Agriculture

24 pages, 34370 KB

Open AccessArticle

A Semi-Automatic and Visual Leaf Area Measurement System Integrating Hough Transform and Gaussian Level-Set Method

by Linjuan Wang, Chengyi Hao, Xiaoying Zhang, Wenfeng Guo, Zhifang Bi, Zhaoqing Lan, Lili Zhang and Yuanhuai Han

Agriculture 2025, 15(19), 2101; https://doi.org/10.3390/agriculture15192101 - 9 Oct 2025

Viewed by 154

Abstract

Accurate leaf area measurement is essential for plant growth monitoring and ecological research; however, it is often challenged by perspective distortion and color inconsistencies resulting from variations in shooting conditions and plant status. To address these issues, this study proposes a visual and [...] Read more.

Accurate leaf area measurement is essential for plant growth monitoring and ecological research; however, it is often challenged by perspective distortion and color inconsistencies resulting from variations in shooting conditions and plant status. To address these issues, this study proposes a visual and semi-automatic measurement system. The system utilizes Hough transform-based perspective transformation to correct perspective distortions and incorporates manually sampled points to obtain prior color information, effectively mitigating color inconsistency. Based on this prior knowledge, the level-set function is automatically initialized. The leaf extraction is achieved through level-set curve evolution that minimizes an energy function derived from a multivariate Gaussian distribution model, and the evolution process allows visual monitoring of the leaf extraction progress. Experimental results demonstrate robust performance under diverse conditions: the standard deviation remains below 1 cm², the relative error is under 1%, the coefficient of variation is less than 3%, and processing time is under 10 s for most images. Compared to the traditional labor-intensive and time-consuming manual photocopy-weighing approach, as well as OpenPheno (which lacks parameter adjustability) and ImageJ 1.54g (whose results are highly operator-dependent), the proposed system provides a more flexible, controllable, and robust semi-automatic solution. It significantly reduces operational barriers while enhancing measurement stability, demonstrating considerable practical application value. Full article

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

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45 pages, 13482 KB

Open AccessReview

Evaluating the Sustainability of Emerging Extraction Technologies for Valorization of Food Waste: Microwave, Ultrasound, Enzyme-Assisted, and Supercritical Fluid Extraction

by Elixabet Díaz-de-Cerio and Esther Trigueros

Agriculture 2025, 15(19), 2100; https://doi.org/10.3390/agriculture15192100 - 9 Oct 2025

Viewed by 172

Abstract

Food industry generates substantial waste, raising economic and environmental concerns. Green Chemistry (GC) highlights the extraction of nutritional and bioactive compounds as a key strategy for waste valorization, driving interest in sustainable methods to recover valuable compounds efficiently. This review evaluates the sustainability [...] Read more.

Food industry generates substantial waste, raising economic and environmental concerns. Green Chemistry (GC) highlights the extraction of nutritional and bioactive compounds as a key strategy for waste valorization, driving interest in sustainable methods to recover valuable compounds efficiently. This review evaluates the sustainability of widely used emerging extraction technologies—Microwave-, Ultrasound- and Enzyme-Assisted, as well as Supercritical Fluid Extraction—and their alignment with GC principles for agri-food waste valorization. It first outlines the principles, key parameters, and main advantages and limitations of each technique. Subsequently, sustainability is then assessed in selected studies using the Analytical GREEnness Metric Approach (AGREEprep). By calculating the greenness score (GS), this metric quantifies the adherence of extraction processes to sustainability standards. The analysis reveals variations within the same extraction method, influenced by solvent choice and operating conditions, as well as differences across the techniques, highlighting the importance of process design in achieving green and efficient valorization. Full article

(This article belongs to the Special Issue Innovative Approaches to Circular Agriculture: Incorporating Green Chemistry and Waste Valorization)

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15 pages, 3812 KB

Open AccessArticle

Comparative Analysis of Static Rollover Stability Between Conventional and Electric Tractor

by Juhee Lee, Seokho Kang, Yujin Han, Jinho Son and Yushin Ha

Agriculture 2025, 15(19), 2099; https://doi.org/10.3390/agriculture15192099 - 9 Oct 2025

Viewed by 177

Abstract

As the development of electric tractors progresses, battery systems have become a key component, accounting for a significant portion of the vehicle’s total weight. With rollover accidents remaining a leading cause of fatal injuries in agricultural machinery, the stability of electric tractors is [...] Read more.

As the development of electric tractors progresses, battery systems have become a key component, accounting for a significant portion of the vehicle’s total weight. With rollover accidents remaining a leading cause of fatal injuries in agricultural machinery, the stability of electric tractors is drawing increasing attention. In particular, battery placement may critically affect the overall mass distribution and rollover behavior, highlighting the need for safety-focused design optimization. This study evaluates the static rollover stability of a 55 kW electric tractor by analyzing the effect of battery mounting position and comparing it with a conventional tractor. Three tractor models were considered: an electric tractor with a front-mounted battery, one with a center-mounted battery, and a conventional tractor. Multibody dynamic simulations were conducted using RecurDyn, and a total of 24 orientations, at 15° intervals, were simulated to determine the tipping angles in all directions. The results revealed that battery placement had a significant impact on rollover stability. The front-mounted battery type exhibited up to 30% higher tipping angles than the conventional tractor in the forward pitch direction near 90°, indicating improved stability. In contrast, the center-mounted battery type showed a tipping angle distribution generally similar to that of the conventional tractor, with smaller variations across directions. These findings demonstrate the influence of mass distribution on rollover safety and provide valuable insight for structural design of electric tractors. Full article

(This article belongs to the Section Agricultural Technology)

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23 pages, 9068 KB

Open AccessArticle

Stratified Nitrogen Application Enhances Subsoil Carbon Sequestration via Enzyme-Mediated Pathways in Straw-Incorporated Croplands of North China Plain

by Bin Wang, Yanqun Wang, Jingyu Li, Rui Hou, Yulong Liu, Xin Fu, Jie Men, Yingchun Li and Zhengping Peng

Agriculture 2025, 15(19), 2098; https://doi.org/10.3390/agriculture15192098 - 9 Oct 2025

Viewed by 101

Abstract

Nitrogen (N) fertilization critically regulates the storage and availability of soil carbon (C) and N pools. However, the internal mechanism through which stratified N application affects soil organic carbon (SOC) sequestration and soil quality index (SQI) remains unclear. To investigate the effects of [...] Read more.

Nitrogen (N) fertilization critically regulates the storage and availability of soil carbon (C) and N pools. However, the internal mechanism through which stratified N application affects soil organic carbon (SOC) sequestration and soil quality index (SQI) remains unclear. To investigate the effects of stratified N application on C sequestration and SQI in both topsoil and subsoil, this study established six treatments (N_0:0, N_1:0, N_4:1, N_3:2, N_2:3, N_1:4) and analyzed soil biochemical indicators. The results showed that compared to N_1:0, stratified N fertilization did not significantly improve soil C and N content in the 0–20 cm layer. In contrast, the N_2:3 and N_1:4 treatments even led to a significant reduction in soil C and N pools in the topsoil. In the 20–40 cm, compared to N_1:0, stratified N fertilization increased SOC, TN, labile C fractions, N fractions (particulate organic N and microbial biomass N), enzyme activity and C pool management index (CPMI), increasing by 0.52–7.94%, 2.05–8.42%, 4.77–42.59%, 14.46–56.01%, 6.34–45.82%, and 31.26–51.93%, respectively. In 0–20 cm, compared to N_0:0, N application increased SQI by 24.84–45.77%, and N_2:3 and N_1:4 treatments were lower SQI than N_1:0. Furthermore, N_2:3, N_3:2, and N_1:4 treatments in 20–40 cm were higher than other treatments. N fertilizer application drives the synergistic changes in C and N fractions by regulating enzyme activity and stoichiometric ratio, thus affecting CPMI and SQI. Thus, the 3:2 stratified N fertilization (0–20 cm:20–40 cm) method achieves synergistic dual-layer enhancement-maintaining surface C and N pools while boosting subsoil C sequestration and quality-through enzyme-mediated precision regulation of C/N stoichiometry. The study provides a scientific foundation for integrated C emission reduction and cropland quality enhancement in the North China. Full article

(This article belongs to the Section Agricultural Soils)

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21 pages, 5164 KB

Open AccessArticle

Effects of Different Operation Years of Photovoltaic Power Stations on Vegetation and Soil Characteristics in Temperate Deserts

by Yaoxin Yu, Tao Chen, Shijun Ma, Ya Tian, Qing Li, Zhaoshan Cai, Lijun Zhao, Xiaoni Liu, Jianhua Xiao and Yafei Shi

Agriculture 2025, 15(19), 2097; https://doi.org/10.3390/agriculture15192097 - 9 Oct 2025

Viewed by 124

Abstract

The rapid expansion of photovoltaic installations in arid and semi-arid regions has altered regional water–heat regimes, triggering complex responses in vegetation recovery and soil processes. However, systematic assessments of ecological restoration under varying operational durations and microenvironmental interactions remain insufficient. Therefore, this study [...] Read more.

The rapid expansion of photovoltaic installations in arid and semi-arid regions has altered regional water–heat regimes, triggering complex responses in vegetation recovery and soil processes. However, systematic assessments of ecological restoration under varying operational durations and microenvironmental interactions remain insufficient. Therefore, this study examines photovoltaic power stations operating for 1, 7, and 13 years within China’s temperate desert regions, alongside undeveloped control areas, to compare differences across four microenvironments: the front eave of photovoltaic panels (FP), underneath photovoltaic panels (UP), back eave of photovoltaic panels (BP), and interval between photovoltaic panels (IP). Combining analysis of variance, correlation analysis, variance partitioning analysis (VPA), and generalised additive models (GAMs), the study evaluates the coupling mechanisms between vegetation and soil. The results indicate that operational duration significantly enhances vegetation cover, biomass, and species diversity, with the 13 year operational zone demonstrating optimal restoration outcomes. Microenvironmental variations were pronounced, with vegetation and soil quality in the front eave zone surpassing other areas, while the inter-panel zone exhibited the weakest recovery. Key soil factors shifted with recovery stages: early-stage vegetation showed heightened sensitivity to soil water content (SWC), whereas later stages relied more heavily on soil organic matter (SOM) and nutrient supply. Variation Partial Analysis (VPA) revealed that soil factors in the 13 year operational zone accounted for 71.9% of the variation in vegetation cover. The operational lifespan of photovoltaic power stations, microenvironmental variations, and key soil factors collectively drive the restoration of thermophilic desert vegetation. This research reveals phased regulatory mechanisms during the restoration process, providing scientific grounds for optimising photovoltaic layouts and enhancing desert ecosystem stability. Full article

(This article belongs to the Section Agricultural Systems and Management)

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10 pages, 399 KB

Open AccessArticle

Potential of Native Entomopathogenic Nematodes (Steinernematidae) as Biological Control Agents of Tetranychus urticae Koch

by Dorota Tumialis, Lidia Florczak, Julia Dylewska, Magdalena Jakubowska, Jolanta Kowalska and Anna Mazurkiewicz

Agriculture 2025, 15(19), 2096; https://doi.org/10.3390/agriculture15192096 - 9 Oct 2025

Viewed by 174

Abstract

The two-spotted spider mite (Tetranychus urticae Koch) (Acari: Tetranychidae) is one of the most widespread and destructive phytophagous mite species, occurring across all climatic zones worldwide. Currently, the control of spider mites in crop protection relies primarily on chemical acaricides. However, the [...] Read more.

The two-spotted spider mite (Tetranychus urticae Koch) (Acari: Tetranychidae) is one of the most widespread and destructive phytophagous mite species, occurring across all climatic zones worldwide. Currently, the control of spider mites in crop protection relies primarily on chemical acaricides. However, the selection of resistant populations to their active ingredients is reducing their efficacy. The aim of the present study was to assess the susceptibility of T. urticae to a native isolate of entomopathogenic nematodes, Steinernema feltiae Filipjev ZWO21, under laboratory conditions. The experiment was conducted using Petri dishes, each containing 22–28 adult T. urticae. Infective juveniles (IJs) of the nematodes were then applied at a dose of 8000 IJs per dish (±300 IJs per mite). Petri dishes with mites treated with nematodes were placed in a Sanyo incubation chamber at 25 °C and 60% relative humidity. After three days, dead mites were collected from the Petri dishes and dissected, and mortality was subsequently determined. The present study confirmed that the S. feltiae ZWO21 isolate exhibited considerable potential for the biological control of T. urticae, causing 37.5–83.3% (mean 57.0%) mortality in this pest species. Although this result indicates a moderate efficacy when nematodes are applied alone, it also underscores the relevance of further research into their integration with other control strategies, including acaricides, within integrated pest management (IPM) programmes. Full article

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

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22 pages, 29892 KB

Open AccessArticle

Lightweight Deep Learning for Real-Time Cotton Monitoring: UAV-Based Defoliation and Boll-Opening Rate Assessment

by Minghui Xia, Xuegeng Chen, Xinliang Tian, Haojun Wen, Yan Zhao, Hongxia Liu, Wei Liu and Yuchen Zheng

Agriculture 2025, 15(19), 2095; https://doi.org/10.3390/agriculture15192095 - 8 Oct 2025

Viewed by 237

Abstract

Unmanned aerial vehicle (UAV) imagery provides an efficient approach for monitoring cotton defoliation and boll-opening rates. Deep learning, particularly convolutional neural networks (CNNs), has been widely applied in image processing and agricultural monitoring, achieving strong performance in tasks such as disease detection, weed [...] Read more.

Unmanned aerial vehicle (UAV) imagery provides an efficient approach for monitoring cotton defoliation and boll-opening rates. Deep learning, particularly convolutional neural networks (CNNs), has been widely applied in image processing and agricultural monitoring, achieving strong performance in tasks such as disease detection, weed recognition, and yield prediction. However, existing models often suffer from heavy computational costs and slow inference speed, limiting their real-time deployment in agricultural fields. To address this challenge, we propose a lightweight cotton maturity recognition model, RTCMNet (Real-time Cotton Monitoring Network). By incorporating a multi-scale convolutional attention (MSCA) module and an efficient feature fusion strategy, RTCMNet achieves high accuracy with substantially reduced computational complexity. A UAV dataset was constructed using images collected in Xinjiang, and the proposed model was benchmarked against several state-of-the-art networks. Experimental results demonstrate that RTCMNet achieves 0.96 and 0.92 accuracy on defoliation rate and boll-opening rate classification tasks, respectively. Meanwhile, it contains only 0.35 M parameters—94% fewer than DenseNet121—and only requires an inference time of 33 ms, representing a 97% reduction compared to DenseNet121. Field tests further confirm its real-time performance and robustness on UAV platforms. Overall, RTCMNet provides an efficient and low-cost solution for UAV-based cotton maturity monitoring, supporting the advancement of precision agriculture. Full article

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

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28 pages, 7904 KB

Open AccessArticle

Optimising Rice Straw Bale Quality Through Vibration-Assisted Compression

by Fudong Xu, Wenlong Xu, Changsu Xu, Jinwu Wang and Han Tang

Agriculture 2025, 15(19), 2094; https://doi.org/10.3390/agriculture15192094 - 8 Oct 2025

Viewed by 199

Abstract

This study focuses on enhancing the comprehensive utilisation of rice straw by proposing a vibration-assisted compression technology, with the aim of resolving inherent issues in traditional baling, such as uneven compression and low density. This study designed a multi-point vibration-assisted compression test rig [...] Read more.

This study focuses on enhancing the comprehensive utilisation of rice straw by proposing a vibration-assisted compression technology, with the aim of resolving inherent issues in traditional baling, such as uneven compression and low density. This study designed a multi-point vibration-assisted compression test rig and established a vibration-enhanced compression mechanical model based on the physical properties of rice straw. By integrating discrete element method (DEM) simulations with bench testing, the optimal length-to-width ratio of 1:1 was identified for achieving superior compaction quality. A systematic analysis was conducted to evaluate the effects of vibration point configuration, frequency, and amplitude control on straw bale integrity. The results of the DEM simulations demonstrated that vibration-assisted compression significantly enhanced the compaction uniformity and stability of rice straw. The dimensional stability coefficient and pressure transmission rates of the straw bales reached 88.25% and 58.04%, respectively, validating the efficacy of the vibration-assisted compression technique. This study provides innovative concepts and theoretical foundations for optimising the design of straw baling and in-field collection equipment. It holds critical significance for advancing the resource-efficient utilisation of agricultural residues and promoting sustainable agricultural practices. Full article

(This article belongs to the Section Agricultural Technology)

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15 pages, 878 KB

Open AccessArticle

The Effect of Native Strain-Based Biofertilizer with TiO₂, ZnO, Fe_xO_x, and Ag NPs on Wheat Yield (Triticum durum Desf.)

by Andrés Torres-Gómez, Cesar R. Sarabia-Castillo, René Juárez-Altamirano and Fabián Fernández-Luqueño

Agriculture 2025, 15(19), 2093; https://doi.org/10.3390/agriculture15192093 - 8 Oct 2025

Viewed by 225

Abstract

This study evaluated the effects of applying a biofertilizer, alone and in combination with nanoparticles (NPs), under controlled greenhouse conditions to improve soil quality and wheat performance (soil from the region of General Cepeda, Coahuila, Mexico, was used). The integration of the biofertilizer [...] Read more.

This study evaluated the effects of applying a biofertilizer, alone and in combination with nanoparticles (NPs), under controlled greenhouse conditions to improve soil quality and wheat performance (soil from the region of General Cepeda, Coahuila, Mexico, was used). The integration of the biofertilizer with Fe_xO_x NPs proved particularly effective in enhancing soil physical and biological parameters as well as promoting superior crop growth compared with individual treatments. The incorporation of NPs markedly improved the biofertilizer’s biocompatibility and stability, reinforcing its potential for optimizing plant nutrition, nutrient use efficiency, and overall agricultural sustainability. In addition, the combined treatments enhanced the utilization of native microbial diversity, thereby contributing to increased soil fertility and the quality and yield of crops in the study region. The best yield obtained in previous harvests (8.3 Mg ha⁻¹) was improved to 8.48 Mg ha⁻¹ with application of the biofertilizer with Fe_xO_x NPs. Moreover, shoot length increased significantly with the combination of the biofertilizer and ZnO NPs as well as with Fe_xO_x NPs separately, whereas root length was maximized with the addition of the biofertilizer alone. These findings underscore the synergistic effects of combining biofertilizers with metal-based nanoparticles to sustainably enhance wheat growth and productivity. Full article

(This article belongs to the Special Issue Effects of Engineered Nanomaterials on Soil Health and Plant Growth)

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24 pages, 6407 KB

Open AccessArticle

Lightweight SCC-YOLO for Winter Jujube Detection and 3D Localization with Cross-Platform Deployment Evaluation

by Meng Zhou, Yaohua Hu, Anxiang Huang, Yiwen Chen, Xing Tong, Mengfei Liu and Yunxiao Pan

Agriculture 2025, 15(19), 2092; https://doi.org/10.3390/agriculture15192092 - 8 Oct 2025

Viewed by 170

Abstract

Harvesting winter jujubes is a key step in production, yet traditional manual approaches are labor-intensive and inefficient. To overcome these challenges, we propose SCC-YOLO, a lightweight method for winter jujube detection, 3D localization, and cross-platform deployment, aiming to support intelligent harvesting. In this [...] Read more.

Harvesting winter jujubes is a key step in production, yet traditional manual approaches are labor-intensive and inefficient. To overcome these challenges, we propose SCC-YOLO, a lightweight method for winter jujube detection, 3D localization, and cross-platform deployment, aiming to support intelligent harvesting. In this study, RGB-D cameras were integrated with an improved YOLOv11 network optimized by ShuffleNetV2, CBAM, and a redesigned C2f_WTConv module, which enables joint spatial–frequency feature modeling and enhances small-object detection in complex orchard conditions. The model was trained on a diversified dataset with extensive augmentation to ensure robustness. In addition, the original localization loss was replaced with DIoU to improve bounding box regression accuracy. A robotic harvesting system was developed, and an Eye-to-Hand calibration-based 3D localization pipeline was implemented to map fruit coordinates to the robot workspace for accurate picking. To validate engineering applicability, the SCC-YOLO model was deployed on both desktop (PyTorch and ONNX Runtime) and mobile (NCNN with Vulkan+FP16) platforms, and FPS, latency, and stability were comparatively analyzed. Experimental results showed that SCC-YOLO improved mAP by 5.6% over YOLOv11, significantly enhanced detection precision and robustness, and achieved real-time performance on mobile devices while maintaining peak throughput on high-performance desktops. Field and laboratory tests confirmed the system’s effectiveness for detection, localization, and harvesting efficiency, demonstrating its adaptability to diverse deployment environments and its potential for broader agricultural applications. Full article

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

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16 pages, 4875 KB

Open AccessArticle

ZjBZR2, a BES/BZR Transcription Factor from Zoysia japonica, Positively Regulates Leaf Angle and Osmotic Stress Tolerance in Rice

by Qianqian Zuo, Jingjin Yu, Qiuguo Li, Tian Hao and Zhimin Yang

Agriculture 2025, 15(19), 2091; https://doi.org/10.3390/agriculture15192091 - 8 Oct 2025

Viewed by 204

Abstract

The BRI1-EMS suppressor/Brassinazole-resistant (BES/BZR) transcription factors (TFs) act as regulators of the Brassinosteroid (BR) signaling pathway and play key roles in modulating plant growth, development, and abiotic stress tolerance. However, the function of BES/BZR TFs remains unknown in warm-season turfgrass species. In this [...] Read more.

The BRI1-EMS suppressor/Brassinazole-resistant (BES/BZR) transcription factors (TFs) act as regulators of the Brassinosteroid (BR) signaling pathway and play key roles in modulating plant growth, development, and abiotic stress tolerance. However, the function of BES/BZR TFs remains unknown in warm-season turfgrass species. In this study, ZjBZR2, a BES/BZR TF in Zoysia japonica was identified and shared the closest evolutionary relationship with OsBZR2 from Oryza sativa. ZjBZR2 was a nuclear-localized protein and had transcriptional activation activity. ZjBZR2 was predominantly expressed in roots, stems, and lamina joints, and could be significantly induced by BR treatment and osmotic stresses including PEG and salinity. ZjBZR2-overexpressing rice lines increased leaf angle compared with wild-type plants. Furthermore, overexpression of ZjBZR2 enhanced osmotic stress (PEG and salt) tolerance which is associated with the upregulation of stress-responsive and ROS-scavenging genes. These findings provide the first functional characterization of ZjBZR2 in rice and offer excellent genetic resources for the improvement of turfgrass cultivars. Full article

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

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21 pages, 18237 KB

Open AccessArticle

Monitoring of Farmland Abandonment Based on Google Earth Engine and Interpretable Machine Learning

by Yameng Jiang, Yefeng Jiang, Xi Guo, Zichun Guo, Yingcong Ye, Ji Huang and Jia Liu

Agriculture 2025, 15(19), 2090; https://doi.org/10.3390/agriculture15192090 - 8 Oct 2025

Viewed by 297

Abstract

In recent years, China’s hilly and mountainous areas have faced widespread farmland abandonment. However, research on farmland abandonment and its driving mechanisms in hilly and mountainous regions is limited. This study proposes a transferable methodological framework that integrates Landsat data, Google Earth Engine, [...] Read more.

In recent years, China’s hilly and mountainous areas have faced widespread farmland abandonment. However, research on farmland abandonment and its driving mechanisms in hilly and mountainous regions is limited. This study proposes a transferable methodological framework that integrates Landsat data, Google Earth Engine, a time sliding-window algorithm, and the interpretable XGBoost–Shapley Additive explanation (SHAP) model. The time sliding-window algorithm is used to robustly detect long-term land cover changes across the entire study period. The SHAP quantifies the contributions of key drivers to farmland abandonment, providing transparent insights into the driving mechanisms. Applying this framework, we systematically analyzed the spatiotemporal evolution patterns and driving factors of farmland abandonment in Ji’an City, a typical city located in the hilly and mountainous areas of southern China and ultimately developed a farmland abandonment probability distribution map. The findings demonstrate the following. (1) Methodological validation showed that the random forest classifier achieved a mean overall accuracy (OA) of 91.05% (Kappa = 0.88) and the abandonment maps achieved OA of 91.58% (Kappa = 0.83). (2) Spatiotemporal analysis revealed that farmland area increased by 13.26% over 1990–2023, evolving through three stages: fluctuation (1990–2005), growth (2006–2015), and stability (2016–2023). The abandonment rate showed a long-term decreasing trend, peaking in 1998, whereas the abandoned area reached its minimum in 2007. From a spatial perspective, abandonment was more pronounced in mountainous and hilly regions of the study areas. (3) The XGBoost–SHAP model (R² > 0.85) identified key driving factors, including the potential crop yield, soil properties, mean annual precipitation, population density, and terrain features. By offering an interpretable and transferable monitoring framework, this study not only advances farmland abandonment research in complex terrains but also provides concrete policy implications. The results can guide targeted protection of high-risk abandonment zones, promote sustainable land-use planning, and support adaptive agricultural policies in hilly and mountainous regions. Full article

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

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22 pages, 2452 KB

Open AccessArticle

A Farm-Scale Water Balance Assessment of Various Rice Irrigation Strategies Using a Bucket-Model Approach in Spain

by Sílvia Cufí, Gerard Arbat, Jaume Pinsach, Blanca Cuadrado-Alarcón, Arianna Facchi, Josep M. Villar, Farida Dechmi and Francisco Ramírez de Cartagena

Agriculture 2025, 15(19), 2089; https://doi.org/10.3390/agriculture15192089 - 7 Oct 2025

Viewed by 265

Abstract

Making effective decisions about scaling up on-farm irrigation practices to the district level requires a comprehensive assessment of irrigation management at the farm level. In this context, a bucket-type water mass balance model was developed, calibrated, and validated over five irrigation seasons on [...] Read more.

Making effective decisions about scaling up on-farm irrigation practices to the district level requires a comprehensive assessment of irrigation management at the farm level. In this context, a bucket-type water mass balance model was developed, calibrated, and validated over five irrigation seasons on a 121-hectare rice farm located in the lower Ter River valley (north-east Spain), to assess the water use efficiency and the impact of different irrigation practices on water savings. The model was implemented considering the spatial variability of the soils within the farm. It showed a satisfactory performance in both the calibration (2020, 2021, 2022) and validation (2023, 2024) cropping seasons, with NSE values greater than 0.50, PBIAS lower than ±20%, and RSR lower than 0.70. After model validation, the simulation of alternative water management practices revealed that the 10-day fixed-turn irrigation reduced irrigation water use by 30% compared to the traditional water management, although it may negatively impact rice yield. Simulations of an early irrigation cut-off at the end of the season and dry seeding with delayed flooding accounted for 17% and 15% irrigation water savings, respectively. The implementation of the no-runoff practice only accounted for a 6% reduction in water use. The water-saving potential of the simulated strategies was mainly driven by shortening the flooded period of rice paddies, thus demonstrating that managing the ponding water level is critical to diminishing water use in rice irrigation. Full article

(This article belongs to the Special Issue Modeling as a Tool to Explore Sustainable Irrigation Practices in Agriculture)

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21 pages, 5895 KB

Open AccessArticle

Intelligent 3D Potato Cutting Simulation System Based on Multi-View Images and Point Cloud Fusion

by Ruize Xu, Chen Chen, Fanyi Liu and Shouyong Xie

Agriculture 2025, 15(19), 2088; https://doi.org/10.3390/agriculture15192088 - 7 Oct 2025

Viewed by 238

Abstract

The quality of seed pieces is crucial for potato planting. Each seed piece should contain viable potato eyes and maintain a uniform size for mechanized planting. However, existing intelligent methods are limited by a single view, making it difficult to satisfy both requirements [...] Read more.

The quality of seed pieces is crucial for potato planting. Each seed piece should contain viable potato eyes and maintain a uniform size for mechanized planting. However, existing intelligent methods are limited by a single view, making it difficult to satisfy both requirements simultaneously. To address this problem, we present an intelligent 3D potato cutting simulation system. A sparse 3D point cloud of the potato is reconstructed from multi-perspective images, which are acquired with a single-camera rotating platform. Subsequently, the 2D positions of potato eyes in each image are detected using deep learning, from which their 3D positions are mapped via back-projection and a clustering algorithm. Finally, the cutting paths are optimized by a Bayesian optimizer, which incorporates both the potato’s volume and the locations of its eyes, and generates cutting schemes suitable for different potato size categories. Experimental results showed that the system achieved a mean absolute percentage error of 2.16% (95% CI: 1.60–2.73%) for potato volume estimation, a potato eye detection precision of 98%, and a recall of 94%. The optimized cutting plans showed a volume coefficient of variation below 0.10 and avoided damage to the detected potato eyes, producing seed pieces that each contained potato eyes. This work demonstrates that the system can effectively utilize the detected potato eye information to obtain seed pieces containing potato eyes and having uniform size. The proposed system provides a feasible pathway for high-precision automated seed potato cutting. Full article

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

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21 pages, 6386 KB

Open AccessArticle

SPMF-YOLO-Tracker: A Method for Quantifying Individual Activity Levels and Assessing Health in Newborn Piglets

by Jingge Wei, Yurong Tang, Jinxin Chen, Kelin Wang, Peng Li, Mingxia Shen and Longshen Liu

Agriculture 2025, 15(19), 2087; https://doi.org/10.3390/agriculture15192087 - 7 Oct 2025

Viewed by 188

Abstract

This study proposes a behavioral monitoring framework for newborn piglets based on SPMF-YOLO object detection and ByteTrack multi-object tracking, which enables precise quantification of early postnatal activity levels and health assessment. The method enhances small-object detection performance by incorporating the SPDConv module, the [...] Read more.

This study proposes a behavioral monitoring framework for newborn piglets based on SPMF-YOLO object detection and ByteTrack multi-object tracking, which enables precise quantification of early postnatal activity levels and health assessment. The method enhances small-object detection performance by incorporating the SPDConv module, the MFM module, and the NWD loss function into YOLOv11. When combined with the ByteTrack algorithm, it achieves stable tracking and maintains trajectory continuity for multiple targets. An annotated dataset containing both detection and tracking labels was constructed using video data from 10 piglet pens for evaluation. Experimental results indicate that SPMF-YOLO achieved a recognition accuracy rate of 95.3% for newborn piglets. When integrated with ByteTrack, it achieves 79.1% HOTA, 92.2% MOTA, and 84.7% IDF1 in multi-object tracking tasks, thereby outperforming existing methods. Building upon this foundation, this study further quantified the cumulative movement distance of each newborn piglet within 30 min after birth and proposed a health-assessment method based on statistical thresholds. The results demonstrated an overall consistency rate of 98.2% across pens and an accuracy rate of 92.9% for identifying abnormal individuals. The results validated the effectiveness of this method for quantifying individual behavior and assessing health status in newborn piglets within complex farming environments, providing a feasible technical pathway and scientific basis for health management and early intervention in precision animal husbandry. Full article

(This article belongs to the Special Issue Modeling of Livestock Breeding Environment and Animal Behavior)

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17 pages, 5729 KB

Open AccessArticle

Nitrogen and Potassium Fertilization Modulate Dalbulus maidis (HEMIPTERA: CICADELLIDAE) Abundance and Corn Stunt Disease Severity

by Ademar Novais Istchuk, Matheus Henrique Schwertner, Matheus Luis Ferrari, Luiz Henrique Marques and Vanda Pietrowski

Agriculture 2025, 15(19), 2086; https://doi.org/10.3390/agriculture15192086 - 7 Oct 2025

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Abstract

Corn stunt complex, transmitted by the corn leafhopper (Dalbulus maidis), poses significant yield risks to corn production. This study evaluated the effects of two corn hybrids and top-dressed nitrogen (N) and potassium (K) fertilization on D. maidis incidence and corn stunt [...] Read more.

Corn stunt complex, transmitted by the corn leafhopper (Dalbulus maidis), poses significant yield risks to corn production. This study evaluated the effects of two corn hybrids and top-dressed nitrogen (N) and potassium (K) fertilization on D. maidis incidence and corn stunt symptom expression under field conditions. Eighteen treatments were tested in a randomized complete block design with six replications over two seasons. Leafhopper populations were monitored using yellow sticky traps, and symptom incidence and severity were assessed at R₁ and R₃ stages, respectively. While D. maidis populations varied substantially between seasons, neither N nor K fertilization, nor hybrid selection, significantly affected vector abundance. Importantly, symptom frequency and severity were not directly proportional to leafhopper density. Top-dressed fertilization, particularly with K, reduced the visual expression of corn stunt symptoms although it did not prevent infection. Hybrid responses to fertilization varied, with a genotype exhibiting greater symptom mitigation. Grain yield was not significantly influenced by nutrient rates or hybrid choice. These findings suggest that balanced N and K fertilization enhances crop resilience to corn stunt disease without directly suppressing vector populations. Integrating nutritional management with hybrid selection presents a promising strategy to add in corn stunt control and deepens our understanding of the environmental factors that mitigate severe symptoms. Full article

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

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13 pages, 1554 KB

Open AccessArticle

Quantification and Optimization of Straight-Line Attitude Control for Orchard Weeding Robots Using Adaptive Pure Pursuit

by Weidong Jia, Zhenlei Zhang, Xiang Dong, Mingxiong Ou, Ronghua Gao, Yunfei Wang, Qizhi Yang and Xiaowen Wang

Agriculture 2025, 15(19), 2085; https://doi.org/10.3390/agriculture15192085 - 7 Oct 2025

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Abstract

In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes [...] Read more.

In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes an adaptive lookahead Pure Pursuit method incorporating angular velocity feedback. By dynamically adjusting the lookahead distance according to real-time attitude changes, the method enhances coordination between path curvature and robot stability. To enable systematic evaluation, three time-series-based metrics are introduced: mean absolute yaw error (MAYE), peak-to-peak fluctuation amplitude, and the standard deviation of angular velocity, with overshoot occurrences included as an additional indicator. Field experiments demonstrate that the proposed method outperforms baseline algorithms, achieving lower yaw errors (0.61–0.66°), reduced maximum deviation (≤3.7°), and smaller steady-state variance (<0.44°²), thereby suppressing high-frequency jitter and improving turning convergence. Under typical working conditions, the method achieved a mean yaw deviation of 0.6602°, a fluctuation of 5.59°, an angular velocity standard deviation of 10.79°/s, and 155 overshoot instances. The yaw angle remained concentrated around the target orientation, while angular velocity responses stayed stable without loss-of-control events, indicating a favorable balance between responsiveness and smoothness. Overall, the study validates the robustness and adaptability of the proposed strategy in complex orchard scenarios and establishes a reusable evaluation framework, offering theoretical insights and practical guidance for intelligent agricultural machinery optimization. Full article

(This article belongs to the Special Issue Design and Development of Smart Crop Protection Equipment)

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19 pages, 3909 KB

Open AccessArticle

The Effects of Long-Term Manure and Grass Mulching on Microbial Communities, Enzyme Activities, and Soil Organic Nitrogen Fractions in Orchard Soils of the Loess Plateau, China

by Qi Wang, Luxiao Guo, Xue Gao, Songling Chen, Xinxin Song, Fei Gao, Wei Liu, Hua Guo, Guoping Wang and Xinping Fan

Agriculture 2025, 15(19), 2084; https://doi.org/10.3390/agriculture15192084 - 6 Oct 2025

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Abstract

Organic manure and grass mulching are widely recognized as modifiers of soil microbial communities and nutrient dynamics; however, the combined effects of these practices on nitrogen fractionation and microbial functionality in orchard ecosystems remain poorly understood. This study conducted a comprehensive evaluation of [...] Read more.

Organic manure and grass mulching are widely recognized as modifiers of soil microbial communities and nutrient dynamics; however, the combined effects of these practices on nitrogen fractionation and microbial functionality in orchard ecosystems remain poorly understood. This study conducted a comprehensive evaluation of soil nitrogen fractions, enzymatic activity, microbial diversity and functional traits in walnut orchards under three management practices: organic manure (OM), grass mulching combined with manure (GM), and chemical fertilization (CF) in China’s Loess Plateau. The results revealed that OM and GM significantly enhanced soil nutrient pools, with GM elevating total nitrogen by 1.96-fold, soil organic carbon by 97.79%, ammonium nitrogen by 128%, and nitrate nitrogen by 54.56% relative to CF. Furthermore, the OM significantly increased the contents of total hydrolysable nitrogen, amino sugar nitrogen, amino acid nitrogen, ammonia nitrogen, hydrolysable unidentified nitrogen, non-acid-hydrolyzable nitrogen compared to the CF and GM treatments. Meanwhile, ASN and AN had significant effects on mineral and total nitrogen. The OM and GM had higher activities of leucine aminopeptidase enzymes (LAP), α-glucosidase enzyme, β-glucosidase enzyme (βG), and N-acetyl-β-D-glucosidase enzyme (NAG). Microbial community analysis revealed distinct responses to different treatments: OM and GM enhanced bacterial Shannon index, while suppressing fungal diversity, promoting the relative abundance of copiotrophic bacterial phyla such as Proteobacteria and Chloroflexi. Moreover, GM favored the enrichment of lignocellulose-degrading Ascomycota fungi. Functional annotation indicated that Chemoheterotrophy (43.54%) and Aerobic chemoheterotrophy (42.09%) were the dominant bacterial metabolic pathways. The OM significantly enhanced the abundance of fermentation-related genes. Additionally, fungal communities under the OM and GM showed an increased relative abundance of saprotrophic taxa, and a decrease in the relative abundances of potential animal and plant pathogenic taxa. The Random forest model further confirmed that βG, LAP, and NAG, as well as Basidiomycota, Mortierellomycota, and Ascomycota served as pivotal mediators of soil organic nitrogen fraction. Our findings demonstrated that combined organic amendments and grass mulching can enhance soil N retention capacity, microbial functional redundancy, and ecosystem stability in semi-arid orchards. These insights support the implementation of integrated organic management as a sustainable approach to enhance nutrient cycling and minimize environmental trade-offs in perennial fruit production systems. Full article

(This article belongs to the Section Agricultural Soils)

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22 pages, 2616 KB

Open AccessArticle

Corn-Domesticated Bacteria Synergy Removes Pyrene and Enhances Crop Biomass: A Sustainable Farmland Remediation Strategy

by Lu Gao, Charles Obinwanne Okoye, Feiyue Lou, Bonaventure Chidi Ezenwanne, Yanfang Wu, Xunfeng Chen, Yongli Wang, Xia Li and Jianxiong Jiang

Agriculture 2025, 15(19), 2083; https://doi.org/10.3390/agriculture15192083 - 6 Oct 2025

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Abstract

High-molecular-weight polycyclic aromatic hydrocarbons (PAHs), such as pyrene, are persistent environmental pollutants that threaten soil health and agricultural productivity due to their resistance to degradation. This study evaluated the efficacy of domesticated bacteria isolated from contaminated farmland soil and activated sludge, used alone [...] Read more.

High-molecular-weight polycyclic aromatic hydrocarbons (PAHs), such as pyrene, are persistent environmental pollutants that threaten soil health and agricultural productivity due to their resistance to degradation. This study evaluated the efficacy of domesticated bacteria isolated from contaminated farmland soil and activated sludge, used alone and in combination with corn (Zea mays L.), to remove pyrene from soil, enhance plant growth, improve tolerance, and ensure crop safety. Six bacterial strains were isolated: three from polluted farmland soil (WB1, WB2, and WF2) and three from activated sludge (WNB, WNC, and WH2). High-throughput 16S rRNA amplicon sequencing profiled bacterial communities after 30 days of treatment. Analytical tools, including LEfSe, random forest, and ZiPi analyses, identified biomarkers and core bacteria associated with pyrene degradation, assessing their correlations with plant growth, tolerance, and pyrene accumulation in corn straw. Bacteria from activated sludge (WNB, WNC, and WH2) outperformed farmland soil-derived strains and the inoculant strain ETN19, with WH2 and WNC achieving 65.06% and 87.69% pyrene degradation by days 15 and 30, respectively. The corn–bacteria consortium achieved up to 97% degradation. Activated sewage sludge (ASS)-derived bacteria were more effective at degrading pyrene and enhancing microbial activity, while soil-derived bacteria better promoted plant growth and reduced pyrene accumulation in straw. Microbial communities, dominated by Proteobacteria, exhibited high species richness and resilience, contributing to xenobiotic degradation. The corn-domesticated bacteria consortia effectively degraded pyrene, promoted plant growth, and minimized pollutant accumulation in crops. This remediation technology offers a promising strategy for rapid and sustainable bioremediation of agricultural soils contaminated with organic compounds such as PAHs or other complex pollutants, while promoting the development of efficient bacterial communities that enhance crop growth. Full article

(This article belongs to the Section Agricultural Soils)

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16 pages, 2036 KB

Open AccessArticle

High Proportion of Blue Light Contributes to Product Quality and Resistance to Phytophthora Infestans in Tomato Seedlings

by Chengyao Jiang, Yue Ma, Kexin Zhang, Yu Song, Zixi Liu, Mengyao Li, Yangxia Zheng, Sang Ge, Tonghua Pan, Junhua Xie and Wei Lu

Agriculture 2025, 15(19), 2082; https://doi.org/10.3390/agriculture15192082 - 6 Oct 2025

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Abstract

Plant seedlings are sensitive to cultivation environment factors and highly susceptible to pathogenic infections under adverse conditions such as inappropriate light environment. In this study, five kinds of LED lighting sources with different red (R) and blue (B) light combinations were set up: [...] Read more.

Plant seedlings are sensitive to cultivation environment factors and highly susceptible to pathogenic infections under adverse conditions such as inappropriate light environment. In this study, five kinds of LED lighting sources with different red (R) and blue (B) light combinations were set up: R10B0, R7B3, R5B5, R2B8 and R0B10 (with R:B ratios of 10:0, 7:3, 5:5, 2:8 and 0:10, respectively) to explore their effects on tomato seedlings’ growth, AsA-GSH cycle, endogenous hormones, and resistance to Phytophthora infestans, providing a basis for factory seedling light-quality selection. The results showed that with the increase in the proportion of blue light in the composite light, the growth indicators, photosynthetic characteristic parameters and enzyme activities of tomato seedlings generally increased. The contents of AsA, reduced glutathione, and oxidized glutathione all reached the maximum under high-proportion blue-light treatments (R2B8 and R0B10). The high-blue-light groups (R2B8 and R0B10) had the highest AsA and glutathione contents. The red–blue combinations reduced inhibitory ABA and increased growth-promoting hormones (e.g., melatonin), while monochromatic light increased ABA to inhibit growth. After inoculation with P. infestans, the apoplastic glucose content was the highest under the red–blue-combined treatments (R5B5 and R2B8), while the total glucose content in leaves was the highest under the combined light R2B8 treatment. In conclusion, high-proportion blue-light treatment can greatly promote the photosynthetic process of tomato, enhance the AsA-GSH cycle, and achieve the best effect in improving the resistance of tomatoes to P. infestans. Given these, the optimal light environment setting was R:B = 2:8. Full article

(This article belongs to the Special Issue Research on Plant Production in Greenhouse and Plant Factory Systems—2nd Edition)

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17 pages, 5330 KB

Open AccessArticle

Global Potential Distribution of Carpomya vesuviana Costa Under Climate Change and Potential Economic Impacts on Chinese Jujube Industries

by Jingxuan Ning, Ming Li, Yuhan Qi, Haoxiang Zhao, Xiaoqing Xian, Jianyang Guo, Nianwan Yang, Hongxu Zhou and Wanxue Liu

Agriculture 2025, 15(19), 2081; https://doi.org/10.3390/agriculture15192081 - 6 Oct 2025

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Abstract

Carpomya vesuviana (Diptera: Tephritidae), a significant invasive forestry pest of Zizyphus crops worldwide, has spread globally across jujube-growing regions, causing substantial yield losses and economic damage. In China, it is classified as both an imported and forestry quarantine pest. Existing risk assessments have [...] Read more.

Carpomya vesuviana (Diptera: Tephritidae), a significant invasive forestry pest of Zizyphus crops worldwide, has spread globally across jujube-growing regions, causing substantial yield losses and economic damage. In China, it is classified as both an imported and forestry quarantine pest. Existing risk assessments have primarily focused on the potential geographical distributions (PGDs) of C. vesuviana, but its economic impact on host plants is unknown. Therefore, we used an optimised MaxEnt model based on species distribution records and relevant environmental variables to predict the PGDs of C. vesuviana under current and future climate scenarios. Meanwhile, we used the @RISK stochastic model to assess the economic impact of this pest on the Chinese jujube industry under various scenarios. The results showed that the human influence index (HII), mean temperature of the wettest quarter (Bio8), temperature seasonality (Bio4), and precipitation during the driest month (Bio14) were the significant environmental variables affecting species distribution. Under the current climatic scenario, the total suitable area of C. vesuviana reached 2171.39 × 10⁴ km², which is mainly distributed in southern and western Asia, southern Europe, central North America, western Africa, and eastern South America. Potentially suitable habitats will increase and shift to the middle and high latitudes of the Northern Hemisphere under future climatic scenarios. Under the no-control scenario, C. vesuviana could cause losses of 15,687 million CNY to the jujube industry in China. However, control measures could have saved losses of 5047 million CNY. This study provides a theoretical basis for preventive monitoring and integrated management of C. vesuviana globally and helps reduce its economic impact on the jujube industry in China. Full article

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

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24 pages, 1738 KB

Open AccessArticle

Manure Production Projections for Latvia: Challenges and Potential for Reducing Greenhouse Gas Emissions

by Irina Pilvere, Agnese Krievina, Ilze Upite and Aleksejs Nipers

Agriculture 2025, 15(19), 2080; https://doi.org/10.3390/agriculture15192080 - 6 Oct 2025

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Abstract

Manure is a valuable organic resource for sustainable agriculture, enhancing soil fertility and promoting nutrient cycling; however, it also contributes significantly to methane and nitrous oxide emissions. The European Green Deal and Latvia’s National Energy and Climate Plan have set targets for reducing [...] Read more.

Manure is a valuable organic resource for sustainable agriculture, enhancing soil fertility and promoting nutrient cycling; however, it also contributes significantly to methane and nitrous oxide emissions. The European Green Deal and Latvia’s National Energy and Climate Plan have set targets for reducing agricultural greenhouse gas (GHG) emissions, including those related to improved manure management. Therefore, this research aims to estimate the future manure production in Latvia to determine the potential for reducing GHG emissions by 2050. Using the LASAM model developed in Latvia, the number of farm animals, the amount of manure, and the associated GHG emissions were projected for the period up to 2050. The calculations followed the Intergovernmental Panel on Climate Change (IPCC) methodology and were based on national indicators and current national GHG inventory data covering the period of 2021–2050. Significant changes in the structure of manure in Latvia are predicted by 2050, with the proportion of liquid manure expected to increase while the amounts of solid manure and manure deposited by grazing animals are expected to decrease. The GHG emission projection results indicate that by 2050, total emissions from manure management will decrease by approximately 5%, primarily due to a decline in the number of farm animals and, consequently, a reduction in the amount of manure. In contrast, methane emissions are expected to increase by approximately 5% due to production intensification. The research results emphasise the need to introduce more effective methane emission reduction technologies and improved projection approaches. Full article

(This article belongs to the Special Issue New Challenges and Trends in Agri-Environmental Management: Accomplishment of Sustainable Development Goals)

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19 pages, 4791 KB

Open AccessArticle

Design and Test of a Low-Damage Garlic Seeding Device Based on Rigid–Flexible Coupling

by Siyuan Wang, Yubai Meng, Yongjian Wang, Hua Li and Xiaodong Zhang

Agriculture 2025, 15(19), 2079; https://doi.org/10.3390/agriculture15192079 - 5 Oct 2025

Viewed by 300

Abstract

In conventional mechanized garlic seeding process, seed remains a persistent challenge that is difficult to avoid. This study proposes a solution by designing and testing a garlic seeding device based on a rigid–flexible coupling mechanism, aimed at minimizing seed damage during sowing. The [...] Read more.

In conventional mechanized garlic seeding process, seed remains a persistent challenge that is difficult to avoid. This study proposes a solution by designing and testing a garlic seeding device based on a rigid–flexible coupling mechanism, aimed at minimizing seed damage during sowing. The seeding pocket was constructed from a flexible metal sheet, which served as its structural foundation. A slider moving along a fixed track enabled the retraction and release of the pocket, thereby facilitating seed collection and discharge. The effects of pocket radius, rotational speed of seed discharge disc, and thickness of metal sheet on the stress of garlic seeds were investigated through the finite element method. Subsequently, an experimental bench was set up to analyze the effects of influence of these parameters on seed damage rate, single-seed rate, and leakage rate. Results demonstrated that under optimal parameters—a pocket radius of 12 mm, a seed discharge disc rotational speed of 0.21 rad/s, and a metal sheet thickness of 0.15 mm—the mechanism achieved a single-seed rate of 78.4%, a leakage rate of 11.4%, and a maximum stress on garlic seeds of only 0.535 MPa. Notably, this stress level was well below the damage threshold of garlic seeds, resulting in zero damage that outperformed conventional rigid seeding devices. These findings demonstrate the mechanism’s strong potential to preserve seed integrity, although the overall seeding performance remains modest and warrants further optimization in future designs. Full article

(This article belongs to the Special Issue Research Progress on Agricultural Equipments for Precision Planting and Harvesting—2nd Edition)

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17 pages, 2114 KB

Open AccessArticle

Effect of Organic Amendments and Biostimulants on Zucchini Yield and Fruit Quality Under Alkaline Conditions

by Samira Islas-Valdez, Reagan Sproull, Ty Sumners and Nicole Wagner

Agriculture 2025, 15(19), 2078; https://doi.org/10.3390/agriculture15192078 - 5 Oct 2025

Viewed by 342

Abstract

Soil amendments can enhance soil and plant health; however, limited research has addressed their effects on soil health and crop productivity in alkaline soil. This study investigated the effects of various soil amendments and biostimulants by the Haney Soil Health Test, plant sap [...] Read more.

Soil amendments can enhance soil and plant health; however, limited research has addressed their effects on soil health and crop productivity in alkaline soil. This study investigated the effects of various soil amendments and biostimulants by the Haney Soil Health Test, plant sap analysis, and Cucurbita pepo cv. ‘Dunja’ yield and quality. Treatments included unamended soil (T1) and applications of Humisoil® (T2), Humisoil with biochar (T3), wood vinegar (T4), Ensoil algae^TM (T5), and Humisoil with biochar and basaltic rock dust (T6). Compared to T1, T6, T5, T2, and T3 increased yield by 107%, 87%, 86%, and 52%, respectively. Regarding total fruit number per plant, T2, T6, and T5 outperformed T1 by 42%, 37%, and 37%, respectively. Additionally, T6 decreased Na concentration by 59% in the sap of young leaves and 50% in old leaves compared to T1. Compared to T1, T2 also reduced Na concentration in the sap of old leaves by 63%. For Cl, decreases of 30%, 16%, and 24% in old leaves were observed in T2, T4, and T6 treatments, respectively. These findings highlight the potential of biostimulants and soil amendments to improve zucchini yield and quality while improving soil health in alkaline soils. Full article

(This article belongs to the Section Agricultural Soils)

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36 pages, 4484 KB

Open AccessReview

Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control

by Yu Wu, Li Chen, Ning Yang and Zongbao Sun

Agriculture 2025, 15(19), 2077; https://doi.org/10.3390/agriculture15192077 - 3 Oct 2025

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Abstract

With the rapid advancement of artificial intelligence technology, the widespread application of deep learning in computer vision is driving the transformation of agricultural pest detection and control toward greater intelligence and precision. This paper systematically reviews the evolution of agricultural pest detection and [...] Read more.

With the rapid advancement of artificial intelligence technology, the widespread application of deep learning in computer vision is driving the transformation of agricultural pest detection and control toward greater intelligence and precision. This paper systematically reviews the evolution of agricultural pest detection and control technologies, with a special focus on the effectiveness of deep-learning-based image recognition methods for pest identification, as well as their integrated applications in drone-based remote sensing, spectral imaging, and Internet of Things sensor systems. Through multimodal data fusion and dynamic prediction, artificial intelligence has significantly improved the response times and accuracy of pest monitoring. On the control side, the development of intelligent prediction and early-warning systems, precision pesticide-application technologies, and smart equipment has advanced the goals of eco-friendly pest management and ecological regulation. However, challenges such as high data-annotation costs, limited model generalization, and constrained computing power on edge devices remain. Moving forward, further exploration of cutting-edge approaches such as self-supervised learning, federated learning, and digital twins will be essential to build more efficient and reliable intelligent control systems, providing robust technical support for sustainable agricultural development. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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22 pages, 4631 KB

Open AccessArticle

Crop Disease Spore Detection Method Based on Au@Ag NRS

by Yixue Zhang, Jili Guo, Fei Bian, Zhaowei Li, Chuandong Guo, Jialiang Zheng and Xiaodong Zhang

Agriculture 2025, 15(19), 2076; https://doi.org/10.3390/agriculture15192076 - 3 Oct 2025

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Abstract

Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via [...] Read more.

Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via a modified seed-mediated growth method. Using an existing microfluidic chip developed by the research group, disease spores were separated and enriched, followed by combining Au@Ag NRS with Crop Disease Spores through electrostatic adsorption. Raman spectroscopy was employed to collect SERS fingerprint spectra of Crop Disease Spores. The spectra underwent baseline correction using Adaptive Least Squares (ALS) and standardization via Standard Normal Variate (SNV). Dimensionality reduction preprocessing was performed using Principal Component Analysis (PCA) and Successive Projections Algorithm combined with Competitive Adaptive Reweighted Sampling (SCARS). Classification was then executed using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The SCARS-MLP model achieved the highest accuracy at 97.92% on the test set, while SCARS-SVM, PCA-SVM, and SCARS-MLP models attained test set accuracy of 95.83%, 95.24%, and 96.55%, respectively. Thus, the proposed Au@Ag NRS-based SERS technology can be applied to detect airborne disease spores, establishing an early and precise method for Crop Disease detection. Full article

(This article belongs to the Special Issue Spectral Data Analytics for Crop Growth Information)

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23 pages, 15968 KB

Open AccessArticle

YOLOv8n-RMB: UAV Imagery Rubber Milk Bowl Detection Model for Autonomous Robots’ Natural Latex Harvest

by Yunfan Wang, Lin Yang, Pengze Zhong, Xin Yang, Chuanchuan Su, Yi Zhang and Aamir Hussain

Agriculture 2025, 15(19), 2075; https://doi.org/10.3390/agriculture15192075 - 3 Oct 2025

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Abstract

Natural latex harvest is pushing the boundaries of unmanned agricultural production in rubber milk collection via integrated robots in hilly and mountainous regions, such as the fixed and mobile tapping robots widely deployed in forests. As there are bad working conditions and complex [...] Read more.

Natural latex harvest is pushing the boundaries of unmanned agricultural production in rubber milk collection via integrated robots in hilly and mountainous regions, such as the fixed and mobile tapping robots widely deployed in forests. As there are bad working conditions and complex natural environments surrounding rubber trees, the real-time and precision assessment of rubber milk yield status has emerged as a key requirement for improving the efficiency and autonomous management of these kinds of large-scale automatic tapping robots. However, traditional manual rubber milk yield status detection methods are limited in their ability to operate effectively under conditions involving complex terrain, dense forest backgrounds, irregular surface geometries of rubber milk, and the frequent occlusion of rubber milk bowls (RMBs) by vegetation. To address this issue, this study presents an unmanned aerial vehicle (UAV) imagery rubber milk yield state detection method, termed YOLOv8n-RMB, in unstructured field environments instead of manual watching. The proposed method improved the original YOLOv8n by integrating structural enhancements across the backbone, neck, and head components of the network. First, a receptive field attention convolution (RFACONV) module is embedded within the backbone to improve the model’s ability to extract target-relevant features in visually complex environments. Second, within the neck structure, a bidirectional feature pyramid network (BiFPN) is applied to strengthen the fusion of features across multiple spatial scales. Third, in the head, a content-aware dynamic upsampling module of DySample is adopted to enhance the reconstruction of spatial details and the preservation of object boundaries. Finally, the detection framework is integrated with the BoT-SORT tracking algorithm to achieve continuous multi-object association and dynamic state monitoring based on the filling status of RMBs. Experimental evaluation shows that the proposed YOLOv8n-RMB model achieves an AP@0.5 of 94.9%, an AP@0.5:0.95 of 89.7%, a precision of 91.3%, and a recall of 91.9%. Moreover, the performance improves by 2.7%, 2.9%, 3.9%, and 9.7%, compared with the original YOLOv8n. Plus, the total number of parameters is kept within 3.0 million, and the computational cost is limited to 8.3 GFLOPs. This model meets the requirements of yield assessment tasks by conducting computations in resource-limited environments for both fixed and mobile tapping robots in rubber plantations. Full article

(This article belongs to the Special Issue Plant Diagnosis and Monitoring for Agricultural Production)

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23 pages, 4086 KB

Open AccessArticle

Evaluation of Water Resources Carrying Capacity and Analysis of Influencing Factors in China’s Major Grain-Producing Areas Based on Machine Learning

by Kun Cheng, Xingyang Zhang and Nan Sun

Agriculture 2025, 15(19), 2074; https://doi.org/10.3390/agriculture15192074 - 2 Oct 2025

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Abstract

Evaluating regional water resource carrying capacity (WRCC) helps alleviate regional water supply–demand conflicts. This study constructed a 17-indicator system for evaluating WRCC in Major Grain-Producing Areas (MGPAs) based on the “production–living–ecology” functional perspective. It employed a combined Entropy Weight–Root Mean Square Deviation–CRITIC weighting [...] Read more.

Evaluating regional water resource carrying capacity (WRCC) helps alleviate regional water supply–demand conflicts. This study constructed a 17-indicator system for evaluating WRCC in Major Grain-Producing Areas (MGPAs) based on the “production–living–ecology” functional perspective. It employed a combined Entropy Weight–Root Mean Square Deviation–CRITIC weighting approach with a BP neural network model to conduct a comprehensive assessment of WRCC across 13 MGPAs from 2004 to 2023. The results demonstrated the following: (1) Both MGPAs and the national level exhibit a “ecology dominance–living secondary–production weakness” pattern in functional weighting. (2) WRCC in MGPAs is characterized by agricultural production dominance, basic domestic needs as the core, and localized ecological protection as the focus—significantly differing from the national pattern of industrial-driven, economically interconnected, and trans-regional ecological concerns. (3) Spatiotemporally, WRCC levels across the 13 provinces have consistently increased, with a spatial distribution characterized by “higher in the north, lower in the south.” These findings reveal that water resource management in MGPAs requires strategies distinct from national approaches, emphasizing agricultural water conservation and efficiency alongside localized ecological protection. This provides precise policy tools and scientific decision support for implementing water-based production quotas and coordinating food security with water resource security in these regions. Full article

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

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25 pages, 5267 KB

Open AccessArticle

Evolution of the Global Forage Products Trade Network and Implications for China’s Import Security

by Shuxia Zhang, Zihao Wei, Cha Cui and Mingli Wang

Agriculture 2025, 15(19), 2073; https://doi.org/10.3390/agriculture15192073 - 2 Oct 2025

Viewed by 435

Abstract

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). [...] Read more.

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). Using complex network analysis methods, it systematically analyzes the network’s topological structure and evolutionary patterns, with a focus on their impact on China’s import security. The study addresses the following questions: What evolutionary patterns does the global forage trade network exhibit in terms of its topological structure? How does the evolution of this network impact the import security of forage products in China, specifically regarding supply chain stability and risk resilience? The research findings indicate the following: (1) From 2000 to 2024, the total volume of global forage products trade increased by 48.17%, primarily driven by forage products excluding alfalfa meal and pellets, which accounted for an average of 82.04% of volume annually. Additionally, the number of participating countries grew by 21.95%. (2) The global forage products trade network follows a power–law distribution, characterized by increasing network density, a clustering coefficient that initially declines and then rises, and a shortening of the average path length. (3) The core structure of the global forage products trade network shows an evolutionary trend of diffusion from core nodes in North America, Oceania, and Asia to multiple core nodes, including those in North America, Oceania, Europe, Africa, and Asia. (4) China’s forage products trade network displays distinct phase characteristics; however, imports face significant risks from high supply chain dependency and exposure to international price fluctuations. Based on these conclusions, it is recommended that China actively expands trade relations with potential product-exporting countries in Africa, encouraging enterprises to “go global.” Additionally, China should establish a three-dimensional supply chain security system, comprising maritime, land, and storage components, to enhance risk resistance and import safety. Full article

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

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18 pages, 18468 KB

Open AccessArticle

Assessment of Heavy Metal Transfer from Soil to Forage and Milk in the Tungurahua Volcano Area, Ecuador

by Lourdes Carrera-Beltrán, Irene Gavilanes-Terán, Víctor Hugo Valverde-Orozco, Steven Ramos-Romero, Concepción Paredes, Ángel A. Carbonell-Barrachina and Antonio J. Signes-Pastor

Agriculture 2025, 15(19), 2072; https://doi.org/10.3390/agriculture15192072 - 2 Oct 2025

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Abstract

The Bilbao parish, located on the slopes of the Tungurahua volcano (Ecuador), was heavily impacted by ashfall during eruptions between 1999 and 2016. Volcanic ash may contain toxic metals such as Pb, Cd, Hg, As, and Se, which are linked to neurological, renal, [...] Read more.

The Bilbao parish, located on the slopes of the Tungurahua volcano (Ecuador), was heavily impacted by ashfall during eruptions between 1999 and 2016. Volcanic ash may contain toxic metals such as Pb, Cd, Hg, As, and Se, which are linked to neurological, renal, skeletal, pulmonary, and dermatological disorders. This study evaluated metal concentrations in soil (40–50 cm depth, corresponding to the rooting zone of forage grasses), forage (English ryegrass and Kikuyu grass), and raw milk to assess potential risks to livestock and human health. Sixteen georeferenced sites were selected using a simple random probabilistic sampling method considering geological variability, vegetation cover, accessibility, and cattle presence. Samples were digested and analyzed with a SpectrAA 220 atomic absorption spectrophotometer (Varian Inc., Victoria, Australia). Soils (Andisols) contained Hg (1.82 mg/kg), Cd (0.36 mg/kg), As (1.36 mg/kg), Pb (1.62 mg/kg), and Se (1.39 mg/kg); all were below the Ecuadorian limits, except for Hg and Se. Forage exceeded FAO thresholds for Pb, Cd, As, Hg, and Se. Milk contained Pb, Cd, and Hg below detection limits, while Se averaged 0.047 mg/kg, exceeding water safety guidelines. Findings suggest soils act as sources with significant bioaccumulation in forage but limited transfer to milk. Although immediate consumer risk is low, forage contamination highlights long-term hazards, emphasizing the need for monitoring, soil management, and farmer guidance. Full article

(This article belongs to the Section Agricultural Soils)

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Agriculture, Volume 15, Issue 19 (October-1 2025) – 110 articles

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