of cotton farmers. Among the sample that adopted this service, as much as 53.04% of the farmers have their production efficiency within the range of [0.8, 0.9], demonstrating a high production capability. In contrast, the production efficiency values of the farmers who did not adopt such services are more dispersed, with inefficient samples accounting for 11.48%. Furthermore, while the technical efficiency levels across different regions are similar, there are significant efficiency differences within regions. A further analysis indicates that the age of the household head, their education level, the number of agricultural laborers in the family, the proportion of income from planting, and irrigation convenience have a positive impact on farmers’ adoption of AMSs, while the degree of land fragmentation has a negative impact. Therefore, AMSs are not only a core pathway to enhance cotton production’s technical efficiency but also an important support for promoting agricultural modernization in arid areas and strengthening farmers’ risk-resistance capabilities. Future policies should focus on optimizing service delivery, enhancing technical adaptability, and promoting regional collaboration to drive the high-quality development of the cotton industry and support sustainable rural revitalization. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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25 pages, 1595 KB  
Review
Research Status and Development Trends of Deep Reinforcement Learning in the Intelligent Transformation of Agricultural Machinery
by Jiamuyang Zhao, Shuxiang Fan, Baohua Zhang, Aichen Wang, Liyuan Zhang and Qingzhen Zhu
Agriculture 2025, 15(11), 1223; https://doi.org/10.3390/agriculture15111223 - 4 Jun 2025
Cited by 27 | Viewed by 3666
Abstract
With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, [...] Read more.
With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, DRL can help UAVs plan more efficient flight paths to cover more areas in less time. To enhance the systematicity and credibility of this review, this paper systematically examines the application status, key issues, and development trends of DRL in agricultural scenarios, based on the research literature from mainstream Chinese and English databases spanning from 2018 to 2024. From the perspective of algorithm–hardware synergy, the article provides an in-depth analysis of DRL’s specific applications in agricultural ground platform navigation, path planning for intelligent agricultural end-effectors, and autonomous operations of low-altitude unmanned aerial vehicles. It highlights the technical advantages of DRL by integrating typical experimental outcomes, such as improved path-tracking accuracy and optimized spraying coverage. Meanwhile, this paper identifies three major challenges facing DRL in agricultural contexts: the difficulty of dynamic path planning in unstructured environments, constraints imposed by edge computing resources on algorithmic real-time performance, and risks to policy reliability and safety under human–machine collaboration conditions. Looking forward, the DRL-driven smart transformation of agricultural machinery will focus on three key aspects: (1) The first aspect is developing a hybrid decision-making architecture based on model predictive control (MPC). This aims to enhance the strategic stability and decision-making interpretability of agricultural machinery (like unmanned tractors, harvesters, and drones) in complex and dynamic field environments. This is essential for ensuring the safe and reliable autonomous operation of machinery. (2) The second aspect is designing lightweight models that support edge-cloud collaborative deployment. This can meet the requirements of low-latency responses and low-power operation in edge computing scenarios during field operations, providing computational power for the real-time intelligent decision-making of machinery. (3) The third aspect is integrating meta-learning with self-supervised mechanisms. This helps improve the algorithm’s fast generalization ability across different crop types, climates, and geographical regions, ensuring the smart agricultural machinery system has broad adaptability and robustness and accelerating its application in various agricultural settings. This paper proposes research directions from three key dimensions-“algorithm capability enhancement, deployment architecture optimization, and generalization ability improvement”-offering theoretical references and practical pathways for the continuous evolution of intelligent agricultural equipment. 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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23 pages, 4799 KB  
Article
Path Tracking Control of Agricultural Automatic Navigation Vehicles Based on an Improved Sparrow Search-Pure Pursuit Algorithm
by Junhao Wen, Liwen Yao, Jiawei Zhou, Zidong Yang, Lijun Xu and Lijian Yao
Agriculture 2025, 15(11), 1215; https://doi.org/10.3390/agriculture15111215 - 1 Jun 2025
Cited by 9 | Viewed by 1943
Abstract
A pure pursuit method based on an improved sparrow search algorithm is proposed to address low path-tracking accuracy of intelligent agricultural machinery in complex farmland environments. Firstly, we construct a function relating speed to look-ahead distance and develop a fitness function based on [...] Read more.
A pure pursuit method based on an improved sparrow search algorithm is proposed to address low path-tracking accuracy of intelligent agricultural machinery in complex farmland environments. Firstly, we construct a function relating speed to look-ahead distance and develop a fitness function based on the prototype’s speed and pose deviation. Subsequently, an improved sparrow search algorithm (ISSA) is employed to adjust the pure pursuit model’s speed and look-ahead distance dynamically. Finally, improvements are made to the initialization of the original algorithm and the position update method between different populations. Simulation results indicate that the improved sparrow search algorithm exhibits faster convergence speed and better capability to escape local extrema. The real vehicle test results show that the proposed algorithm achieves an average lateral deviation of approximately 3 cm, an average heading deviation below 5°, an average stabilization distance under 5 m, and an average navigation time of around 46 s during path tracking. These results represent reductions of 51.25%, 30.62%, 49.41%, and 10.67%, respectively, compared to the traditional pure pursuit model. Compared to the pure pursuit model that only dynamically adjusts the look-ahead distance, the proposed algorithm shows reductions of 34.11%, 24.96%, 32.13%, and 11.23%, respectively. These metrics demonstrate significant improvements in path-tracking accuracy, pose correction speed, and path-tracking efficiency, indicating that the proposed algorithm can serve as a valuable reference for path-tracking research in complex agricultural environments. Full article
(This article belongs to the Section Agricultural Technology)
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26 pages, 2959 KB  
Review
Intelligent Recognition and Automated Production of Chili Peppers: A Review Addressing Varietal Diversity and Technological Requirements
by Sheng Tai, Zhong Tang, Bin Li, Shiguo Wang and Xiaohu Guo
Agriculture 2025, 15(11), 1200; https://doi.org/10.3390/agriculture15111200 - 31 May 2025
Cited by 7 | Viewed by 4311
Abstract
Chili pepper (Capsicum annuum L.), a globally important economic crop, faces production challenges characterized by high labor intensity, cost, and inefficiency. Intelligent technologies offer key opportunities for sector transformation. This review begins by outlining the diversity of major chili pepper cultivars, differences [...] Read more.
Chili pepper (Capsicum annuum L.), a globally important economic crop, faces production challenges characterized by high labor intensity, cost, and inefficiency. Intelligent technologies offer key opportunities for sector transformation. This review begins by outlining the diversity of major chili pepper cultivars, differences in key quality indicators, and the resulting specific harvesting needs. It then reviews recent progress in intelligent perception, recognition, and automation within the chili pepper industry. For perception and recognition, the review covers the evolution from traditional image processing to deep learning-based methods (e.g., YOLO and Mask R-CNN achieving a mAP > 90% in specific studies) for pepper detection, segmentation, and fine-grained cultivar identification, analyzing the performance and optimization in complex environments. In terms of automation, we systematically discuss the principles and feasibility of different mechanized harvesting machines, consider the potential of vision-based keypoint detection for the point localization of picking, and explore motion planning and control for harvesting robots (e.g., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. The main current research challenges are listed including the environmental adaptability/robustness, efficiency/real-time performance, multi-cultivar adaptability/flexibility, system integration, and cost-effectiveness. Finally, future directions are given (e.g., multimodal sensor fusion, lightweight models, and edge computing applications) in the hope of guiding the intelligent growth of the chili pepper industry. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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37 pages, 14623 KB  
Review
Research Review of Agricultural Machinery Power Chassis in Hilly and Mountainous Areas
by Yiyong Jiang, Ruochen Wang, Renkai Ding, Zeyu Sun, Yu Jiang and Wei Liu
Agriculture 2025, 15(11), 1158; https://doi.org/10.3390/agriculture15111158 - 28 May 2025
Cited by 16 | Viewed by 3986
Abstract
The terrain in hilly and mountainous areas is complex, and the level of agricultural mechanization is low. This article systematically reviews the research progress of key technologies for agricultural machinery power chassis in hilly and mountainous areas, and conducts an analysis of five [...] Read more.
The terrain in hilly and mountainous areas is complex, and the level of agricultural mechanization is low. This article systematically reviews the research progress of key technologies for agricultural machinery power chassis in hilly and mountainous areas, and conducts an analysis of five aspects: the power system, walking system, steering system, leveling system, and automatic navigation and path tracking control system. In this manuscript, (1) in terms of the power system, the technical characteristics and application scenarios of mechanical, hydraulic, and electric drive systems were compared. (2) In terms of the walking system, the performance differences between wheeled, crawler, legged, and composite walking devices and the application of suspension systems in agricultural machinery chassis were discussed. (3) In terms of the steering system, the steering characteristics of wheeled chassis and crawler chassis were analyzed, respectively. (4) In terms of the leveling system, the research progress on hydraulic and electric leveling mechanisms, as well as intelligent leveling control algorithms, was summarized. (5) The technology of automatic navigation and path tracking for agricultural machinery chassis was discussed, focusing on multi-sensor fusion and advanced control algorithms. In the future, agricultural machinery chassis will develop towards the directions of intelligence, automation, greening, being lightweight, and being multi-functionality. Full article
(This article belongs to the Special Issue Application of Smart Agricultural Technologies in Mountain Farming Systems)
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39 pages, 1425 KB  
Article
The Role of Agricultural Socialized Services in Mitigating Rural Labor Shortages: A Multi-Crop Analysis of Production Performance
by Zhixiong Liu, Yuheng Wei, Ruofan Liao and Jianxu Liu
Agriculture 2025, 15(11), 1151; https://doi.org/10.3390/agriculture15111151 - 27 May 2025
Cited by 9 | Viewed by 2084
Abstract
China’s agricultural sector faces unprecedented challenges due to rapid urbanization. The rural labor force is declining, and the agricultural workforce is aging significantly. This labor shortage, worsened by the exodus of agricultural technicians, threatens food security and agricultural sustainability. This study analyzes data [...] Read more.
China’s agricultural sector faces unprecedented challenges due to rapid urbanization. The rural labor force is declining, and the agricultural workforce is aging significantly. This labor shortage, worsened by the exodus of agricultural technicians, threatens food security and agricultural sustainability. This study analyzes data from 30 Chinese provinces from 2011 to 2022 using a transcendental logarithmic production function. The research examines how agricultural socialized services can alleviate rural labor shortages by improving production efficiency. It also investigates these services’ impact on labor input intensity and grain yield across different crops and regions. The results show that socialized agricultural services effectively promote food production. At the national level, these services can promote a 54.4% increase in total crop production. Agricultural socialized services are gradually developing toward labor substitution. The significant negative interaction coefficient between services and labor confirms this substitution effect. The input–output elasticity of these services is positive for total crop and cereal crop production in major production areas. It also shows positive elasticity for total crop and tuber crop production in non-major production areas. The national-level “service-labor” technical elasticity of substitution maintains values above zero, averaging 0.37 across regions, offering an effective solution to agricultural labor shortages. This study identifies a threshold effect where these services’ impact on food production significantly increases with business scale expansion. These findings highlight the importance of optimizing agricultural socialized services through strengthened service systems, differentiated regional strategies, technological innovation, and comprehensive support policies. Such targeted approaches would enhance substitution effects and service efficiency, addressing labor shortages and boosting food production. Full article
(This article belongs to the Topic Efficient and Sustainable Agricultural Resource Use: Advances in Research Methods and Applications)
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23 pages, 2776 KB  
Article
GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments
by Peng Gao, Jinzhen Fang, Junlin He, Shuang Ma, Guanghua Wen and Zhen Li
Agriculture 2025, 15(11), 1135; https://doi.org/10.3390/agriculture15111135 - 24 May 2025
Cited by 5 | Viewed by 1949
Abstract
Precision positioning in orchards relies on Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) integration. However, dense foliage often causes GNSS blockages, degrading accuracy and robustness. This paper proposes an optimized GNSS/INS integrated navigation method based on a hybrid Gated Recurrent Unit [...] Read more.
Precision positioning in orchards relies on Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) integration. However, dense foliage often causes GNSS blockages, degrading accuracy and robustness. This paper proposes an optimized GNSS/INS integrated navigation method based on a hybrid Gated Recurrent Unit (GRU)–Transformer model (GRU-T). The GRU–Transformer hybrid dynamically adjusts the process noise covariance matrix within an error-state Extended Kalman Filter (ES-EKF) framework to address non-stationary noise and signal outages. Forest field tests demonstrate that GRU-T significantly improves positioning accuracy. Compared with the conventional ES-EKF, the proposed method achieves reductions in position root mean square error (PRMSE) of 48.74% (East), 41.94% (North), and 61.59% (Up), and reductions in velocity root mean square error (VRMSE) of 71.5% (East), 39.31% (North), and 56.48% (Up) in the East–North–Up (ENU) coordinate frame. The GRU-T model effectively captures both short- and long-term temporal dependencies and meets real-time, high-frequency sampling requirements. These results indicate that the GRU–Transformer hybrid model enhances the accuracy and robustness of GNSS/INS navigation in complex orchard environments, offering technical support for high-precision positioning in intelligent agricultural machinery systems. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Orchard Management)
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17 pages, 531 KB  
Article
Can the Chinese Cultural Consumption Pilot Policy Facilitate Sustainable Development in the Agritourism Economy?
by Hanlian Lin, Haibo Chen, Hua Tang and Mo Chen
Agriculture 2025, 15(11), 1117; https://doi.org/10.3390/agriculture15111117 - 22 May 2025
Cited by 5 | Viewed by 1410
Abstract
The growing importance of cultural consumption in driving tourism development is reflected in its expanding scale and the simultaneous transformation and upgrading of the cultural industry. This study adopts a multi-period difference-in-differences (DID) model to leverage the quasi-natural experiment created by China’s national [...] Read more.
The growing importance of cultural consumption in driving tourism development is reflected in its expanding scale and the simultaneous transformation and upgrading of the cultural industry. This study adopts a multi-period difference-in-differences (DID) model to leverage the quasi-natural experiment created by China’s national cultural consumption pilot policy. Using panel data from 30 provinces spanning the period from 2011 to 2024, we quantitatively assess the policy’s impact on sustainable development within the agritourism economy. Specifically, the study aims to isolate and identify the net effect of the pilot policy on improving the quality and sustainability of agritourism outcomes. Empirical results demonstrate that the implementation of the national cultural consumption pilot policy significantly promotes the development of sustainable agritourism products. Moreover, the policy exerts a notable positive influence on the broader sustainable development of the agritourism economy. These effects are particularly pronounced in the eastern and central regions, while the western region exhibits comparatively weaker impacts. Heterogeneity analysis suggests that the limited effectiveness observed in the western and parts of the central regions may be attributed to constraints such as lower levels of economic development and weaker performance of control variables in promoting sustainability. Overall, this study provides robust empirical evidence supporting the wider implementation and promotion of cultural consumption pilot policies at the national level. The findings offer valuable policy implications for advancing sustainability in the agritourism sector. Full article
(This article belongs to the Topic Ecological Protection and Modern Agricultural Development)
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24 pages, 6894 KB  
Article
Early Yield Prediction of Oilseed Rape Using UAV-Based Hyperspectral Imaging Combined with Machine Learning Algorithms
by Hongyan Zhu, Chengzhi Lin, Zhihao Dong, Jun-Li Xu and Yong He
Agriculture 2025, 15(10), 1100; https://doi.org/10.3390/agriculture15101100 - 19 May 2025
Cited by 10 | Viewed by 1671
Abstract
Oilseed rape yield critically reflects varietal superiority. Rapid field-scale estimation enables efficient high-throughput breeding. This study evaluates unmanned aerial vehicle (UAV) hyperspectral imagery’s potential for yield prediction at the pod stage by utilizing wavelength selection and vegetation indices. Meanwhile, optimized feature selection algorithms [...] Read more.
Oilseed rape yield critically reflects varietal superiority. Rapid field-scale estimation enables efficient high-throughput breeding. This study evaluates unmanned aerial vehicle (UAV) hyperspectral imagery’s potential for yield prediction at the pod stage by utilizing wavelength selection and vegetation indices. Meanwhile, optimized feature selection algorithms identified effective wavelengths (EWs) and vegetation indices (VIs) for yield estimation. The optimal yield estimation models based on EWs and VIs were established, respectively, by using multiple linear regression (MLR), partial least squares regression (PLSR), extreme learning machine (ELM), and a least squares support vector machine (LS-SVM). The main results were as follows: (i) The yield prediction of oilseed rape using EWs showed better prediction and robustness compared to the full-spectral model. In particular, the competitive adaptive reweighted sampling–extreme learning machine (CARS-ELM) model (Rpre = 0.8122, RMSEP = 170.4 kg/hm2) achieved the best prediction performance. (ii) The ELM model (Rpre = 0.7674 and RMSEP = 187.6 kg/hm2), using 14 combined VIs, showed excellent performance. These results indicate that the remote sensing image data obtained from the UAV hyperspectral remote sensing system can be used to enable the high-throughput acquisition of oilseed rape yield information in the field. This study provides technical guidance for the crop yield estimation and high-throughput detection of breeding information. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)
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29 pages, 515 KB  
Article
Artificial Intelligence Models for Bankruptcy Prediction in Agriculture: Comparing the Performance of Artificial Neural Networks and Decision Trees
by Dominika Gajdosikova and Jakub Michulek
Agriculture 2025, 15(10), 1077; https://doi.org/10.3390/agriculture15101077 - 16 May 2025
Cited by 9 | Viewed by 3486
Abstract
Debt levels are a crucial factor when assessing the financial stability of agricultural firms, and excessive indebtedness is usually the most important indicator of financial distress. As agriculture is a capital-intensive sector with a high reliance on borrowed funds, firms in this sector [...] Read more.
Debt levels are a crucial factor when assessing the financial stability of agricultural firms, and excessive indebtedness is usually the most important indicator of financial distress. As agriculture is a capital-intensive sector with a high reliance on borrowed funds, firms in this sector are more vulnerable to insolvency. This study examines the performance of artificial neural networks (ANNs) and decision trees (DTs) in predicting the bankruptcy of Slovak agricultural enterprises. In an attempt to compare the models’ performances, the most consequential indebtedness ratios are investigated through machine learning approaches. ANN and DT models are found to perform significantly better than traditional forecast methods. ANN achieved an AUC of 0.9500, accuracy of 96.37%, precision of 96.60%, recall of 99.68%, and an F1-score of 98.12%, determining its robust predictive ability. DT performed a little better on AUC (0.9550) and achieved an accuracy of 97.78%, precision of 98.69%, recall of 99.01%, and an F1-score of 98.85%, determining its predictive ability and interpretability. These findings confirm the potential for applying AI-based models to enhance financial risk assessment. This study provides informative results for financial analysts, policymakers, and corporate managers in support of early intervention strategies. Additional research would be required to explore state-of-the-art AI techniques to further refine bankruptcy forecasting and financial decision-making in vulnerable sectors like agriculture. Full article
(This article belongs to the Special Issue Innovation and Sustainability in Agribusiness: Policies and Market Dynamics)
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22 pages, 4173 KB  
Article
Comprehensive Assessment of Soil Heavy Metal Contamination in Agricultural and Protected Areas: A Case Study from Iași County, Romania
by Camelia Elena Luchian, Iuliana Motrescu, Anamaria Ioana Dumitrașcu, Elena Cristina Scutarașu, Irina Gabriela Cara, Lucia Cintia Colibaba, Valeriu V. Cotea and Gerard Jităreanu
Agriculture 2025, 15(10), 1070; https://doi.org/10.3390/agriculture15101070 - 15 May 2025
Cited by 5 | Viewed by 3039
Abstract
Soil contamination with heavy metals poses a significant risk to human health and ecological systems through multiple exposure pathways: direct ingestion of crops, dermal contact with polluted soil, and bioaccumulation within the food chain. This study analyses eleven composite soils, each collected in [...] Read more.
Soil contamination with heavy metals poses a significant risk to human health and ecological systems through multiple exposure pathways: direct ingestion of crops, dermal contact with polluted soil, and bioaccumulation within the food chain. This study analyses eleven composite soils, each collected in triplicate from different sites in Iași County, four of which are designated Natura 2000 protected areas (Mârzești Forest, Plopi Lake—Belcești, Moldova Delta, and Valea lui David). The assessment includes measurements of soil humidity by the gravimetric method, pH, and organic matter content, examined in relation to heavy metal concentrations due to their well-established interdependencies. For heavy metal determination, energy-dispersive X-ray spectroscopy (EDS) using an EDAX system (AMETEK Inc., Berwyn, PA, USA) and X-ray fluorescence spectrometry (XRFS) with a Vanta 4 analyser (Olympus, Waltham, MA, USA) were employed. Additionally, scanning electron microscopy (SEM) with a Quanta 450 microscope (FEI, Thermo Scientific, Hillsboro, OR, USA) was used primarily for informational purposes and to provide a broader perspective. In the case of chromium, 45.45% of the samples exceeded the permissible levels, with concentrations ranging from 106 mg/kg to 186 mg/kg, the highest value being nearly twice the alert threshold. Notably, not all protected areas maintain contaminant levels within safe limits. The sample from the Mârzești Forest protected site revealed considerably raised concentrations of mercury, arsenic, and lead, exceeding the alert thresholds (1 mg/kg—mercury, 15 mg/kg—arsenic, and 50 mg/kg—lead) established through Order no. 756/1997 issued by the Minister of Water, Forests, and Environmental Protection from Romania. On the other hand, the sample from Podu Iloaiei, an area with intensive agricultural activity, shows contamination with mercury and cadmium, highlighting significant anthropogenic pollution. The findings of this study are expected to raise public awareness regarding soil pollution levels, particularly in densely populated regions and protected ecological zones. Moreover, the results provide a scientific basis for policymakers and relevant authorities to implement targeted measures to manage soil contamination and ensure long-term environmental sustainability. Full article
(This article belongs to the Section Agricultural Soils)
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22 pages, 2483 KB  
Article
Effects of Biofertilizer on Yield and Quality of Crops and Properties of Soil Under Field Conditions in China: A Meta-Analysis
by Baolei Pei, Ting Liu, Ziyan Xue, Jian Cao, Yunpeng Zhang, Mulan Yu, Engang Liu, Jincheng Xing, Feibing Wang, Xuqin Ren and Zhenhua Zhang
Agriculture 2025, 15(10), 1066; https://doi.org/10.3390/agriculture15101066 - 15 May 2025
Cited by 11 | Viewed by 5442
Abstract
Biofertilizers play a crucial role in promoting sustainable agriculture in China; however, comprehensive quantification of their effects and limitations in field conditions remain unclear. In this study, a meta-analysis encompassing 1818 comparisons from 107 studies was conducted to quantify their systematic effects in [...] Read more.
Biofertilizers play a crucial role in promoting sustainable agriculture in China; however, comprehensive quantification of their effects and limitations in field conditions remain unclear. In this study, a meta-analysis encompassing 1818 comparisons from 107 studies was conducted to quantify their systematic effects in field conditions in China. The results demonstrated that biofertilizers enhanced crop yields across 21 of the 23 investigated crops, with notable increases in millet (+65.42%), vegetables (e.g., Chinese cabbage +35.57%, ginger +39.18%), and legumes (kidney beans +54.03%), while cotton and rapeseed showed non-significant improvements. Nutritional quality was also improved, as evidenced by elevated levels of vitamin C (14.61%), protein (16.61%), and carotenoids (15.18%), alongside a reduction in nitrate content (21.94%). Soil health was significantly improved through increased organic matter (16.64%), enhanced enzymatic activities (urease: 57.60%; phosphatase: 43.51%), and a proliferation of beneficial microbes (bacteria: 157.10%; fungi: 30.28%), while pathogenic organisms were suppressed by 51.81%. The observed yield improvements were attributed to enhanced nutrient availability (total nitrogen: 16.67%; available phosphorus: 10.98%), optimized root growth (19.23% increase in volume), and a reduction in disease incidence (42.52%). The efficacy of biofertilizers was maximized when they were used in conjunction with organic amendments, resulting in a 29.20% increase in yield, particularly when applied prior to planting. These results show that biofertilizers boost productivity, quality, and soil functionality, depending on their production and field management practices. Their effectiveness is tied to optimizing soil properties and suppressing pathogens, providing strategies for sustainable agriculture in China. Full article
(This article belongs to the Section Agricultural Soils)
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19 pages, 2518 KB  
Article
Rice Growth Parameter Estimation Based on Remote Satellite and Unmanned Aerial Vehicle Image Fusion
by Jiaqi Duan, Hong Wang, Yuhang Yang, Mingwang Cheng and Dan Li
Agriculture 2025, 15(10), 1026; https://doi.org/10.3390/agriculture15101026 - 9 May 2025
Cited by 7 | Viewed by 1636
Abstract
Precise monitoring of the leaf area index (LAI) and soil–plant analysis development (SPAD, which represents chlorophyll content) at the field level is crucial for enhancing crop yield and formulating agricultural management strategies. Currently, most studies use multispectral sensors mounted on unmanned aerial vehicles [...] Read more.
Precise monitoring of the leaf area index (LAI) and soil–plant analysis development (SPAD, which represents chlorophyll content) at the field level is crucial for enhancing crop yield and formulating agricultural management strategies. Currently, most studies use multispectral sensors mounted on unmanned aerial vehicles (UAVs) to obtain images, whereby the spectral information is utilized to estimate rice growth parameters. Considering the cost of multispectral sensors and factors influencing rice growth parameters, this study integrated satellite remote sensing images with UAV visible-light images to obtain high-resolution multispectral images during key rice growth stages, thereby determining the rice LAI and SPAD on the same day. The vegetation indices and textural features most correlated with rice LAI and SPAD were selected using Pearson correlation analysis, and based on vegetation indices, textural features, and their combinations, regression models were established. The results indicate the following: (1) The fusion of satellite and UAV images, combined with spectral information and textural features, can significantly improve the estimation accuracy of LAI and SPAD compared to using only spectral information or textural features. (2) Sparrow search algorithm-optimized extreme gradient boosting (SSA-XGBoost) regression achieved the highest accuracy, with R2 and RMSE of 0.904 and 0.183 in LAI estimation and 0.857 and 0.882 in SPAD estimation, respectively. This demonstrates that integrating satellite and UAV images, combined with vegetation indices and texture features, can effectively establish rice LAI and SPAD estimation models, using the SSA-optimized XGBoost method, as an effective and feasible solution for precise monitoring of rice growth parameters. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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22 pages, 18367 KB  
Article
Grasping Force Optimization and DDPG Impedance Control for Apple Picking Robot End-Effector
by Xiaowei Yu, Wei Ji, Hongwei Zhang, Chengzhi Ruan, Bo Xu and Kaiyang Wu
Agriculture 2025, 15(10), 1018; https://doi.org/10.3390/agriculture15101018 - 8 May 2025
Cited by 11 | Viewed by 2036
Abstract
To minimize mechanical damage caused by an apple picking robot end-effector during the apple grasping process, and on the basis of optimizing the minimum stable grasping force of apple, a variable impedance control strategy based on a reinforcement learning deep deterministic policy gradient [...] Read more.
To minimize mechanical damage caused by an apple picking robot end-effector during the apple grasping process, and on the basis of optimizing the minimum stable grasping force of apple, a variable impedance control strategy based on a reinforcement learning deep deterministic policy gradient (DDPG) algorithm is proposed to achieve compliant grasping control for apples. Firstly, according to the apple contact force model, the gradient flow algorithm is adopted to optimize grasping force in terms of the friction cone, force balancing condition, and stability assessment index and to obtain a minimum stable grasping force for apples. Secondly, based on the analysis of the influence of impedance parameters on the control system, a variable impedance control based on the DDPG algorithm is designed, with the reward function adopted so as to improve the control performance. Then, the improved control strategy is used to train the optimized impedance control. Finally, simulation and experimental results indicate that the proposed variable impedance control outperforms the traditional impedance control by reducing the peak grasping force from 4.49 N to 4.18 N while achieving a 0.6 s faster adjustment time and a 0.24 N narrower grasping force fluctuation range. The improved impedance control successfully tracks desired grasping forces for apples of varying sizes and significantly reduces mechanical damage during apple harvesting. Full article
(This article belongs to the Section Agricultural Technology)
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38 pages, 3222 KB  
Review
Sustainable Practices for Enhancing Soil Health and Crop Quality in Modern Agriculture: A Review
by Denis-Constantin Țopa, Sorin Căpșună, Anca-Elena Calistru and Costică Ailincăi
Agriculture 2025, 15(9), 998; https://doi.org/10.3390/agriculture15090998 - 5 May 2025
Cited by 55 | Viewed by 37212
Abstract
Soil health is the cornerstone of sustainable agriculture, serving as the foundation for crop productivity, environmental resilience, and long-term ecosystem stability. Contemporary agricultural methods, characterized by excessive pesticide and fertilizer application, monoculture, and intensive tillage, have resulted in extensive soil degradation, requiring novel [...] Read more.
Soil health is the cornerstone of sustainable agriculture, serving as the foundation for crop productivity, environmental resilience, and long-term ecosystem stability. Contemporary agricultural methods, characterized by excessive pesticide and fertilizer application, monoculture, and intensive tillage, have resulted in extensive soil degradation, requiring novel strategies to restore and sustain soil functionality. This review examined sustainable practices to enhance soil health and improve crop quality in modern agricultural systems. Preserving soil’s physical, chemical, and biological characteristics is essential for its health, achievable through various agronomic strategies. Practices such as crop rotation, cover cropping, no-till or carbon farming, conservation agriculture (CA), and the use of organic amendments were explored for their ability to restore the soil structure, increase organic matter, and promote biodiversity. These initiatives seek to preserve and enhance soil ecosystems by aligning agricultural practices with ecological principles, ensuring long-term productivity and environmental stability. Enhancing soil health will improve soil functions, supporting the concept that increasing the soil organic carbon (SOC) is necessary. This study determined that conservation tillage is more advantageous for soil health than conventional tillage, a topic that is still controversial among scientists and farmers, and that various tillage systems exhibit distinct interactions. These strategies, through the integrated management of the interaction of plant, soil, microbial, and human activities, would enhance soil health. Full article
(This article belongs to the Special Issue Multiple Soil Health Assessment Methods for Changing Agricultural Environment)
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20 pages, 2336 KB  
Article
The Impact of Extreme Weather Events on Agricultural Insurance in Europe
by Alina Claudia Manescu, Flavia Mirela Barna, Horatiu Dan Regep, Camelia Maria Manescu and Cristina Cerba
Agriculture 2025, 15(9), 995; https://doi.org/10.3390/agriculture15090995 - 3 May 2025
Cited by 4 | Viewed by 4187
Abstract
In Europe, climate change has a big impact on agriculture, due to an increase in the frequency and severity of extreme weather events. Many and prolonged droughts, heatwaves, floods, and hailstorms cause major economic losses that affect crop quality and generate instability in [...] Read more.
In Europe, climate change has a big impact on agriculture, due to an increase in the frequency and severity of extreme weather events. Many and prolonged droughts, heatwaves, floods, and hailstorms cause major economic losses that affect crop quality and generate instability in supply chains. In this study, we analyse the evolution of extreme weather events across Europe starting from the 1980s. The economic losses caused by extreme events were divided into three categories: heatwaves, frost, and fires; floods; and storms. In order to identify the trend and any shifts of the trend of the extreme weather events, we calculated moving averages over different periods: 5, 10, 20, and 30 years. The moving average analysis shows how climate change has altered from causing isolated and temporary economic losses to generate a consistent upward trend in losses, with an increasingly significant impact in the short, medium, and long term. In the second part of this study, we conducted a correlation analysis between the economic losses caused by extreme weather events and variations in property insurance premiums (fire and other property damage—which includes crop insurance premiums) and we calculated correlation coefficients directly, with a one-year lag, and with a two-year lag. Thus, we analysed whether insurance markets respond immediately to incurred losses or whether, depending on climate trends, there are delays in premium adjustments. Full article
(This article belongs to the Special Issue Agri-Environmental Risk Assessment and Management for Sustainable Rural Development)
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21 pages, 1186 KB  
Review
The Role of Exogenously Applied Polyamines to Improve Heat Tolerance in Tomatoes: A Review
by Raheleh Najafi, Noémi Kappel and Maryam Mozafarian
Agriculture 2025, 15(9), 988; https://doi.org/10.3390/agriculture15090988 - 2 May 2025
Cited by 5 | Viewed by 2524
Abstract
Tomato (Solanum lycopersicum L.) is one of the most widely consumed vegetables globally and plays a crucial role in food security. However, rising temperatures due to climate change pose a significant threat to tomato cultivation by reducing yield and fruit quality. Among [...] Read more.
Tomato (Solanum lycopersicum L.) is one of the most widely consumed vegetables globally and plays a crucial role in food security. However, rising temperatures due to climate change pose a significant threat to tomato cultivation by reducing yield and fruit quality. Among various abiotic stresses, heat stress (HS) can severely impair tomato growth, reproduction, and physiological functions. Polyamines (PAs), such as spermidine (Spd), putrescine (Put), and spermine (Spm), are natural compounds that play vital roles in plant stress tolerance by modulating growth and physiological responses. This review evaluates the effects of HS on tomatoes and examines the potential of exogenously applied PAs to mitigate HS. Through detailed analysis of agronomic, physiological, and biochemical responses, the review highlights how PAs can enhance heat tolerance by improving antioxidant activity, stabilizing cellular membranes, and maintaining photosynthetic efficiency. Understanding these mechanisms can aid in developing strategies to improve tomato resilience under climate stress and ensure sustainable production. Full article
(This article belongs to the Section Crop Production)
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24 pages, 2085 KB  
Review
A Review on the Evolution of Air-Assisted Spraying in Orchards and the Associated Leaf Motion During Spraying
by Guanqun Wang, Ziyu Li, Weidong Jia, Mingxiong Ou, Xiang Dong and Zhengji Zhang
Agriculture 2025, 15(9), 964; https://doi.org/10.3390/agriculture15090964 - 29 Apr 2025
Cited by 10 | Viewed by 2752
Abstract
Air-assisted spraying is vital in modern orchard pest management by enhancing droplet penetration and coverage on complex canopies. However, the interaction between airflow, droplets, and flexible foliage remains unclear, limiting spray efficiency and environmental sustainability. This review summarizes recent advances in understanding leaf [...] Read more.
Air-assisted spraying is vital in modern orchard pest management by enhancing droplet penetration and coverage on complex canopies. However, the interaction between airflow, droplets, and flexible foliage remains unclear, limiting spray efficiency and environmental sustainability. This review summarizes recent advances in understanding leaf motion dynamics in wind and droplet fields and their impact on pesticide deposition. First, we review orchard spraying technologies, focusing on air-assisted systems and their contribution to more uniform coverage. Next, we analyze mechanisms of droplet deposition within canopies, highlighting how wind characteristics, droplet size, and canopy structure influence pesticide distribution. Special attention is given to leaf aerodynamic responses, including bending, vibration, and transient deformation induced by wind and droplet impacts. Experimental and simulation studies reveal how leaf motion affects droplet retention, spreading, and secondary splashing. The limitations of static boundary models in deposition simulations are discussed, along with the potential of fluid-structure interaction (FSI) models. Future directions include integrated leaf-droplet experiments, intelligent airflow control, and incorporating plant biomechanics into precision spraying. Understanding leaf motion in spray environments is key to enhancing orchard spraying efficiency, precision, and sustainability. Full article
(This article belongs to the Section Agricultural Technology)
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23 pages, 14677 KB  
Article
Design of and Experimentation on an Intelligent Intra-Row Obstacle Avoidance and Weeding Machine for Orchards
by Weidong Jia, Kaile Tai, Xiang Dong, Mingxiong Ou and Xiaowen Wang
Agriculture 2025, 15(9), 947; https://doi.org/10.3390/agriculture15090947 - 27 Apr 2025
Cited by 11 | Viewed by 1604
Abstract
Based on the current issues of difficulty in clearing intra-row weeds in orchards, inaccurate sensor detection, and the inability to adjust the row spacing depth, this study designs an intelligent intra-row obstacle avoidance and weeding machine for orchards. We designed the weeding machine’s [...] Read more.
Based on the current issues of difficulty in clearing intra-row weeds in orchards, inaccurate sensor detection, and the inability to adjust the row spacing depth, this study designs an intelligent intra-row obstacle avoidance and weeding machine for orchards. We designed the weeding machine’s sensor device, depth-limiting device, row spacing adjustment mechanism, joystick-based obstacle avoidance mechanism, weeding shovel, and hydraulic system. The sensor device integrates non-contact sensors and a mechanical tactile structure, which overcomes the instability of non-contact detection and avoids the risk of collision obstacle avoidance by the weeding parts. The weeding shovel can be adapted to the environments of orchards with small plant spacing. The combination of the sensor device and the obstacle avoidance mechanism realizes flexible obstacle avoidance. We used Ansys Workbench to conduct static and vibration modal analyses on the chassis of the in-field weeding machine. On this basis, through topology optimization, the chassis quality of the weeding machine is reduced by 8%, which realizes the goal of light weight and ensures the stable operation of the machinery. To further optimize the weeding operation parameters, we employed the Box–Behnken design response surface analysis, with weeding coverage as the optimization target. We systematically explored the effects of forward speed, hydraulic cylinder extension speed, and retraction speed on the weeding efficiency. The optimal operational parameter combination determined by this study for the weeding machine is as follows: forward speed of 0.5 m/s, hydraulic cylinder extension speed of 11.5 cm/s, and hydraulic cylinder retraction speed of 8 cm/s. Based on the theoretical analysis and scenario simulations, we validated the performance of the weeding machine through field experiments. The results show that the weeding machine, while exhibiting excellent obstacle avoidance performance, can achieve a maximum weeding coverage of 84.6%. This study provides a theoretical foundation and technical support for the design and development of in-field mechanical weeding, which is of great significance for achieving intelligent orchard management and further improving fruit yield and quality. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Orchard Management)
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16 pages, 5514 KB  
Article
Crop-Free-Ridge Navigation Line Recognition Based on the Lightweight Structure Improvement of YOLOv8
by Runyi Lv, Jianping Hu, Tengfei Zhang, Xinxin Chen and Wei Liu
Agriculture 2025, 15(9), 942; https://doi.org/10.3390/agriculture15090942 - 26 Apr 2025
Cited by 10 | Viewed by 1301
Abstract
This study is situated against the background of shortages in the agricultural labor force and shortages of cultivated land. In order to improve the intelligence level and operational efficiency of agricultural machinery and solve the problems of difficulties in recognizing navigation lines and [...] Read more.
This study is situated against the background of shortages in the agricultural labor force and shortages of cultivated land. In order to improve the intelligence level and operational efficiency of agricultural machinery and solve the problems of difficulties in recognizing navigation lines and a lack of real-time performance of transplanters in the crop-free ridge environment, we propose a crop-free-ridge navigation line recognition method based on an improved YOLOv8 segmentation algorithm. First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. Second, we use the least-squares method to fit the obtained point set to accurately obtain navigation lines. Finally, the method is applied to testing and analyzing the field experimental ridges. The results showed that the average precision of the improved neural network model using this method was 90.4%, with a Params of 1.8 M, a FLOPs of 8.8 G, and an FPS of 49.5. The results indicate that the model maintains high accuracy while significantly outperforming Mask-RCNN, YOLACT++, YOLOv8, and YOLO11 in terms of computational speed. The detection frame rate increased significantly, improving the real-time performance of detection. This method uses the least-squares method to fit the 55% ridge contour feature points under the picture, and the fitting navigation line shows no large deviation compared with the image ridge centerline; the result is better than that of the RANSAC fitting method. The research results indicate that this method significantly reduces the size of the model parameters and improves the recognition speed, providing a more efficient solution for the autonomous navigation of intelligent carrier aircraft. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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29 pages, 10278 KB  
Article
Design and Experiment of a Universal Harvesting Platform for Cabbage and Chinese Cabbage
by Ze Liu, Hanping Mao, Yana Wang, Tao Jiang, Zhiyu Zuo, Jiajun Chai, Chengyi Liu, Lei Shen, Shuocheng Wei and Guoxin Ma
Agriculture 2025, 15(9), 935; https://doi.org/10.3390/agriculture15090935 - 25 Apr 2025
Cited by 5 | Viewed by 1656
Abstract
To address the issue of the single-crop adaptability of current head-forming leafy vegetable harvesters in China—which limits their ability to harvest multiple vegetable varieties—a universal cabbage–Chinese cabbage harvesting platform was designed. This design was based on the statistical analysis of the physical and [...] Read more.
To address the issue of the single-crop adaptability of current head-forming leafy vegetable harvesters in China—which limits their ability to harvest multiple vegetable varieties—a universal cabbage–Chinese cabbage harvesting platform was designed. This design was based on the statistical analysis of the physical and planting parameters of major cabbage and Chinese cabbage varieties in Jiangsu and Zhejiang provinces. The harvesting platform adopts a modular design, enabling the harvesting of both Chinese cabbage and cabbage by replacing specific components and adjusting relevant parameters. Through the theoretical analysis of key components, the specific parameters of each part were determined, and a soil-trough harvesting test was conducted. The results of the Chinese cabbage harvesting test showed that at a forward speed of 1 km·h−1 and a conveyor belt speed of 60 RPM, the platform achieved optimal performance, with an extraction success rate of 86.7%, a clamping and conveying success rate of 92.3%, and an operational damage rate of 6.7%. The cabbage soil-trough harvesting test results indicated that when the extraction roller speed was 100 RPM, the conveyor belt speed was 60 RPM, and the forward speed was 1 km·h−1, the extraction and feeding success rate reached 93.3%, the conveying success rate was 100%, and the operational loss rate was 6.7%, representing the best overall performance. This study provides theoretical support and references for the design of universal harvesters for head-forming leafy vegetables. Full article
(This article belongs to the Section Agricultural Technology)
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17 pages, 5873 KB  
Article
Soil Organic Carbon Monitoring and Modelling via Machine Learning Methods Using Soil and Remote Sensing Data
by Dimitrios Triantakonstantis and Andreas Karakostas
Agriculture 2025, 15(9), 910; https://doi.org/10.3390/agriculture15090910 - 22 Apr 2025
Cited by 8 | Viewed by 2878
Abstract
(1) Background: Soil organic carbon (SOC) is an important parameter of soils and a critical factor in global carbon cycling. The accurate monitoring and modelling of SOC are essential for assessing soil fertility, facilitating sustainable land management, and mitigating climate change. (2) Methods: [...] Read more.
(1) Background: Soil organic carbon (SOC) is an important parameter of soils and a critical factor in global carbon cycling. The accurate monitoring and modelling of SOC are essential for assessing soil fertility, facilitating sustainable land management, and mitigating climate change. (2) Methods: This research paper explores the integration of machine learning (ML) approaches with soil, terrain and remotely sensed data to enhance SOC estimation. Various ML models, including Neural Networks (NNs), Random Forests (RFs), Support Vector Machines (SVMs) and Decision Trees (DTs), were trained and evaluated using a dataset comprising soil laboratory data, Sentinel-2 spectral indices, climate data and topographic features. Feature selection techniques were applied to indicate the most important predictors, improving model performance and interpretability. (3) Results: The results demonstrate the potential of ML-driven approaches to achieve high accuracy in SOC prediction. (4) Conclusions: This research highlights the advantages of leveraging big data and artificial intelligence in soil monitoring, providing a scalable and cost-effective framework for SOC assessment in agricultural and environmental applications. Full article
(This article belongs to the Special Issue GIS and Remote Sensing for Soil Quality Assessment)
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25 pages, 631 KB  
Review
A Comprehensive Review of Digital Twins Technology in Agriculture
by Ruixue Zhang, Huate Zhu, Qinglin Chang and Qirong Mao
Agriculture 2025, 15(9), 903; https://doi.org/10.3390/agriculture15090903 - 22 Apr 2025
Cited by 57 | Viewed by 17062
Abstract
Digital Twin (DT) technology has emerged as a transformative tool in various sectors, like agriculture, due to its potential to improve productivity, sustainability, and decision making processes. This paper provides a comprehensive review of the applications, challenges, and future directions of DT technology [...] Read more.
Digital Twin (DT) technology has emerged as a transformative tool in various sectors, like agriculture, due to its potential to improve productivity, sustainability, and decision making processes. This paper provides a comprehensive review of the applications, challenges, and future directions of DT technology in agriculture. We explore the key concepts and architecture of DTs, focusing on the layering and classification of DT systems. The review delves into the various applications of DTs, such as crop planting management, pest and disease control, livestock management, optimization of agricultural machinery and resource, and agricultural decision support systems. Furthermore, we highlight the integration of agricultural data acquisition, simulation, and modeling techniques that form the backbone of effective DT implementation. Despite its promising potential, the adoption of DTs in agriculture faces several technical challenges, including data acquisition issues, integration difficulties, and the standardization of 3D crop models. Finally, we discuss future direction of DT technology, emphasizing the importance of overcoming existing barriers for wider application and sustainability. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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15 pages, 3328 KB  
Article
AGRARIAN: A Hybrid AI-Driven Architecture for Smart Agriculture
by Michael C. Batistatos, Tomaso de Cola, Michail Alexandros Kourtis, Vassiliki Apostolopoulou, George K. Xilouris and Nikos C. Sagias
Agriculture 2025, 15(8), 904; https://doi.org/10.3390/agriculture15080904 - 21 Apr 2025
Cited by 19 | Viewed by 5690
Abstract
Modern agriculture is increasingly challenged by the need for scalable, sustainable, and connectivity-resilient digital solutions. While existing smart farming platforms offer valuable insights, they often rely heavily on centralized cloud infrastructure, which can be impractical in rural or remote settings. To address this [...] Read more.
Modern agriculture is increasingly challenged by the need for scalable, sustainable, and connectivity-resilient digital solutions. While existing smart farming platforms offer valuable insights, they often rely heavily on centralized cloud infrastructure, which can be impractical in rural or remote settings. To address this gap, this paper presents AGRARIAN, a hybrid AI-driven architecture that combines IoT sensor networks, UAV-based monitoring, satellite connectivity, and edge-cloud computing to deliver real-time, adaptive agricultural intelligence. AGRARIAN supports a modular and interoperable architecture structured across four layers—Sensor, Network, Data Processing, and Application—enabling flexible deployment in diverse use cases such as precision irrigation, livestock monitoring, and pest forecasting. A key innovation lies in its localized edge processing and federated AI models, which reduce reliance on continuous cloud access while maintaining analytical performance. Pilot scenarios demonstrate the system’s ability to provide timely, context-aware decision support, enhancing both operational efficiency and digital inclusion for farmers. AGRARIAN offers a robust and scalable pathway for advancing autonomous, sustainable, and connected farming systems. Full article
(This article belongs to the Special Issue Computational, AI and IT Solutions Helping Agriculture)
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24 pages, 6029 KB  
Review
Synergistic Approaches for Sustainable Remediation of Organic Contaminated Soils: Integrating Biochar and Phytoremediation
by Hao Fang, Cailing Zhou, Dong-Xing Guan, Muhammad Azeem and Gang Li
Agriculture 2025, 15(8), 905; https://doi.org/10.3390/agriculture15080905 - 21 Apr 2025
Cited by 8 | Viewed by 3525
Abstract
Various industrial and agricultural activities have led to significant organic pollution in soil, posing an ongoing threat to both soil ecosystems and human health. Among the available remediation methods, phytoremediation and biochar remediation are recognized as sustainable and low-impact approaches. However, individual remediation [...] Read more.
Various industrial and agricultural activities have led to significant organic pollution in soil, posing an ongoing threat to both soil ecosystems and human health. Among the available remediation methods, phytoremediation and biochar remediation are recognized as sustainable and low-impact approaches. However, individual remediation methods often have limitations, such as plant susceptibility to adverse soil conditions and the desorption of pollutants from biochar. Therefore, integrating biochar with phytoremediation for the remediation of organic-contaminated soils provides a complementary approach that addresses the drawbacks of applying each method alone. The key mechanism of this combined technology lies in the ability of biochar to enhance plant resilience, plant absorption of pollutants, and the degradation capacity of rhizosphere microorganisms. Simultaneously, plants can completely degrade pollutants adsorbed by biochar or present in the soil, either directly or indirectly, through root exudates. This review systematically explores the mechanisms underlying the interactions between biochar and phytoremediation, reviews the progress of their application in the remediation of organic-contaminated soils, and discusses the associated challenges and prospects. Full article
(This article belongs to the Special Issue Risk Assessment and Remediation of Agricultural Soil Pollution)
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22 pages, 1858 KB  
Review
Biochar as a Feedstock for Sustainable Fertilizers: Recent Advances and Perspectives
by Marcela Granato Barbosa dos Santos, Andressa Blasi Paiva, Rhaila da Silva Rodrigues Viana, Keiji Jindo and Cícero Célio de Figueiredo
Agriculture 2025, 15(8), 894; https://doi.org/10.3390/agriculture15080894 - 20 Apr 2025
Cited by 10 | Viewed by 8274
Abstract
The increase in the world population exerts significant pressure on expanding global agricultural production. To achieve this, the use of fertilizers is fundamental. However, highly soluble traditional chemical fertilizers can be easily leached and volatilized, causing environmental damage. Therefore, reducing the use of [...] Read more.
The increase in the world population exerts significant pressure on expanding global agricultural production. To achieve this, the use of fertilizers is fundamental. However, highly soluble traditional chemical fertilizers can be easily leached and volatilized, causing environmental damage. Therefore, reducing the use of these fertilizers and developing new and smart fertilizers is crucial. Biochar, a solid and carbon-rich pyrolysis product, has been studied both as a standalone fertilizer and as a raw material for sustainable fertilizers. Recently, a wide variety of materials and techniques have been used in the production of biochar-based fertilizers (BBFs) and need to be grouped and critically evaluated. Thus, this study aimed to conduct a literature review on new biochar-based fertilizers, involving different routes for biochar-based fertilizer synthesis and their effects on various crops. Recent results indicate the growing interest in nanomaterials and microbial processes for producing new fertilizers. Most assessed studies use biochar to produce slow-release fertilizers. The results also indicate that these new biochar-based fertilizers increase crop yields and reduce the leaching and volatilization of nutrients in soil, demonstrating significant potential as an alternative to traditional fertilizers. Therefore, the agricultural use of biochar holds environmental importance by reducing the negative impacts caused by the use of highly soluble traditional fertilizers. However, long-term field experiments and the economic feasibility of BBF production routes must be carefully studied. Full article
(This article belongs to the Section Agricultural Soils)
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14 pages, 5129 KB  
Article
Mapping for Autonomous Navigation of Agricultural Robots Through Crop Rows Using UAV
by Hasib Mansur, Manoj Gadhwal, John Eric Abon and Daniel Flippo
Agriculture 2025, 15(8), 882; https://doi.org/10.3390/agriculture15080882 - 18 Apr 2025
Cited by 12 | Viewed by 3523
Abstract
Mapping is fundamental to the autonomous navigation of agricultural robots, as it provides a comprehensive spatial understanding of the farming environment. Accurate maps enable robots to plan efficient routes, avoid obstacles, and precisely execute tasks such as planting, spraying, and harvesting. Row crop [...] Read more.
Mapping is fundamental to the autonomous navigation of agricultural robots, as it provides a comprehensive spatial understanding of the farming environment. Accurate maps enable robots to plan efficient routes, avoid obstacles, and precisely execute tasks such as planting, spraying, and harvesting. Row crop navigation presents unique challenges, and mapping plays a crucial role in optimizing routes and avoiding obstacles in coverage path planning (CPP), which is essential for efficient agricultural operations. This study proposes a simple method for using Unmanned Aerial Vehicles (UAVs) to create maps and its application to row crop navigation. A case study is presented to demonstrate the method’s viability and illustrate how the resulting map can be applied in agricultural scenarios. This study focused on two major row crops, namely corn and soybean, but the results indicate that map creation is feasible when the inter-row spaces are not obscured by canopy cover from the adjacent rows. Although the study did not apply the map in a real-world scenario, it offers valuable insights for guiding future research. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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26 pages, 14214 KB  
Article
Stereo Visual Odometry and Real-Time Appearance-Based SLAM for Mapping and Localization in Indoor and Outdoor Orchard Environments
by Imran Hussain, Xiongzhe Han and Jong-Woo Ha
Agriculture 2025, 15(8), 872; https://doi.org/10.3390/agriculture15080872 - 16 Apr 2025
Cited by 4 | Viewed by 5953
Abstract
Agricultural robots can mitigate labor shortages and advance precision farming. However, the dense vegetation canopies and uneven terrain in orchard environments reduce the reliability of traditional GPS-based localization, thereby reducing navigation accuracy and making autonomous navigation challenging. Moreover, inefficient path planning and an [...] Read more.
Agricultural robots can mitigate labor shortages and advance precision farming. However, the dense vegetation canopies and uneven terrain in orchard environments reduce the reliability of traditional GPS-based localization, thereby reducing navigation accuracy and making autonomous navigation challenging. Moreover, inefficient path planning and an increased risk of collisions affect the robot’s ability to perform tasks such as fruit harvesting, spraying, and monitoring. To address these limitations, this study integrated stereo visual odometry with real-time appearance-based mapping (RTAB-Map)-based simultaneous localization and mapping (SLAM) to improve mapping and localization in both indoor and outdoor orchard settings. The proposed system leverages stereo image pairs for precise depth estimation while utilizing RTAB-Map’s graph-based SLAM framework with loop-closure detection to ensure global map consistency. In addition, an incorporated inertial measurement unit (IMU) enhances pose estimation, thereby improving localization accuracy. Substantial improvements in both mapping and localization performance over the traditional approach were demonstrated, with an average error of 0.018 m against the ground truth for outdoor mapping and a consistent average error of 0.03 m for indoor trails with a 20.7% reduction in visual odometry trajectory deviation compared to traditional methods. Localization performance remained robust across diverse conditions, with a low RMSE of 0.207 m. Our approach provides critical insights into developing more reliable autonomous navigation systems for agricultural robots. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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14 pages, 9320 KB  
Article
A Phenology-Based Evaluation of the Optimal Proxy for Cropland Suitability Based on Crop Yield Correlations from Sentinel-2 Image Time-Series
by Dorijan Radočaj and Mladen Jurišić
Agriculture 2025, 15(8), 859; https://doi.org/10.3390/agriculture15080859 - 15 Apr 2025
Cited by 8 | Viewed by 1312
Abstract
Cropland suitability calculations quantify natural suitability according to abiotic conditions, thus making them crucial for sustainable land management. However, since ground-truth yield data are extremely scarce, there is a need to improve knowledge on the optimal proxy metric from satellite imagery, which represents [...] Read more.
Cropland suitability calculations quantify natural suitability according to abiotic conditions, thus making them crucial for sustainable land management. However, since ground-truth yield data are extremely scarce, there is a need to improve knowledge on the optimal proxy metric from satellite imagery, which represents cropland suitability and enables global applicability. This study evaluated four frequently used vegetation indices from Sentinel-2 image time-series (normalized difference vegetation index, enhanced vegetation index, enhanced vegetation index 2, and wide dynamic range vegetation index) with three phenology metrics for correlation analysis with maize and soybean yield. Four years (2019–2022) in two study areas (Iowa and Illinois) were utilized in this research, and 1000 ground-truth crop yield samples were created for each combination of study year and area. The combination of wide dynamic range vegetation index (WDRVI) and maximum vegetation index phenology metric (MAX) was an optimal proxy for maize yield prediction, while enhanced vegetation index 2 (EVI2) and MAX produced the highest correlation for soybean, producing Pearson’s correlation coefficient means of 0.506 and 0.519, respectively. This study improved our knowledge of the optimal proxy metric for cropland suitability by combining multiple large ground-truth crop yield datasets with 30 m spatial resolution satellite imagery, which can be further improved with the use of novel vegetation indices with improved resistance to a saturation effect. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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33 pages, 3982 KB  
Article
Fungal Colonization of the Anatomical Parts of Soybean Seeds Supplied with Different Nitrogen Rates and Inoculated with Bradyrhizobium japonicum
by Jacek Olszewski, Grzegorz Dzienis, Adam Okorski, Weronika Goś and Agnieszka Pszczółkowska
Agriculture 2025, 15(8), 857; https://doi.org/10.3390/agriculture15080857 - 15 Apr 2025
Cited by 6 | Viewed by 1201
Abstract
The soybean [Glycine max (L.) Merr.] plays an important role in human and animal nutrition due to its high protein and oil content. The present study was undertaken to determine the effect of different mineral nitrogen (N) rates and inoculation with Bradyrhizobium [...] Read more.
The soybean [Glycine max (L.) Merr.] plays an important role in human and animal nutrition due to its high protein and oil content. The present study was undertaken to determine the effect of different mineral nitrogen (N) rates and inoculation with Bradyrhizobium japonicum bacteria on fungal colonization of the anatomical parts of seeds (APSS) of two soybean cultivars (Aldana and Annushka). Fungi were identified with the use of the macroscopic method and the polymerase chain reaction (PCR) assay. The study demonstrated that fungal colonization was higher on soybeans cv. Annushka than cv. Aldana. The obtained results indicate that fungal colonization intensity was highest in the cotyledons, lower in the seed coat, and lowest in the embryonic axis. The APSS were colonized by pathogenic fungi belonging mostly to the genus Fusarium, as well as saprotrophic fungi represented by Alternaria alternata, Cladosporium cladosporioides, Penicillium spp., and Rhizopus nigricans. Fungal colonization intensity was highest in soybean seeds inoculated with HiStick®Soy and in control seeds, whereas the number of fungal isolates obtained from the APSS was lower in the remaining treatments: 60 kg N ha−1 + HiStick®Soy, 30 kg N ha−1 + HiStick®Soy, Nitragina, and 60 kg N ha−1. In addition, the statistical analysis revealed that fungal abundance and the biodiversity indicators of fungal communities, including relative frequency (Rf), dominance (Y), and species richness (S), differed across the analyzed APSS and years of the study, which indicates that these parameters were significantly influenced by weather conditions. The abundance of pathogenic and saprotrophic fungal species did not differ significantly between the examined soybean cultivars. Spearman’s rank correlation coefficients were calculated to assess the strength of the relationship between weather conditions and the diversity of fungal communities colonizing soybean seeds. The analysis revealed that the development of pathogenic fungi on soybean seeds was determined by temperature and precipitation on 11–30 June and 1–10 August, whereas the prevalence of saprotrophic fungi was influenced only by precipitation on 1–10 and 21–30 July and 1–10 August. The qPCR analysis demonstrated that the colonization of soybean seeds by F. graminearum and P. verrucosum was affected by all experimental factors. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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20 pages, 10195 KB  
Article
Optimizing Lucerne Productivity and Resource Efficiency in China’s Yellow River Irrigated Region: Synergistic Effects of Ridge-Film Mulching and Controlled-Release Nitrogen Fertilization
by Minhua Yin, Yuanbo Jiang, Yi Ling, Yanlin Ma, Guangping Qi, Yanxia Kang, Yayu Wang, Qiang Lu, Yujie Shang, Xiangrong Fan, Gangqiang Han, Boda Li, Jiapeng Zhu, Jinxi Chen and Haiyan Li
Agriculture 2025, 15(8), 845; https://doi.org/10.3390/agriculture15080845 - 14 Apr 2025
Cited by 8 | Viewed by 1185
Abstract
To address low productivity and water constraints in lucerne fields of China’s Gansu Yellow River Irrigation Region, this study optimized lucerne (Medicago sativa L.) cultivation through synergistic planting nitrogen regimes. A two-year field trial (2021–2022) evaluated three systems: ridge-furrow with ordinary mulch [...] Read more.
To address low productivity and water constraints in lucerne fields of China’s Gansu Yellow River Irrigation Region, this study optimized lucerne (Medicago sativa L.) cultivation through synergistic planting nitrogen regimes. A two-year field trial (2021–2022) evaluated three systems: ridge-furrow with ordinary mulch (PM), ridge-furrow with biodegradable mulch (BM), and conventional flat planting (FP), under four controlled-release N rates (0, 80, 160, 240 kg ha−1). Multidimensional assessments included growth dynamics, dry matter yield, forage quality (crude protein [CP], acid/neutral detergent fiber [ADF/NDF], relative feed value [RFV]), and resource efficiency metrics (water use efficiency [WUE], irrigation WUE [IWUE], partial factor productivity of N [PFPN], agronomic N use efficiency [ANUE]). The results showed the following: (1) Compared with conventional flat planting, ridge planting with film mulching significantly promoted lucerne growth, with ordinary plastic film providing a stronger effect than biodegradable film. Plant height and stem diameter exhibited a quadratic response to elevated nitrogen (N) application rates under identical planting patterns, peaking at intermediate N levels before declining with further increases. (2) Ridge planting with both ordinary plastic film and biodegradable film combined with an appropriate N rate improved lucerne yield and quality. In particular, the PMN2 treatment reached the highest value of yield (14,600 kg ha−1), CP (19.19%) and RFV (124.18), and the lowest value of ADF (29.63%) and NDF (48.86%), and all of them were significantly better than the other treatments (p < 0.05). (3) WUE, IWUE, PFPN, and ANUE followed the pattern PM > BM > FP. With increasing N application rates, WUE, IWUE, and ANUE initially rose and then declined, peaking under N2, whereas PFPN showed a decreasing trend and reached its maximum under N1. Principal component analysis revealed that ridge planting with ordinary plastic film combined with 160 kg·ha−1 N (PMN2) optimized lucerne performance, achieving balanced improvements in yield, forage quality, and water–nitrogen use efficiency. This regimen is recommended as the optimal strategy for lucerne cultivation in the Gansu Yellow River Irrigation Region and analogous ecoregions. Full article
(This article belongs to the Special Issue Advancements in Best Management Practices for Enhancing Soil Health and Water Quality)
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30 pages, 1289 KB  
Review
Foundation Models in Agriculture: A Comprehensive Review
by Shuolei Yin, Yejing Xi, Xun Zhang, Chengnuo Sun and Qirong Mao
Agriculture 2025, 15(8), 847; https://doi.org/10.3390/agriculture15080847 - 14 Apr 2025
Cited by 13 | Viewed by 7971
Abstract
This paper explores the transformative potential of Foundation Models (FMs) in agriculture, driven by the need for efficient and intelligent decision support systems in the face of growing global population and climate change. It begins by outlining the development history of FMs, including [...] Read more.
This paper explores the transformative potential of Foundation Models (FMs) in agriculture, driven by the need for efficient and intelligent decision support systems in the face of growing global population and climate change. It begins by outlining the development history of FMs, including general FM training processes, application trends and challenges, before focusing on Agricultural Foundation Models (AFMs). The paper examines the diversity and applications of AFMs in areas like crop classification, pest detection, and crop image segmentation, and delves into specific use cases such as agricultural knowledge question-answering, image and video analysis, decision support, and robotics. Furthermore, it discusses the challenges faced by AFMs, including data acquisition, training efficiency, data shift, and practical application challenges. Finally, the paper discusses future development directions for AFMs, emphasizing multimodal applications, integrating AFMs across the agricultural and food sectors, and intelligent decision-making systems, ultimately aiming to promote the digitalization and intelligent transformation of agriculture. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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17 pages, 5429 KB  
Article
The Development of a Lightweight DE-YOLO Model for Detecting Impurities and Broken Rice Grains
by Zhenwei Liang, Xingyue Xu, Deyong Yang and Yanbin Liu
Agriculture 2025, 15(8), 848; https://doi.org/10.3390/agriculture15080848 - 14 Apr 2025
Cited by 9 | Viewed by 1571
Abstract
A rice impurity detection algorithm model, DE-YOLO, based on YOLOX-s improvement is proposed to address the issues of small crop target recognition and the similarity of impurities in rice impurity detection. This model achieves correct recognition, classification, and detection of rice target crops [...] Read more.
A rice impurity detection algorithm model, DE-YOLO, based on YOLOX-s improvement is proposed to address the issues of small crop target recognition and the similarity of impurities in rice impurity detection. This model achieves correct recognition, classification, and detection of rice target crops with similar colors in complex environments. Firstly, changing the CBS module to the DBS module in the entire network model and replacing the standard convolution with Depthwise Separable Convolution (DSConv) can effectively reduce the number of parameters and the computational complexity, making the model lightweight. The ECANet module is introduced into the backbone feature extraction network, utilizing the weighted selection feature to cluster the network in the region of interest, enhancing attention to rice impurities and broken grains, and compensating for the reduced accuracy caused by model light weighting. The loss problem of class imbalance is optimized using the Focal Loss function. The experimental results demonstrate that the DE-YOLO model has an average accuracy (mAP) of 97.55% for detecting rice impurity crushing targets, which is 2.9% higher than the average accuracy of the original YOLOX algorithm. The recall rate (R) is 94.46%, the F1 value is 0.96, the parameter count is reduced by 48.89%, and the GFLOPS is reduced by 46.33%. This lightweight model can effectively detect rice impurity/broken targets and provide technical support for monitoring the rice impurity/ broken rate. Full article
(This article belongs to the Special Issue Mathematical Modeling for Technological Processes of Agricultural Products)
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27 pages, 17156 KB  
Article
Evaluating the Dynamic Response of Cultivated Land Expansion and Fallow Urgency in Arid Regions Using Remote Sensing and Multi-Source Data Fusion Methods
by Liqiang Shen, Zexian Li, Jiaxin Hao, Lei Wang, Huanhuan Chen, Yuejian Wang and Baofei Xia
Agriculture 2025, 15(8), 839; https://doi.org/10.3390/agriculture15080839 - 13 Apr 2025
Cited by 5 | Viewed by 1176
Abstract
In order to cope with the ecological pressure caused by the uncontrolled expansion of cultivated land in arid areas and ensure regional food security, the implementation of a cultivated land fallowing system has become an effective way to restore the ecology, alleviate the [...] Read more.
In order to cope with the ecological pressure caused by the uncontrolled expansion of cultivated land in arid areas and ensure regional food security, the implementation of a cultivated land fallowing system has become an effective way to restore the ecology, alleviate the pressure on cultivated land, and increase productivity. In view of this, this paper takes the Tarim River Basin, located in the arid zone of China’s agricultural continent, as the research object. Using a land use transfer matrix and a gravity center migration model, the paper analyzes the spatiotemporal characteristics of cultivated land expansion in the Tarim River Basin from 2000 to 2020. Through remote sensing and the integration of multi-source data, the paper constructs an arable land fallow urgency index (SILF) from multiple dimensions such as human activity intensity, ecological vulnerability, output value, water resources status, and terrain conditions. The research results show that (1) cultivated land in the Tarim River Basin expanded by 15,665.133 km2 in general, which is manifested by spreading around based on existing cultivated land, mainly from the conversion of grassland and unused land; the center of gravity of cultivated land moved 37.833 km to the northeast and 7.257 km to the southwest first. (2) The area of not urgently fallow (NUF) in the watershed showed an overall downward trend, decreasing by 10%, while the area of very urgently fallow (VUF) increased by 16%. VUF is mainly distributed in the marginal areas of cultivated land close to the desert and is gradually expanding into the interior of cultivated land. (3) The overall ecological environment of cultivated land in the watershed is showing a deteriorating trend, and the deterioration is gradually spreading from the edge of the cultivated land to the interior. (4) There are significant differences in the SILF values of different land use types after conversion to cultivated land. The urgency of fallowing cultivated land converted from unused land is the highest, followed by grassland, forest land, water bodies, and construction land. The expanded cultivated land has a higher SILF value than the original cultivated land. The research results can provide insights into regional land resource management, the formulation of cultivated land protection policies, and the ecological restoration of cultivated land. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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19 pages, 356 KB  
Article
Renewable Energy Consumption and the Ecological Footprint in Denmark: Assessing the Influence of Financial Development and Agricultural Contribution
by Mesut Dogan, Irina Georgescu, Hamza Çeştepe, Sevgi Sümerli Sarıgül and Havanur Ergün Tatar
Agriculture 2025, 15(8), 835; https://doi.org/10.3390/agriculture15080835 - 12 Apr 2025
Cited by 10 | Viewed by 2514
Abstract
The aim of this study is to empirically examine the relationship between renewable energy consumption and the ecological footprint (EF), using Denmark as a case study, based on data covering the period from 1990 to 2020. In examining this relationship, the roles of [...] Read more.
The aim of this study is to empirically examine the relationship between renewable energy consumption and the ecological footprint (EF), using Denmark as a case study, based on data covering the period from 1990 to 2020. In examining this relationship, the roles of agricultural, forestry, and fisheries value-added; economic growth; and financial development are also explored. The analysis, conducted using fractional frequency Fourier approaches, considers the presence of structural breaks. The results reveal a negative relationship between renewable energy consumption and EF, while a positive relationship is found between agricultural, forestry, and fisheries value-added; economic growth; and financial development with the EF. According to the causality analysis, a unidirectional causality is detected from renewable energy consumption to the EF. These findings highlight the potential impact of renewable energy on EF and emphasize the importance of integrating green energy investments and renewable fuel usage into strategies aimed at reducing the EF. Full article
(This article belongs to the Special Issue Sustainability and Energy Economics in Agriculture—2nd Edition)
25 pages, 16964 KB  
Article
AAB-YOLO: An Improved YOLOv11 Network for Apple Detection in Natural Environments
by Liusong Yang, Tian Zhang, Shihan Zhou and Jingtan Guo
Agriculture 2025, 15(8), 836; https://doi.org/10.3390/agriculture15080836 - 12 Apr 2025
Cited by 9 | Viewed by 2056
Abstract
Apple detection in natural environments is crucial for advancing agricultural automation. However, orchards often employ bagging techniques to protect apples from pests and improve quality, which introduces significant detection challenges due to the varied appearance and occlusion of apples caused by bags. Additionally, [...] Read more.
Apple detection in natural environments is crucial for advancing agricultural automation. However, orchards often employ bagging techniques to protect apples from pests and improve quality, which introduces significant detection challenges due to the varied appearance and occlusion of apples caused by bags. Additionally, the complex and variable natural backgrounds further complicate the detection process. To address these multifaceted challenges, this study introduces AAB-YOLO, a lightweight apple detection model based on an improved YOLOv11 framework. AAB-YOLO incorporates ADown modules to reduce model complexity, the C3k2_ContextGuided module for enhanced understanding of complex scenes, and the Detect_SEAM module for improved handling of occluded apples. Furthermore, the Inner_EIoU loss function is employed to boost detection accuracy and efficiency. The experimental results demonstrate significant improvements: mAP@50 increases from 0.917 to 0.921, precision rises from 0.948 to 0.951, and recall improves by 1.04%, while the model’s parameter count and computational complexity are reduced by 37.7% and 38.1%, respectively. By achieving lightweight performance while maintaining high accuracy, AAB-YOLO effectively meets the real-time apple detection needs in natural environments, overcoming the challenges posed by orchard bagging techniques and complex backgrounds. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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20 pages, 7104 KB  
Article
CEFW-YOLO: A High-Precision Model for Plant Leaf Disease Detection in Natural Environments
by Jinxian Tao, Xiaoli Li, Yong He and Muhammad Adnan Islam
Agriculture 2025, 15(8), 833; https://doi.org/10.3390/agriculture15080833 - 12 Apr 2025
Cited by 10 | Viewed by 2453
Abstract
The accurate and rapid detection of apple leaf diseases is a critical component of precision management in apple orchards. The existing deep-learning-based detection algorithms for apple leaf diseases typically demand high computational resources, which limits their practical applicability in orchard environments. Furthermore, the [...] Read more.
The accurate and rapid detection of apple leaf diseases is a critical component of precision management in apple orchards. The existing deep-learning-based detection algorithms for apple leaf diseases typically demand high computational resources, which limits their practical applicability in orchard environments. Furthermore, the detection of apple leaf diseases in natural settings faces significant challenges due to the diversity of disease types, the varied morphology of affected areas, and the influence of factors such as lighting variations, leaf occlusions, and differences in disease severity. To address the above challenges, we constructed an apple leaf disease detection (ALD) dataset, which was collected from real-world scenarios, and we applied data augmentation techniques, resulting in a total of 9808 images. Based on the ALD dataset, we proposed a lightweight YOLO11n-based detection network, named CEFW-YOLO, designed to tackle the current issues in apple leaf disease identification. First, we designed a novel channel-wise squeeze convolution (CWSConv), which employs channel compression and standard convolution to reduce computational resource consumption, enhance the detection of small objects, and improve the model’s adaptability to the morphological diversity of apple leaf diseases and complex backgrounds. Second, we developed an enhanced cross-channel attention (ECCAttention) module and integrated it into the C2PSA_ECCAttention module. By extracting global information, combining horizontal and vertical convolutions, and strengthening cross-channel interactions, this module enables the model to more accurately capture disease features on apple leaves, thereby enhancing detection accuracy and robustness. Additionally, we introduced a new fine-grained multi-level linear attention (FMLAttention) module, which utilizes multi-level asymmetric convolutions and linear attention mechanisms to improve the model’s ability to capture fine-grained features and local details critical for disease detection. Finally, we incorporated the Wise-IoU (WIoU) loss function, which enhances the model’s ability to differentiate overlapping targets across multiple scales. A comprehensive evaluation of CEFW-YOLO was conducted, comparing its performance against state-of-the-art (SOTA) models. CEFW-YOLO achieved a 20.6% reduction in computational complexity. Compared to the original YOLO11n, it improved detection precision by 3.7%, with the mAP@0.5 and mAP@0.5:0.95 increasing by 7.6% and 5.2%, respectively. Notably, CEFW-YOLO outperformed advanced SOTA algorithms in apple leaf disease detection, underscoring its practical application potential in real-world orchard scenarios. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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30 pages, 24057 KB  
Article
Enhancing Autonomous Orchard Navigation: A Real-Time Convolutional Neural Network-Based Obstacle Classification System for Distinguishing ‘Real’ and ‘Fake’ Obstacles in Agricultural Robotics
by Tabinda Naz Syed, Jun Zhou, Imran Ali Lakhiar, Francesco Marinello, Tamiru Tesfaye Gemechu, Luke Toroitich Rottok and Zhizhen Jiang
Agriculture 2025, 15(8), 827; https://doi.org/10.3390/agriculture15080827 - 10 Apr 2025
Cited by 15 | Viewed by 2677
Abstract
Autonomous navigation in agricultural environments requires precise obstacle classification to ensure collision-free movement. This study proposes a convolutional neural network (CNN)-based model designed to enhance obstacle classification for agricultural robots, particularly in orchards. Building upon a previously developed YOLOv8n-based real-time detection system, the [...] Read more.
Autonomous navigation in agricultural environments requires precise obstacle classification to ensure collision-free movement. This study proposes a convolutional neural network (CNN)-based model designed to enhance obstacle classification for agricultural robots, particularly in orchards. Building upon a previously developed YOLOv8n-based real-time detection system, the model incorporates Ghost Modules and Squeeze-and-Excitation (SE) blocks to enhance feature extraction while maintaining computational efficiency. Obstacles are categorized as “Real”—those that physically impact navigation, such as tree trunks and persons—and “Fake”—those that do not, such as tall weeds and tree branches—allowing for precise navigation decisions. The model was trained on separate orchard and campus datasets and fine-tuned using Hyperband optimization and evaluated on an external test set to assess generalization to unseen obstacles. The model’s robustness was tested under varied lighting conditions, including low-light scenarios, to ensure real-world applicability. Computational efficiency was analyzed based on inference speed, memory consumption, and hardware requirements. Comparative analysis against state-of-the-art classification models (VGG16, ResNet50, MobileNetV3, DenseNet121, EfficientNetB0, and InceptionV3) confirmed the proposed model’s superior precision (p), recall (r), and F1-score, particularly in complex orchard scenarios. The model maintained strong generalization across diverse environmental conditions, including varying illumination and previously unseen obstacles. Furthermore, computational analysis revealed that the orchard-combined model achieved the highest inference speed at 2.31 FPS while maintaining a strong balance between accuracy and efficiency. When deployed in real-time, the model achieved 95.0% classification accuracy in orchards and 92.0% in campus environments. The real-time system demonstrated a false positive rate of 8.0% in the campus environment and 2.0% in the orchard, with a consistent false negative rate of 8.0% across both environments. These results validate the model’s effectiveness for real-time obstacle differentiation in agricultural settings. Its strong generalization, robustness to unseen obstacles, and computational efficiency make it well-suited for deployment in precision agriculture. Future work will focus on enhancing inference speed, improving performance under occlusion, and expanding dataset diversity to further strengthen real-world applicability. Full article
(This article belongs to the Special Issue Cutting-Edge Technology in Agricultural Robotics: Sensing and Actuation)
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23 pages, 5636 KB  
Article
A Machine Learning-Based Method for Pig Weight Estimation and the PIGRGB-Weight Dataset
by Xintong Ji, Qifeng Li, Kaijun Guo, Weihong Ma, Mingyu Li, Zhankang Xu, Simon X. Yang and Zhiyu Ren
Agriculture 2025, 15(8), 814; https://doi.org/10.3390/agriculture15080814 - 9 Apr 2025
Cited by 8 | Viewed by 4957
Abstract
Traditional pig weighing methods are costly, require driving pigs onto electronic scales, and cannot collect real-time data without interference. Pig weight estimation using deep learning often demands significant computational resources and lacks real-time capabilities, highlighting the need for a more efficient method. To [...] Read more.
Traditional pig weighing methods are costly, require driving pigs onto electronic scales, and cannot collect real-time data without interference. Pig weight estimation using deep learning often demands significant computational resources and lacks real-time capabilities, highlighting the need for a more efficient method. To overcome these challenges, this study proposes a machine learning-based approach for real-time pig weight estimation by extracting image features. The method reduces computational demands while maintaining high accuracy. The SAM2-Pig model is employed for instant segmentation of pig RGB images to extract features such as relative projection area, body length, and body width, which are crucial for accurate weight prediction. Regression models, including the BPNN with Trainlm, are used to predict pig weight based on the extracted features, achieving the best performance in our experiments. This study demonstrates that machine learning methods using RGB image features provide accurate and adaptable results, offering a viable solution for real-time pig weight estimation. This study also publicly releases the PIGRGB-Weight dataset, consisting of 9579 RGB images of pigs in a free-moving state, annotated with weight information, enabling future research and model testing. The method demonstrates remarkable stability, low computational demand, and practical applicability, making it a lightweight and effective approach for estimating pig weight in real time. Full article
(This article belongs to the Section Farm Animal Production)
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21 pages, 9087 KB  
Article
An Intelligent Field Monitoring System Based on Enhanced YOLO-RMD Architecture for Real-Time Rice Pest Detection and Management
by Jiangdong Yin, Jun Zhu, Gang Chen, Lihua Jiang, Huanhuan Zhan, Haidong Deng, Yongbing Long, Yubin Lan, Binfang Wu and Haitao Xu
Agriculture 2025, 15(8), 798; https://doi.org/10.3390/agriculture15080798 - 8 Apr 2025
Cited by 11 | Viewed by 2207
Abstract
This study presents a comprehensive solution for precise and timely pest monitoring in field environments through the development of an advanced rice pest detection system based on the YOLO-RMD model. Addressing critical challenges in real-time detection accuracy and environmental adaptability, the proposed system [...] Read more.
This study presents a comprehensive solution for precise and timely pest monitoring in field environments through the development of an advanced rice pest detection system based on the YOLO-RMD model. Addressing critical challenges in real-time detection accuracy and environmental adaptability, the proposed system integrates three innovative components: (1) a novel Receptive Field Attention Convolution module enhancing feature extraction in complex backgrounds; (2) a Mixed Local Channel Attention module balances local and global features to improve detection precision for small targets in dense foliage; (3) an enhanced multi-scale detection architecture incorporating Dynamic Head with an additional detection head, enabling simultaneous improvement in multi-scale pest detection capability and coverage. The experimental results demonstrate a 3% accuracy improvement over YOLOv8n, achieving 98.2% mean Average Precision at 50% across seven common rice pests while maintaining real-time processing capabilities. This integrated solution addresses the dual requirements of precision and timeliness in field monitoring, representing a significant advancement for agricultural vision systems. The developed framework provides practical implementation pathways for precision pest management under real-world farming conditions. Full article
(This article belongs to the Special Issue Smart Agriculture Sensors and Monitoring Systems for Field Detection)
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21 pages, 5607 KB  
Article
Soybean–Corn Seedling Crop Row Detection for Agricultural Autonomous Navigation Based on GD-YOLOv10n-Seg
by Tao Sun, Feixiang Le, Chen Cai, Yongkui Jin, Xinyu Xue and Longfei Cui
Agriculture 2025, 15(7), 796; https://doi.org/10.3390/agriculture15070796 - 7 Apr 2025
Cited by 10 | Viewed by 2040
Abstract
Accurate crop row detection is an important foundation for agricultural machinery to realize autonomous operation. Existing methods often compromise between real-time performance and detection accuracy, limiting their practical field applicability. This study develops a high-precision, efficient crop row detection algorithm specifically optimized for [...] Read more.
Accurate crop row detection is an important foundation for agricultural machinery to realize autonomous operation. Existing methods often compromise between real-time performance and detection accuracy, limiting their practical field applicability. This study develops a high-precision, efficient crop row detection algorithm specifically optimized for soybean–corn compound planting conditions, addressing both computational efficiency and recognition accuracy. In this paper, a real-time soybean–corn crop row detection method based on GD-YOLOv10n-seg with principal component analysis (PCA) fitting was proposed. Firstly, the dataset of soybean–corn seedling crop rows was established, and the images were labeled with line labels. Then, an improved model GD-YOLOv10n-seg model was constructed by integrating GhostModule and DynamicConv into the YOLOv10n-segmentation model. The experimental results showed that the improved model performed better in MPA and MIoU, and the model size was reduced by 18.3%. The crop row center lines of the segmentation results were fitted by PCA, where the fitting accuracy reached 95.08%, the angle deviation was 1.75°, and the overall processing speed was 61.47 FPS. This study can provide an efficient and reliable solution for agricultural autonomous navigation operations such as weeding and pesticide application under a soybean–corn compound planting mode. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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19 pages, 3399 KB  
Article
Comparative Analysis of CNN-Based Semantic Segmentation for Apple Tree Canopy Size Recognition in Automated Variable-Rate Spraying
by Tantan Jin, Su Min Kang, Na Rin Kim, Hye Ryeong Kim and Xiongzhe Han
Agriculture 2025, 15(7), 789; https://doi.org/10.3390/agriculture15070789 - 6 Apr 2025
Cited by 7 | Viewed by 2353
Abstract
Efficient pest control in orchards is crucial for preserving crop quality and maximizing yield. A key factor in optimizing automated variable-rate spraying is accurate tree canopy size estimation, which helps reduce pesticide overuse while minimizing environmental and health risks. This study evaluates the [...] Read more.
Efficient pest control in orchards is crucial for preserving crop quality and maximizing yield. A key factor in optimizing automated variable-rate spraying is accurate tree canopy size estimation, which helps reduce pesticide overuse while minimizing environmental and health risks. This study evaluates the performance of two advanced convolutional neural networks, PP-LiteSeg and fully convolutional networks (FCNs), for segmenting tree canopies of varying sizes—small, medium, and large—using short-term dense-connection networks (STDC1 and STDC2) as backbones. A dataset of 305 field-collected images was used for model training and evaluation. The results show that FCNs with STDC backbones outperform PP-LiteSeg, delivering superior semantic segmentation accuracy and background classification. The STDC1-based model excels in precision variable-rate spraying, achieving an Intersection-over-Union of up to 0.75, Recall of 0.85, and Precision of approximately 0.85. Meanwhile, the STDC2-based model demonstrates greater optimization stability and faster convergence, making it more suitable for resource-constrained environments. Notably, the STDC2-based model significantly enhances canopy-background differentiation, achieving a background classification Recall of 0.9942. In contrast, PP-LiteSeg struggles with small canopy detection, leading to reduced segmentation accuracy. These findings highlight the potential of FCNs with STDC backbones for automated apple tree canopy recognition, advancing precision agriculture and promoting sustainable pesticide application through improved variable-rate spraying strategies. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Orchard Management)
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25 pages, 14345 KB  
Article
Research on an Apple Recognition and Yield Estimation Model Based on the Fusion of Improved YOLOv11 and DeepSORT
by Zhanglei Yan, Yuwei Wu, Wenbo Zhao, Shao Zhang and Xu Li
Agriculture 2025, 15(7), 765; https://doi.org/10.3390/agriculture15070765 - 2 Apr 2025
Cited by 16 | Viewed by 3075
Abstract
Accurate apple yield estimation is essential for effective orchard management, market planning, and ensuring growers’ income. However, complex orchard conditions, such as dense foliage occlusion and overlapping fruits, present challenges to large-scale yield estimation. This study introduces APYOLO, an enhanced apple detection algorithm [...] Read more.
Accurate apple yield estimation is essential for effective orchard management, market planning, and ensuring growers’ income. However, complex orchard conditions, such as dense foliage occlusion and overlapping fruits, present challenges to large-scale yield estimation. This study introduces APYOLO, an enhanced apple detection algorithm based on an improved YOLOv11, integrated with the DeepSORT tracking algorithm to improve both detection accuracy and operational speed. APYOLO incorporates a multi-scale channel attention (MSCA) mechanism and an enhanced multi-scale prior distribution intersection over union (EnMPDIoU) loss function to enhance target localization and recognition under complex environments. Experimental results demonstrate that APYOLO outperforms the original YOLOv11 by improving mAP@0.5, mAP@0.5–0.95, accuracy, and recall by 2.2%, 2.1%, 0.8%, and 2.3%, respectively. Additionally, the combination of a unique ID with the region of line (ROL) strategy in DeepSORT further boosts yield estimation accuracy to 84.45%, surpassing the performance of the unique ID method alone. This study provides a more precise and efficient system for apple yield estimation, offering strong technical support for intelligent and refined orchard management. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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23 pages, 1334 KB  
Review
Research Progress on the Improvement of Farmland Soil Quality by Green Manure
by Yulong Wang, Aizhong Yu, Yongpan Shang, Pengfei Wang, Feng Wang, Bo Yin, Yalong Liu, Dongling Zhang and Qiang Chai
Agriculture 2025, 15(7), 768; https://doi.org/10.3390/agriculture15070768 - 2 Apr 2025
Cited by 14 | Viewed by 4794
Abstract
Long-term intensive agricultural management practices have led to a continuous decline in farmland soil quality, posing a serious threat to food security and agricultural sustainability. Green manure, as a natural, cost-effective, and environmentally friendly cover crop, plays a significant role in enhancing soil [...] Read more.
Long-term intensive agricultural management practices have led to a continuous decline in farmland soil quality, posing a serious threat to food security and agricultural sustainability. Green manure, as a natural, cost-effective, and environmentally friendly cover crop, plays a significant role in enhancing soil quality, ensuring food security, and promoting sustainable agricultural development. The improvement of soil quality by green manure is primarily manifested in the enhancement of soil physical, chemical, and biological properties. Specifically, it increases soil organic matter content, optimizes soil structure, enhances nutrient cycling, and improves microbial community composition and metabolic activity. The integration of green manure with agronomic practices such as intercropping, crop rotation, conservation tillage, reduced fertilizer application, and organic material incorporation demonstrates its potential in addressing agricultural development challenges, particularly through its contributions to soil quality improvement, crop yield stabilization, water and nutrient use efficiency enhancement, fertilizer input reduction, and agricultural greenhouse gas emission mitigation. However, despite substantial evidence from both research and practical applications confirming the benefits of green manure, its large-scale adoption faces numerous challenges, including regional variability in application effectiveness, low farmer acceptance, and insufficient extension technologies. Future research should further clarify the synergistic mechanism between green manure and agronomic measures such as intercropping, crop rotation, conservation tillage, reduced fertilization and organic material return to field. This will help explore the role of green manure in addressing the challenges of soil degradation, climate change and food security, develop green manure varieties adapted to different ecological conditions, and optimize green manure planting and management technologies. Governments should comprehensively promote the implementation of green manure technologies through economic incentives, technology extension, and educational training programs. The integration of scientific research, policy support, and technological innovation is expected to establish green manure as a crucial driving force for facilitating the global transition towards sustainable agriculture. Full article
(This article belongs to the Special Issue Soil Chemical Properties and Soil Conservation in Agriculture)
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20 pages, 3424 KB  
Review
How Can Farmers’ Green Production Behavior Be Promoted? A Literature Review of Drivers and Incentives for Behavioral Change
by Dalin Zhang, Feng Dong, Zhicheng Li and Sulan Xu
Agriculture 2025, 15(7), 744; https://doi.org/10.3390/agriculture15070744 - 31 Mar 2025
Cited by 14 | Viewed by 2432
Abstract
The promotion of farmers’ green production behavior (GPB) to accelerate agricultural green development and food system transformation is a popular issue worldwide. Based on the representative literature from 2015 to October 2024, this study reviews the connotation and stage characteristics of farmers’ GPB. [...] Read more.
The promotion of farmers’ green production behavior (GPB) to accelerate agricultural green development and food system transformation is a popular issue worldwide. Based on the representative literature from 2015 to October 2024, this study reviews the connotation and stage characteristics of farmers’ GPB. The current research focuses primarily on the primary industry, particularly agriculture, which is not in line with the global trend of agricultural and rural development; thus, it seems necessary to reiterate the connotation. The driving factors of farmers’ GPB are discussed at the individual, household, and external levels, and the relationships and effects of each group of factors in the literature are reviewed; future research should re-examine the formation mechanism from the perspective of industry integration and upgrading. This paper refers to the agricultural transformation practices of major economies worldwide and summarizes the policy implications in the literature concerning the promotion of farmers’ GPB. A multiagent incentive mechanism system is constructed from the perspectives of government-led, market-oriented, and social participation. Finally, based on the evolving trends in global agriculture and rural development, three potential research directions are proposed as follows: (i) broadening the research scope of farmers’ GPB from the perspective of industry integration; (ii) empowering farmers’ GPB through digital intelligence; and (iii) increasing farmers’ GPB and food security. This review is beneficial for better understanding farmers’ GPB and promoting it globally. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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21 pages, 1672 KB  
Article
Soil Fertility and Plant Growth Enhancement Through Compost Treatments Under Varied Irrigation Conditions
by Subanky Suvendran, Miguel F. Acevedo, Breana Smithers, Stephanie J. Walker and Pei Xu
Agriculture 2025, 15(7), 734; https://doi.org/10.3390/agriculture15070734 - 28 Mar 2025
Cited by 24 | Viewed by 9964
Abstract
Global challenges such as soil degradation and water scarcity necessitate sustainable agricultural practices, particularly in regions where saline water is increasingly used for irrigation. This study investigates the effects of four compost treatments, including surface-applied mulch compost (MC), Johnson–Su biologically active compost incorporated [...] Read more.
Global challenges such as soil degradation and water scarcity necessitate sustainable agricultural practices, particularly in regions where saline water is increasingly used for irrigation. This study investigates the effects of four compost treatments, including surface-applied mulch compost (MC), Johnson–Su biologically active compost incorporated into soil (JCI), mulch compost incorporated into soil (MCI), and no compost as control (NC), on soil fertility, microbial activity, and Capsicum annuum (chili pepper) growth. Greenhouse experiments were conducted using soil from two different sites (New Mexico State University’s (NMSU) agricultural research plots and agricultural field-testing site at the Brackish Groundwater National Desalination Research Facility (BGNDRF) in Alamogordo, New Mexico) and two irrigation water salinities (brackish at ~3000 µS/cm and agricultural at ~800 µS/cm). The Johnson–Su compost treatment demonstrated superior performance, due to its high soil organic matter (41.5%), nitrate (NO3−) content (82.5 mg/kg), and phosphorus availability (193.1 mg/kg). In the JCI-treated soils, microbial biomass increased by 40%, and total microbial carbon reached 64.69 g/m2 as compared to 64.7 g/m2 in the NC. Plant growth parameters, including chlorophyll content, root length, and wet biomass, improved substantially with JCI. For instance, JCI increased plant height by 20% and wet biomass by 30% compared to NC treatments. The JCI treatment also effectively mitigated soil salinity, reducing Na+ accumulation by 60% and Cl− by 70% while enhancing water retention and soil structure. Principal Component Analysis (PCA) revealed a distinct clustering of JCI treatments, demonstrating its ability to increase nutrient retention and minimize salinity stress. These results indicate that biologically active properties, such as fungi-rich compost, are critical to providing an effective, environmentally resilient approach for enhancing soil fertility and supporting sustainable crop production under brackish groundwater irrigation, particularly in regions facing freshwater scarcity. Full article
(This article belongs to the Section Agricultural Soils)
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