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Agriculture
Volume 16
Issue 17
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Agriculture, Volume 16, Issue 17 (September-1 2026) – 134 articles

Cover Story (view full-size image): Peatlands in Hokkaido are typically covered with a 15–30 cm or thicker layer of mineral soil. Mineral soil dressing has enabled peatlands to support crop production by improving soil bearing capacity and growing conditions. However, evidence on how this cover alters greenhouse gas emissions and land subsidence remains limited. In these mineral-soil-covered peatlands, greenhouse gas emissions associated with peat decomposition may be minimized at a lower groundwater level than in peatlands without mineral soil cover. At the same time, mineral soil dressing can promote subsidence through consolidation. Therefore, a key future task is to identify a groundwater level for mineral-soil-covered peatlands that can sustain food production while reducing both greenhouse gas emissions and land subsidence, and to develop subsurface irrigation strategies to achieve it. View this paper
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27 pages, 29405 KB  
Article
Design and Experiment of a 3D Laser-Based Pork Rib Cutting System
by Shiwei Yan, Yiqi Fang, Qianrui Xia, Yifan Lv, Ming Huang, Kunjie Chen and Jichao Huang
Agriculture 2026, 16(17), 1935; https://doi.org/10.3390/agriculture16171935 - 7 Sep 2026
Viewed by 478
Abstract
Accurate portioning of bone-in pork ribs remains challenging because of their curved geometry, uneven meat thickness, and irregular bone–meat distribution. To address this issue, we developed a semi-automatic two-stage pork rib cutting system based on 3D line laser scanning. In the first stage, [...] Read more.
Accurate portioning of bone-in pork ribs remains challenging because of their curved geometry, uneven meat thickness, and irregular bone–meat distribution. To address this issue, we developed a semi-automatic two-stage pork rib cutting system based on 3D line laser scanning. In the first stage, inter-rib seams were identified from the point cloud of a whole rib rack to determine cutting positions. The separated rib strips were then manually reloaded, aligned, and rescanned. In the second stage, skeleton curves were extracted and adaptive equal-arc-length cutting positions were generated within a preset range of 40–50 mm. The segmentation method was evaluated on 20 independent pork rib racks and achieved mean precision, recall, F1-score, and overall accuracy values of 0.936, 0.918, 0.927, and 0.931, respectively. In the cutting experiment, three racks containing 32 rib strips produced 163 final portions. The pooled rib-seam cutting success, length qualification, bone fracture, and overall cutting success rates were 89.66%, 89.57%, 5.52%, and 87.73%, respectively. The operating cycle time was approximately 120 s per rack, corresponding to a nominal throughput of 30 racks h−1. These results indicate that the system can identify rib seams and produce uniform portions, providing a feasible approach for semi-automatic processing of bone-in pork ribs. Full article
(This article belongs to the Section Agricultural Technology)
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31 pages, 823 KB  
Article
Livelihood Capitals and Climate Change Adaptation Among Smallholder Coffee Farmers in Southwestern Uganda
by Noel Kishaija and Bálint Heil
Agriculture 2026, 16(17), 1934; https://doi.org/10.3390/agriculture16171934 - 7 Sep 2026
Viewed by 1455
Abstract
Climate change poses growing risks to coffee farming in Uganda. However, households do not adapt to the same degree, and it remains unclear which household resources contribute to differences in adaptation. This study examines the specific household resources associated with climate adaptation among [...] Read more.
Climate change poses growing risks to coffee farming in Uganda. However, households do not adapt to the same degree, and it remains unclear which household resources contribute to differences in adaptation. This study examines the specific household resources associated with climate adaptation among 376 smallholder coffee-farming households across four districts in Southwestern Uganda. Adaptation was measured using a count of adopted practices and a continuous index derived from Principal Component Analysis (PCA), with Multiple Correspondence Analysis (MCA) used as a robustness check. The analysis examined constituent household resources separately to assess whether broad livelihood-capital categories obscure divergent relationships among their components. Household size, knowledge exchange with other farmers, training, and ownership of basic farm tools were the most consistent positive correlates of adaptation. Years of coffee-farming experience, by contrast, showed a negative relationship with adaptation, opposite to the direction observed for household size despite both being classified within the human-capital domain. A composite human-capital specification showed weaker statistical fit than the disaggregated specification. A similar divergence occurred within financial capital: off-farm income showed a positive relationship with adaptation, whereas access to credit did not. Within physical capital, tool ownership was positively related to adaptation, whereas irrigation equipment, owned by only 1.6% of households, was not statistically significant. These findings suggest that policies to strengthen climate adaptation among smallholder coffee farmers should prioritize farmer-to-farmer knowledge exchange, training, and access to basic farm tools. Additionally, findings caution against treating broad livelihood-capital categories as internally homogeneous because constituent resources may have different or opposing relationships with adaptation. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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32 pages, 435 KB  
Review
Valorisation of Selected Fruit Pomaces in Animal Nutrition
by Vanajah Liyinthan, Junyue Guan, Qinghua Qiu, Xianghui Zhao, Yanjiao Li, Lingli Chen, Wenjun Wang and Kehui Ouyang
Agriculture 2026, 16(17), 1933; https://doi.org/10.3390/agriculture16171933 - 7 Sep 2026
Viewed by 571
Abstract
Rising feed costs and the shift towards circular bioeconomy systems have generated interest in agro-industrial by-products as livestock feeds, but the evidence on fruit pomaces is fragmented across single pomaces, single species and single processing methods. This review compares apple, citrus and grape [...] Read more.
Rising feed costs and the shift towards circular bioeconomy systems have generated interest in agro-industrial by-products as livestock feeds, but the evidence on fruit pomaces is fragmented across single pomaces, single species and single processing methods. This review compares apple, citrus and grape pomaces against a common set of nutritional, processing, functional and environmental criteria, and grades the strength of the evidence supporting each comparison. Under particular species-, processing- and dose-specific conditions, properly prepared pomaces have been reported to improve ruminal fermentation, nutrient digestibility, milk composition and oxidative status in ruminants, and gut health, antioxidant status and product quality in non-ruminants. Responses are heterogeneous, and null or adverse effects on intake, digestibility and performance also occur, particularly at high inclusion levels and with lignin- and tannin-rich material. Reductions in enteric methane have been measured in vivo for grape by-products in dairy cattle, whereas the evidence for citrus flavonoids remains in vitro; whole-system environmental and economic benefits are unquantified. Research priorities include standardised processing and compositional reporting, routine control of mycotoxins, pesticide residues and heavy metals, species-specific dose–response trials, and whole-chain techno-economic and life-cycle assessment. Full article
(This article belongs to the Special Issue Alternative Feed in Livestock Nutrition: Potential Benefits and Risks)
38 pages, 1459 KB  
Article
Limits to the Spatial Transferability of Machine-Learning Models for Farm-Record-Level Maize Yield Prediction in Manabí, Ecuador
by Teresa Guarda
Agriculture 2026, 16(17), 1932; https://doi.org/10.3390/agriculture16171932 - 7 Sep 2026
Viewed by 363
Abstract
Farm-level yield models are commonly evaluated using random train–test partitions; however, spatial dependence among geographically related observations can lead to overly optimistic estimates of predictive performance and may not adequately reflect generalization to unseen locations. This study evaluated whether supervised machine-learning models could [...] Read more.
Farm-level yield models are commonly evaluated using random train–test partitions; however, spatial dependence among geographically related observations can lead to overly optimistic estimates of predictive performance and may not adequately reflect generalization to unseen locations. This study evaluated whether supervised machine-learning models could predict farm-level hard dry maize yield in previously unseen parishes of Manabí, Ecuador. The response variable was record-level maize yield, while predictors included survey-derived farm, crop, producer, management, socioeconomic, and digital-access characteristics. Climate-enhanced models additionally included crop-season cumulative precipitation, longest dry spell, mean 2 m air temperature, and mean upper-layer soil moisture. The analysis comprised 518 sole-crop hard dry maize records representing 433 producers in 45 parishes. Elastic Net, Random Forest, and histogram-based gradient boosting models were compared with a training-fold mean benchmark using nested cross-validation grouped by parish. The mean observed yield was 4.09 t ha−1. The benchmark achieved a mean absolute error (MAE) of 1.534 t ha−1, whereas the best survey-only and survey-plus-climate models achieved MAE values of 1.535 and 1.548 t ha−1, respectively. For histogram-based gradient boosting, the estimated climate-related MAE reduction was −0.013 t ha−1 (−0.85%; parish-cluster bootstrap 95% CI: −0.061 to 0.036). All model configurations produced negative pooled R2 values. Random record-level validation yielded more favorable results, indicating potential spatial optimism. The findings do not imply that management practices or climate conditions are agronomically unimportant; rather, they indicate that the available survey data and parish-level climate variables were insufficient to support reliable yield prediction for individual crop records in previously unseen parishes. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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28 pages, 17045 KB  
Article
Relative Localization Error Compensation Under Attitude Disturbances Based on Long Short-Term Memory Residual Learning and Adaptive Extended Kalman Filtering
by Dongfang Li, Haoran Wu, Wenxiang Xu, Weihua Wei, Haijun Zhang, Yejun Zhu, Maohua Xiao and Ke Chen
Agriculture 2026, 16(17), 1931; https://doi.org/10.3390/agriculture16171931 - 7 Sep 2026
Viewed by 430
Abstract
Tracked vehicles operating in hilly and mountainous agricultural environments are frequently subjected to pitch, roll, and vibration, which can introduce time-varying errors into ultra-wideband phase-difference-of-arrival (UWB-PDOA) relative localization. Aiming to improve localization accuracy under such disturbances, this study proposes a relative localization error [...] Read more.
Tracked vehicles operating in hilly and mountainous agricultural environments are frequently subjected to pitch, roll, and vibration, which can introduce time-varying errors into ultra-wideband phase-difference-of-arrival (UWB-PDOA) relative localization. Aiming to improve localization accuracy under such disturbances, this study proposes a relative localization error compensation method that integrates long short-term memory (LSTM) residual learning with a residual-adaptive extended Kalman filter (RAEKF), referred to as LSTM-RAEKF. The proposed method combines UWB-PDOA measurements with inertial measurement unit information to learn disturbance-related localization residuals and adaptively compensate for relative position and theta observations before filtering. A UWB/IMU relative localization test bench was developed, and experiments were performed under static, pitch, roll, and vibration conditions. Across different fixed-point tests, the proposed method reduced the planar position RMSE and theta RMSE by 35.0–62.9% and 54.7–70.8%, respectively. Considering all experimental conditions, the position RMSE decreased from 4.00 cm to 1.81 cm, while the theta RMSE decreased from 5.64° to 2.17°, corresponding to reductions of 54.8% and 61.5%, respectively. Furthermore, LSTM-RAEKF outperformed the standard extended Kalman filter and the innovation-based adaptive estimation extended Kalman filter. Overall, these results demonstrate that LSTM-RAEKF can effectively suppress localization errors induced by attitude disturbances and provide stable relative localization information for subsequent tracked vehicle following control. Full article
(This article belongs to the Section Agricultural Technology)
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24 pages, 4447 KB  
Article
Dynamic Light Field Reconstruction and Digital Twin Surrogate Modeling for Multi-Objective Light Environment Optimization of a Vertical Circulating Rice Seedling Rack
by Liwei Wang, Tianyang Wang, Yubo Yang, You Zhang, Lishuo Guo, Chengcheng Deng, Jiaying Dong, Lifeng Guo, Rui Gao, Qiuju Xie, Junying Zhao, Hongbo Li, Zhongbin Su and Shoutian Dong
Agriculture 2026, 16(17), 1930; https://doi.org/10.3390/agriculture16171930 - 7 Sep 2026
Viewed by 432
Abstract
Vertical circulating rice seedling racks in cold regions of northeast China face limited natural radiation, uneven light distribution, and high energy demand. This study develops a digital twin optimization framework integrating dynamic light field reconstruction, crop physiological response, surrogate modeling, and multi-objective analysis. [...] Read more.
Vertical circulating rice seedling racks in cold regions of northeast China face limited natural radiation, uneven light distribution, and high energy demand. This study develops a digital twin optimization framework integrating dynamic light field reconstruction, crop physiological response, surrogate modeling, and multi-objective analysis. A solar position algorithm-based simulator reconstructs the spatiotemporal PPFD distribution along the circulating trajectory and couples it with stage-specific rice seedling light response curves. An XGBoost surrogate model is then used to accelerate parameter evaluation for stage-wise optimization. Preliminary measurements using a plant light analyzer showed limited PPFD deviations from simulation, with a mean deviation of −3.4% and a maximum deviation of +11.4%. The surrogate model achieved a test R2 of 0.9955 and a five-fold cross-validation R2 of 0.9933 ± 0.0024. Under the engineering constraint of CV ≤ 0.10, stage-wise optimization identified max yield, best efficiency, and balanced operating strategies. The balanced strategy predicted a 7.7% increase in YI, a 6.0% reduction in energy use, and a decrease in CV from 0.0353 to 0.0058 relative to the fixed-parameter baseline. SHAP analysis further identified growth stage as the dominant structural factor, while lighting duration and rack speed mainly regulated within-stage performance. This framework provides a quantitative basis for stage-aware light environment optimization and subsequent experimental validation. Full article
(This article belongs to the Topic Intelligent Agriculture: Perception Technologies and Agricultural Equipment for Crop Production Processes)
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24 pages, 301 KB  
Perspective
The Artefact Trap: Why Digital Agriculture Research Keeps Missing the Field
by Jean-Pierre Chanet
Agriculture 2026, 16(17), 1929; https://doi.org/10.3390/agriculture16171929 - 6 Sep 2026
Viewed by 419
Abstract
Digital agriculture (sensors, Internet of Things, machine learning, robotics, digital twins) has for fifteen years been sustained by massive investment promising more productive, precise, and sustainable agriculture, yet adoption remains heterogeneous and systemic impact limited despite intense scientific output. The dominant explanation treats [...] Read more.
Digital agriculture (sensors, Internet of Things, machine learning, robotics, digital twins) has for fifteen years been sustained by massive investment promising more productive, precise, and sustainable agriculture, yet adoption remains heterogeneous and systemic impact limited despite intense scientific output. The dominant explanation treats this as a diffusion deficit and calls for more farmer training, advisory support, and subsidy; this reading leaves the structure of research itself unexamined and does not, on its own, explain why the gap between announced and delivered performance persists across technology generations. We argue that, alongside diffusion-side and demand-side factors, the paradox has a further, under-recognised upstream component: research design oriented towards technological artefacts rather than guaranteed functions. We develop the case for this complementary reading using Stahel’s performance economy framework and the Product-Service Systems literature. Four contributions follow: a tripartite typology of function (service-, specification-, and process-function) articulated hierarchically explains why research centred on specification-function alone cannot guarantee a service-function; current research is characterised, on the reading developed here, by three cumulative biases and three blind spots, including data governance and the under-representation of the social sciences; a four-axis reorientation agenda is proposed; and the framework is distinguished from Agricultural Innovation Systems, Responsible Research and Innovation, and mission-oriented research by specifying the functional level these leave indeterminate. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
33 pages, 15254 KB  
Article
Design and Experimental Validation of a DT-FPID-Based Local Canopy CO2 Enrichment Control System in a Chinese Solar Greenhouse
by Zhenwei Du, Yalong Song, Aiguang Zhang, Shuo Zhang, Jianfei Xing, Xufeng Wang, Long Wang and Wentao Li
Agriculture 2026, 16(17), 1928; https://doi.org/10.3390/agriculture16171928 - 6 Sep 2026
Viewed by 451
Abstract
Carbon dioxide (CO2) enrichment is an important means of increasing crop productivity in protected cultivation. However, local canopy CO2 concentration in Chinese solar greenhouses is jointly affected by gas release, pipeline transport, and ventilation disturbances, making fixed-parameter proportional–integral–derivative (PID) control [...] Read more.
Carbon dioxide (CO2) enrichment is an important means of increasing crop productivity in protected cultivation. However, local canopy CO2 concentration in Chinese solar greenhouses is jointly affected by gas release, pipeline transport, and ventilation disturbances, making fixed-parameter proportional–integral–derivative (PID) control unable to simultaneously achieve rapid tracking, low overshoot, and fast disturbance recovery. This study developed a CO2 enrichment system comprising controlled thermal decomposition of ammonium bicarbonate, condensation and water scrubbing, near-canopy delivery, and programmable logic controller (PLC)-based closed-loop control, and proposed a dynamic-target fuzzy PID (DT-FPID) strategy. Step-response tests were used to establish a first-order-plus-dead-time model linking heater duty cycle to local canopy CO2 concentration, followed by fixed-target tracking, rule-based dynamic-target execution, and short-term ventilation-disturbance recovery tests in a local validation zone of a Chinese solar greenhouse. Relative to fixed-parameter PID, DT-FPID showed approximately 68–79% lower maximum overshoot and approximately 35–70% shorter ±20 ppm precision settling time (T20) in simulation. At 600 ppm, the ±5% settling time was approximately 71% shorter, whereas at 800 and 1000 ppm it was broadly comparable to PID. In the greenhouse experiments, each controller–target combination included three independent runs. Based on descriptive comparisons of group means, DT-FPID showed approximately 47–49% lower mean maximum overshoot, approximately 36–40% shorter mean settling time, and approximately 77–80% shorter mean ventilation-disturbance recovery time; its mean maximum overshoot and settling time were also lower than those of conventional fuzzy PID. All three dynamic-target field runs completed the prescribed switches among the 600, 800, and 1000 ppm target levels. These results support control performance only under the short-term local validation conditions of this study; they are not used to determine physiologically or economically optimal CO2 concentrations or to extrapolate whole-greenhouse uniformity or long-term production effects. Full article
(This article belongs to the Section Agricultural Technology)
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30 pages, 5940 KB  
Article
Tip Penetration Resistance Under a Coaxial-Sleeve Stress-Shielding Boundary: Dimensionless and Mechanism-Corrected Models for Probing Stored Wheat
by Peifang Xin, Caixia Wang, Yilei Shu, Zhiyong Zhang, Chunqi Bai and Longwang Yue
Agriculture 2026, 16(17), 1927; https://doi.org/10.3390/agriculture16171927 - 6 Sep 2026
Viewed by 322
Abstract
Deep in situ sensing is essential for grain storage safety, yet penetration resistance restricts sensing devices in deep grain layers. Everting growth robots reduce body-side friction through tip eversion and are therefore well suited to low-disturbance probing in grain bulks. However, real eversion [...] Read more.
Deep in situ sensing is essential for grain storage safety, yet penetration resistance restricts sensing devices in deep grain layers. Everting growth robots reduce body-side friction through tip eversion and are therefore well suited to low-disturbance probing in grain bulks. However, real eversion couples soft-body deployment, particle rearrangement and boundary contact, so tip resistance cannot be measured independently with conventional full-contact models. This study combined dimensional analysis, discrete element simulations and coaxial double-sleeve equivalent penetration tests to establish a dimensionless baseline model (DBM) and a mechanism-corrected penetration model (MCPM). The double-sleeve apparatus acts as an equivalent rigid test boundary; it does not reproduce membrane eversion or flexible deployment. Within the stable penetration interval (100–300 mm), the measured tip resistance reached only 8.0–12.5% of the unshielded DBM baseline, a model-relative estimate obtained under different boundary conditions. The geometry exponent changed from −0.5642 in the DBM to +0.2983 in the MCPM, reflecting an empirical correction fitted to three retained geometries. Prediction failed for the lowest aspect ratio (λ=0.64), and an aspect ratio of 1.2–1.6 is suggested only as a preliminary range pending validation. The proposed equivalent test and corrected model provide preliminary references for tip-load estimation and probe optimisation of everting growth robots. Full article
(This article belongs to the Section Agricultural Technology)
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58 pages, 22598 KB  
Review
Multi-Target Harvesting Robots: Key Advances and Future Trends in Perception, Decision-Making, and Task Scheduling
by Meng Wang, Wei Ji, Donghao Wu, Zijie Shi, Hongjie Weng and Bo Xu
Agriculture 2026, 16(17), 1926; https://doi.org/10.3390/agriculture16171926 - 6 Sep 2026
Viewed by 409
Abstract
Multi-target harvesting is an important direction for improving the efficiency of orchard harvesting robots and advancing their engineering deployment. Compared with single-target harvesting, multi-target harvesting is constrained by dense fruit distribution, branch and leaf occlusion, illumination variation, manipulator accessibility, harvesting-sequence planning, and multi-agent [...] Read more.
Multi-target harvesting is an important direction for improving the efficiency of orchard harvesting robots and advancing their engineering deployment. Compared with single-target harvesting, multi-target harvesting is constrained by dense fruit distribution, branch and leaf occlusion, illumination variation, manipulator accessibility, harvesting-sequence planning, and multi-agent conflicts. These factors impose higher requirements on visual perception, state representation, decision-making, and task scheduling. This paper focuses on multi-target harvesting tasks in complex orchard environments. It first analyzes task characteristics and constraint sources and then reviews advances in visual perception and scene understanding for target recognition, attribute identification, spatial localization, and spatiotemporal consistency maintenance. Typical decision-making methods are summarized, including rule- and experience-based heuristics, cost-model-based optimization, and data-driven learning approaches. Progress in multi-arm and multi-robot harvesting systems is further reviewed in terms of perception sharing, task allocation, cooperative operation, and conflict avoidance. Continuous and stable perception under complex occlusion, unified scheduling-oriented state representation, real-time decision-making under strong constraints, collision avoidance in shared multi-agent workspaces, and distributed state synchronization remain key challenges. Overall, multi-target harvesting has shown potential to improve orchard operation efficiency, but efficient collaboration and engineering deployment in complex orchard environments still require further breakthroughs. Full article
(This article belongs to the Topic Intelligent Agriculture: Perception Technologies and Agricultural Equipment for Crop Production Processes)
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25 pages, 15131 KB  
Article
Spatiotemporal Evolution of Waterlogging Intensity During Winter Oilseed Rape Growth Stages and Its Relationship with Yield Losses in the Middle-Lower Yangtze River Region
by Long Qian, Huayue Meng, Yunying Luo and Kai Duan
Agriculture 2026, 16(17), 1925; https://doi.org/10.3390/agriculture16171925 - 5 Sep 2026
Viewed by 341
Abstract
Understanding the spatiotemporal evolution of waterlogging intensity across winter oilseed rape (WOSR) growth stages and its quantitative relationship with yield losses is critical for agricultural water management in the Middle-Lower Yangtze River Region (MLYRR). Here, a daily-scale agrometeorological index (SAPEI) was introduced to [...] Read more.
Understanding the spatiotemporal evolution of waterlogging intensity across winter oilseed rape (WOSR) growth stages and its quantitative relationship with yield losses is critical for agricultural water management in the Middle-Lower Yangtze River Region (MLYRR). Here, a daily-scale agrometeorological index (SAPEI) was introduced to quantify waterlogging intensity during four WOSR growth stages from 1961 to 2020. Waterlogging intensity increased significantly over the past six decades in most areas, particularly during the overwintering stage (38 stations, 57% of all stations), with the 2000s representing the historical high levels for most provinces, followed by those in the 1990s. The northeastern and southernmost MLYRR were identified as highly waterlogging-prone regions, and the overwintering stage exhibited substantially larger daily waterlogging intensity than other stages. The yield-reducing impact of severe waterlogging (SAPEI > 1.0) was greater than general waterlogging (SAPEI > 0.5), with larger absolute regression coefficients of waterlogging indices. The flowering–maturing stage, followed by the overwintering stage, was the most waterlogging-sensitive period, and it was also selected as the sole explanatory variable in up to 14 districts in stepwise regression. A marked spatial mismatch was found between waterlogging proneness and crop sensitivity, suggesting that drainage scheduling should account for crop waterlogging sensitivity rather than waterlogging intensity alone. These findings provide guidance for targeted WOSR drainage planning in key regions of the MLYRR under climate change. Full article
(This article belongs to the Section Agricultural Water Management)
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39 pages, 10256 KB  
Review
Advances in Recognition Methods for Fruit and Vegetable Harvesting
by Dianlei Han, Shixing Xu, Siyu Zhou, Qingzhen Zhu and Xuegeng Chen
Agriculture 2026, 16(17), 1924; https://doi.org/10.3390/agriculture16171924 - 5 Sep 2026
Viewed by 511
Abstract
The harvesting of fruit and vegetable crops has long been plagued by prominent issues such as high labor costs, low harvesting efficiency, and high fruit damage rates. The application of object recognition technology has enabled harvesting robots to identify, detect, and locate crops [...] Read more.
The harvesting of fruit and vegetable crops has long been plagued by prominent issues such as high labor costs, low harvesting efficiency, and high fruit damage rates. The application of object recognition technology has enabled harvesting robots to identify, detect, and locate crops in certain agricultural scenarios, achieving a degree of automated harvesting. However, these systems still suffer from shortcomings such as poor robustness in complex environments, insufficient generalization ability, and high model deployment costs, which significantly limit their large-scale application in agricultural harvesting equipment. This paper comprehensively reviews recent literature in the field of fruit and vegetable target recognition. It summarizes how current research focuses on the implementation principles and directions for the improvement of mainstream methods—including digital image processing, traditional machine learning, and deep learning—while also identifying the remaining issues and challenges facing current technology in terms of algorithmic model real-time performance, robustness, and generalization ability. In the future, target recognition technology is expected to achieve breakthroughs through approaches such as multimodal feature fusion, large-scale models, and semi-supervised learning, evolving toward higher accuracy, faster processing speeds, and easier deployment, thereby providing technical support for the large-scale implementation of smart agriculture. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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21 pages, 5946 KB  
Article
Integrated Transcriptomic and Metabolomic Analysis of Cold Stress Network in Interspecific Hybrids Between Chrysanthemum lavandulifolium (Fisch. ex Trautv.) Makino and × morifolium Ramat., cv. ‘Yannong Qiujin’
by Zimeng Li, Chunxin Dong, Hongbo Liu, Xin Liu, Beining Zhang, Jingwen Quan, Xinhui Ma, Li Zhao and Ri Gao
Agriculture 2026, 16(17), 1923; https://doi.org/10.3390/agriculture16171923 - 5 Sep 2026
Viewed by 397
Abstract
Groundcover chrysanthemums exhibit inherently poor low-temperature adaptability, making them highly vulnerable to cold stress and thereby constraining their large-scale regional cultivation. Interspecific hybridization was conducted between diploid Chrysanthemum lavandulifolium (Fisch. ex Trautv.) Makino (pollen donor) and hexaploid cultivar Chrysanthemum × morifolium Ramat., cv. [...] Read more.
Groundcover chrysanthemums exhibit inherently poor low-temperature adaptability, making them highly vulnerable to cold stress and thereby constraining their large-scale regional cultivation. Interspecific hybridization was conducted between diploid Chrysanthemum lavandulifolium (Fisch. ex Trautv.) Makino (pollen donor) and hexaploid cultivar Chrysanthemum × morifolium Ramat., cv. ‘Yannong Qiujin’ (seed parent) to generate cold-tolerant groundcover chrysanthemum germplasm. Comprehensive physiological profiling and integrated multi-omics analyses were performed on both parental plants and their hybrid progeny to identify candidate molecular pathways associated with cold adaptation in ploidy-divergent Chrysanthemum hybrids. Hybrid progeny exhibited significantly lower semi-lethal temperature (LT50), malondialdehyde (MDA) content, and relative electrical conductivity compared with the female parent, indicating heterosis-mediated enhancement of cellular membrane integrity. Integrated transcriptomic and metabolomic profiling identified significant enrichment of glycerophospholipid metabolism in the hybrid progeny. Upregulated expression of glycerol-3-phosphate acyltransferase (GPAT) and lysophosphatidic acid acyltransferase (LPAAT) genes promoted substantial accumulation of lysophosphatidic acid (LPA) and phosphatidic acid (PA). We found that higher expression levels of GPAT and LPAAT were positively associated with increased accumulation of LPA and PA. PA further contributes to phosphatidylcholine (PC) synthesis, potentially improving plasma-membrane permeability and sustaining plasma membrane integrity under chilling stress. Meanwhile, transcript abundance of the inducer of CBF expression (ICE), cold-regulated (COR) genes were markedly elevated. This study identifies candidate molecular pathways underlying cold adaptation in hybrid progeny derived from ploidy-divergent Chrysanthemum, thereby providing a robust theoretical foundation for breeding novel cold-tolerant chrysanthemum cultivars. Full article
(This article belongs to the Topic Plant Breeding, Genetics and Genomics, 2nd Edition)
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16 pages, 1907 KB  
Article
White-Rot Mushrooms as Promising Biocontrol Agents Against Fusarium Head Blight in Wheat
by Žarko Ivanović, Jasmina Ćilerdžić, Tijana Jeremić, Danica Žeželj, Milica Galić, Carla Ceoloni, Ljiljana Kuzmanović and Mirjana Stajić
Agriculture 2026, 16(17), 1922; https://doi.org/10.3390/agriculture16171922 - 4 Sep 2026
Viewed by 472
Abstract
Whether the extracts of Fomes fomentarius, Ganoderma applanatum and Trametes versicolor can inhibit the growth of Fusarium graminearum in vitro, reduce the severity of Fusarium head blight (FHB) and reduce fungal colonization of wheat seeds artificially infected with this pathogen were the [...] Read more.
Whether the extracts of Fomes fomentarius, Ganoderma applanatum and Trametes versicolor can inhibit the growth of Fusarium graminearum in vitro, reduce the severity of Fusarium head blight (FHB) and reduce fungal colonization of wheat seeds artificially infected with this pathogen were the questions that initiated this research. The mycelial growth of F. graminearum was significantly reduced after treatment with ethanol extracts of the studied white-rot fungi, from 43.1% with F. fomentarius to 53.2% with T. versicolor. Likewise, these extracts significantly reduced disease development in artificially infected wheat seeds. Disease severity was quantified using the Disease Severity Index (DSI). In fact, after artificial infection with F. graminearum of lines carrying effective resistance (Fhb7 locus) from an alien source and their treatment with mushroom extracts, disease severity was negligible, and seedling fitness was very high. The highest efficacy of 99.49% was recorded in the resistant line R6-1, carrying the Fhb7 allele from Thinopyrum elongatum (7E) following treatment with the T. versicolor extracts. Slightly lower efficacy values were obtained after treatment with G. applanatum (97.83%) and F. fomentarius (97.47%). The obtained results showed that the selected mushroom extracts can be used in F. graminearum control and support successful wheat cultivation in FHB-prone areas. Full article
(This article belongs to the Special Issue Application of Biological Control in Crop Protection)
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25 pages, 12500 KB  
Article
Potato Planting Quality Detection and Reseeding System Based on Lightweight Detection and Multi-Frame Decision Making
by Kaiqi Liu, Rao Zhou, Gang Sun, Hua Zhang, Xiaolong Liu, Hui Li and Wei Sun
Agriculture 2026, 16(17), 1921; https://doi.org/10.3390/agriculture16171921 - 4 Sep 2026
Viewed by 366
Abstract
Automatic reseeding in spoon-chain potato planters requires reliable classification of miss-seeding, normal, and multiple-seeding events. Oblique viewing and continuous spoon motion cause scale changes, boundary truncation, blur, and occlusion, which destabilize single-frame predictions. The proposed system treats each seed-spoon passage as one control [...] Read more.
Automatic reseeding in spoon-chain potato planters requires reliable classification of miss-seeding, normal, and multiple-seeding events. Oblique viewing and continuous spoon motion cause scale changes, boundary truncation, blur, and occlusion, which destabilize single-frame predictions. The proposed system treats each seed-spoon passage as one control event. It combines lightweight detection, track-level learning, and multi-frame classification with air-blow clearing and missed-seed reseeding. Based on the target-size distribution, the detector removes the P3 head from YOLOv8n and restores shallow details through space-to-depth rearrangement and gated addition. Frame quality combines boundary distance, sharpness, adjacent-box intersection over union, and box-area stability. High-quality frames form a track prototype, while low-quality frames receive stronger constraints. A quality-gated temporal network classifies each spoon as containing 0, 1, or at least 2 seed potatoes. Each model was trained with three random seeds under the same data split. Compared with YOLOv8n, the detector reduced the parameter count and computational cost by 35.8% and 48.3%, respectively. Track-level learning improved event accuracy by 3.67 percentage points over frame-level training. The seven-frame model achieved 93.41% event accuracy and 93.80% macro-F1. At 0.5 m/s, the closed-loop recognition, air-blow, and reseeding success rates were 93.7%, 96.4%, and 97.1%, respectively. These results provide a practical basis for real-time, event-level planting-quality control in spoon-chain potato planters. Full article
(This article belongs to the Special Issue Intelligent Agricultural Seeding Equipment)
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34 pages, 1671 KB  
Article
Toward More Resilient Agricultural Enterprises: Integrating Agricultural Environment Information into Explainable Financial Distress Prediction
by Dominika Gajdosikova
Agriculture 2026, 16(17), 1920; https://doi.org/10.3390/agriculture16171920 - 4 Sep 2026
Viewed by 469
Abstract
Financial vulnerability in agriculture is shaped by both enterprise-specific factors and the agricultural systems in which firms operate, challenging financial distress prediction models based exclusively on firm-level financial metrics. This study assesses the predictive performance of logistic regression (LR), extreme gradient boosting (XGBoost), [...] Read more.
Financial vulnerability in agriculture is shaped by both enterprise-specific factors and the agricultural systems in which firms operate, challenging financial distress prediction models based exclusively on firm-level financial metrics. This study assesses the predictive performance of logistic regression (LR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and category boosting (CatBoost), examines whether country-level agricultural environment characteristics improve prediction beyond traditional financial indicators, and uses Shapley additive explanations (SHAP) to analyze predictor contributions. The empirical analysis is based on 28,745 firm-year observations of agricultural enterprises in the Visegrad Group (V4) countries over 2019–2024. Adding agricultural environment variables produced a modest but consistent improvement in predictive performance, increasing AUC from approximately 0.854–0.855 in firm-level boosting models to 0.864–0.865 in integrated specifications. All three gradient boosting algorithms outperform conventional LR, although differences among XGBoost, LightGBM, and CatBoost remain statistically insignificant. SHAP analysis identifies liquidity, leverage, profitability, and firm size as the primary predictors while revealing nonlinear and threshold-dependent relationships not captured by conventional linear models. Integrating agricultural environment characteristics with explainable machine learning (ML) therefore supports more comprehensive, transparent, and context-aware early-warning systems for agricultural financial distress. Full article
(This article belongs to the Special Issue Competitiveness, Productivity, and Efficiency in the Agricultural Market)
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34 pages, 31934 KB  
Article
Dynamic-Headland-Aware Coverage Path Planning for Ground-Based Plant Protection Operations in Irregular Fields Using DQN-SHADE
by Tuo Sun, Xiaoyi Liu, Zhixin Yao, Taihong Zhang and Yanlin Xin
Agriculture 2026, 16(17), 1919; https://doi.org/10.3390/agriculture16171919 - 4 Sep 2026
Viewed by 442
Abstract
Fixed full-boundary headlands create redundant non-working space in irregular fields, while working direction and swath offset jointly affect coverage quality and inter-swath turning cost. This study proposes a dynamic-headland-aware coverage path planning framework that allocates headland space according to swath endpoints and turning [...] Read more.
Fixed full-boundary headlands create redundant non-working space in irregular fields, while working direction and swath offset jointly affect coverage quality and inter-swath turning cost. This study proposes a dynamic-headland-aware coverage path planning framework that allocates headland space according to swath endpoints and turning demand and generates parallel working swaths and Bézier U-turns for each direction–offset candidate. Field-specific empirical cumulative distribution function (ECDF)-midrank normalization converts turning distance, coverage error, and headland ratio into relative quality scores, and DQN-SHADE searches the resulting non-smooth discrete evaluation landscape. In geometric simulations on 24 actual field boundaries, DQN-SHADE achieved the lowest mean gap to the discrete reference optimum (0.0034) and the highest threshold success rate, SR5×10−3, of 80.83% among six stochastic optimizers under a common candidate-evaluation budget. Relative to fixed full-boundary headlands, dynamic headland allocation reduced the mean headland ratio from 13.30% to 7.52%, increased retained working area by 16,378.23 m2 per field, and maintained 98.16% mean coverage. The proposed framework improves field-space utilization while maintaining coverage quality and geometric feasibility under the evaluated simulation conditions. Full article
(This article belongs to the Section Agricultural Technology)
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26 pages, 5314 KB  
Article
Climate–Yield Relationships and Climate Resilience of Regional Alfalfa Production Systems in Romania
by Claudiu Andrei Badea, Ana Maria Stanciu and Paula Stoicea
Agriculture 2026, 16(17), 1918; https://doi.org/10.3390/agriculture16171918 - 4 Sep 2026
Viewed by 468
Abstract
Climate variability can affect forage production through changes in temperature, precipitation, and the frequency of stressful weather conditions. We examined climatic variability and alfalfa (Medicago sativa L.) productivity in three Romanian development regions—South-Muntenia, South-East, and North-East—over 2006–2024. The climatic variables included mean, [...] Read more.
Climate variability can affect forage production through changes in temperature, precipitation, and the frequency of stressful weather conditions. We examined climatic variability and alfalfa (Medicago sativa L.) productivity in three Romanian development regions—South-Muntenia, South-East, and North-East—over 2006–2024. The climatic variables included mean, maximum, and minimum air temperature, annual precipitation, and maximum 24 h precipitation. Regional relationships between climatic variables and alfalfa yield were examined using descriptive statistics, Kendall’s rank-based trend analysis with Sen’s slope, Pearson correlation, and multiple linear regression with diagnostic testing. We also developed an exploratory Alfalfa Climate Resilience Index (ACRI) to compare relative climate-resilience profiles of regional alfalfa production systems. The index combines two performance-related components, normalized mean yield and yield stability, with two climatic-context components, normalized precipitation and inverse normalized mean temperature. Its sensitivity to alternative component weights was also evaluated. Mean annual temperature increased significantly in all regions, with Sen’s slopes ranging from +0.071 to +0.100 °C year−1, whereas annual precipitation showed no significant monotonic trend. Yield slopes were negative in all regions, but only North-East showed a statistically significant decline (τ = −0.794, p < 0.001). Mean annual temperature was negatively correlated with yield in all three regions (r = −0.617 to −0.749), whereas the precipitation indicators showed no significant bivariate relationships with yield. The regional multiple regression models accounted for 64.3–85.1% of the observed variation in annual yield (R2 = 0.643–0.851), with the relative importance and statistical significance of climatic predictors differing among regions. ACRI ranked North-East first (0.760), South-Muntenia second (0.469), and South-East third (0.295), and this ordering remained unchanged across the alternative weighting scenarios examined. These scores represent relative differences among the three regional production systems within the study dataset and should not be interpreted as absolute measures of intrinsic alfalfa resilience. The findings show that climate–yield relationships vary among regions and that temperature was more consistently associated with yield than the precipitation indicators considered here. ACRI is therefore presented as an exploratory, dataset-dependent framework for within-dataset comparison of regional alfalfa production-system climate resilience and requires validation using broader reference datasets, common normalization criteria, and additional management and environmental variables before wider application. Full article
(This article belongs to the Special Issue Farming System Resilience in a Changing Climate: Implications for Land Use and Sustainability)
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29 pages, 20694 KB  
Article
Inoculation with Bacillus velezensis UFV 3918 Promotes Early Growth and Nutrient Uptake in Sugarcane Under Reduced Phosphorus Fertilization
by Hariane Luiz Santos and Marcelo de Almeida Silva
Agriculture 2026, 16(17), 1917; https://doi.org/10.3390/agriculture16171917 - 4 Sep 2026
Viewed by 461
Abstract
Phosphorus (P) is one of the main limiting factors for sugarcane establishment and longevity in tropical soils, requiring biotechnological strategies to improve fertilizer use efficiency. This study evaluated the effects of inoculation with Bacillus velezensis UFV 3918, alone or combined with reduced monoammonium [...] Read more.
Phosphorus (P) is one of the main limiting factors for sugarcane establishment and longevity in tropical soils, requiring biotechnological strategies to improve fertilizer use efficiency. This study evaluated the effects of inoculation with Bacillus velezensis UFV 3918, alone or combined with reduced monoammonium phosphate (MAP) doses, on the morphological, nutritional, and root nutrient uptake responses of sugarcane grown in a dystrophic Red Latosol under greenhouse conditions. The experiment was conducted in a completely randomized design with six treatments and four replicates: absolute control (AC, without MAP), commercial control (CC, recommended MAP dose), B. velezensis alone (Bv), and Bv combined with 1/3, 2/3, or the full recommended MAP dose. Plant growth responses varied across treatments and stages, with Bv promoting greater leaf area than the CC at 120 and 180 DAP, whereas Bv + 1/3 MAP maintained leaf area comparable to the CC throughout the evaluation period. Principal component analysis (PCA) revealed a clear separation among treatments, with Bv exhibiting the most distinct plant response, primarily associated with greater stalk diameter, root and stalk biomass, higher concentrations of potassium and phosphorus in stalks, sulfur and magnesium in roots, and greater boron-use efficiency. Bv + 1/3 MAP displayed an intermediate multivariate profile, mainly associated with phosphorus uptake per unit root length and nutritional attributes related to phosphorus acquisition under reduced fertilizer supply. Correlation analysis further revealed strong positive associations among growth-related traits, biomass accumulation, and phosphorus-related variables, indicating a close relationship between P acquisition and plant responses to bacterial inoculation. Overall, inoculation with B. velezensis UFV 3918 improved early sugarcane growth and nutrient acquisition, and its combination with 1/3 of the recommended MAP rate maintained plant performance. These findings indicate the potential for reducing mineral P inputs during early sugarcane establishment; however, long-term field trials are required to determine whether these responses can be sustained throughout the crop cycle and under commercial production conditions. Full article
(This article belongs to the Special Issue Using Microbial Inoculants to Improve Soil Quality and Enhance Crop Growth)
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34 pages, 13776 KB  
Systematic Review
How Climate, Soil Conditions, and Biochar Characteristics Influence Crop Yield, Nitrogen and Phosphorus Fertilizer Productivity, and Economic Returns Across Cereals and Legumes: A Global Meta–Analysis
by Rose Nimoh Serwaa, Aaron Ohene Boanor, Dain Hong, Yuna Chang, Taek-Keun Oh and Jwakyung Sung
Agriculture 2026, 16(17), 1916; https://doi.org/10.3390/agriculture16171916 - 4 Sep 2026
Viewed by 710
Abstract
Biochar application is widely proposed to improve crop yield and fertilizer productivity, yet its performance varies across climatic, soil, and management contexts. This global meta–analysis study of about 705 database entries retrieved from 93 studies analyzed responses across climatic classification, biochar feedstock and [...] Read more.
Biochar application is widely proposed to improve crop yield and fertilizer productivity, yet its performance varies across climatic, soil, and management contexts. This global meta–analysis study of about 705 database entries retrieved from 93 studies analyzed responses across climatic classification, biochar feedstock and properties, soil properties, fertilizer and co–application regimes, and experimental types. Overall, biochar responded at 33%, 34%, and 32% for yield, PFPN, and PFPP. The strongest response occurred at the tropical, humid agro–ecological zone under moderate temperature regimes. Wood-derived biochar with lower pyrolysis temperatures (<400 °C), combined with moderate soil carbon content and nitrogen, produced a greater improvement. Organic + biochar co–applied fertilizer application rates of ≤50 kg N ha–1 and 50–100 kg P2O5 ha–1 increased the response, while excessive fertilizer rates (>200 kg ha) showed reduced response. Economic benefit analysis also showed the average overall highest maximum yield at ≤40–80 t ha–1 biochar rates demonstrating simultaneous improvement in yield and fertilizer productivity under appropriate management. Full article
(This article belongs to the Section Agricultural Soils)
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16 pages, 10841 KB  
Article
Strip Tillage with Straw Retention Alters the Associations Between Maize Yield Components and Grain Yield Across Seven Soil Types
by Binghao Li, Long Zhang, Liang Wang, Lichun Wang and Jinsheng Yang
Agriculture 2026, 16(17), 1915; https://doi.org/10.3390/agriculture16171915 - 4 Sep 2026
Viewed by 390
Abstract
Soil degradation constrains cropland productivity in Northeast China, where conservation tillage has been widely adopted to improve soil structure and sustain maize (Zea mays L.) production. However, previous studies have focused mainly on single soil types or regional-scale assessments, and the applicability [...] Read more.
Soil degradation constrains cropland productivity in Northeast China, where conservation tillage has been widely adopted to improve soil structure and sustain maize (Zea mays L.) production. However, previous studies have focused mainly on single soil types or regional-scale assessments, and the applicability of conservation tillage across contrasting cultivated soil types remains inadequately characterized. From 2021 to 2023, field experiments were conducted across geo-ecological zones within mid-temperate sub-humid and mid-temperate sub-arid climatic regions, encompassing seven typical cultivated soil types: Aeolian sandy soil, Bielic, Black soil, Chernozem, Histosol, Inceptisol, and Mollisols. A strip-tillage with straw return treatment (ST) was compared against no-tillage with straw mulching treatment (CK) to systematically evaluate the effects of strip-tillage on (i) soil physical properties (bulk density and porosity), (ii) soil chemical properties (organic matter and total nutrient contents), and (iii) maize yield components. Across the full dataset, ST significantly reduced soil bulk density and increased total porosity relative to CK; however, soil-type-specific comparisons showed that significant responses were detected only in some soil types. By contrast, soil chemical properties were significantly influenced by the treatment × soil type interaction: SOM and STN contents increased significantly in Inceptisol under ST, STP increased significantly in Mollisols, and STK increased significantly in Histosol. A PCA-derived composite physicochemical score, based on six standardized soil indicators, was higher under ST than CK in six of the seven soil types relative to CK, with the most pronounced improvements occurring in Aeolian sandy soil (+1.23), Bielic (+0.87), and Black soil (+0.65); significant maize yield increases under ST were detected in Aeolian sandy soil and Black soil, with mean increases of 7.07% and 10.94%, respectively. The associations between maize yield components and grain yield differed between ST and CK, with 1000-kernel weight showing a stronger association with grain yield under ST. Collectively, these findings indicate that ST can improve soil physical structure, but its effects on soil chemical properties, maize yield, and yield-component associations are soil-type dependent. Full article
(This article belongs to the Special Issue Cropping and Tillage Systems Impacts on Soil Physical Quality)
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18 pages, 8090 KB  
Article
PlTUB1 Negatively Regulates Stem Development by Modulating Microtubule Organization and Cellulose Deposition in Herbaceous Peony
by Jiajia Zuo, Tai Chen, Yuhan Tang, Daqiu Zhao and Jun Tao
Agriculture 2026, 16(17), 1914; https://doi.org/10.3390/agriculture16171914 - 4 Sep 2026
Viewed by 309
Abstract
Stem development determines plant architecture and mechanical support, directly affecting yield, lodging resistance, and ornamental quality. Microtubules are essential components of the plant cytoskeleton, but their role in stem development in herbaceous peony (Paeonia lactiflora Pall.) remains largely unexplored. Here, a β-tubulin [...] Read more.
Stem development determines plant architecture and mechanical support, directly affecting yield, lodging resistance, and ornamental quality. Microtubules are essential components of the plant cytoskeleton, but their role in stem development in herbaceous peony (Paeonia lactiflora Pall.) remains largely unexplored. Here, a β-tubulin gene, PlTUB1, was identified from P. lactiflora. Its coding sequence was 1353 bp in length, encoding a 450-amino-acid protein that localized to microtubules, and its transcript levels declined progressively during stem development. Transient virus-induced gene silencing of PlTUB1 in P. lactiflora, with a silencing efficiency of approximately 55%, enhanced stem development, with increased stem diameter, elevated cellulose accumulation, and thickened secondary walls. Given that cellulose deposition and secondary wall thickening were closely associated with cortical microtubule organization, the role of PlTUB1 in microtubule regulation was further examined. Heterologous overexpression of PlTUB1 in Arabidopsis thaliana disrupted transverse microtubule alignment, reduced cellulose deposition, and impaired secondary wall and xylem development, thereby confirming the mechanistic link between PlTUB1 and microtubule organization. Collectively, these findings demonstrated that PlTUB1 negatively regulates stem development by modulating microtubule organization and secondary wall deposition, providing new insights into the cytoskeletal regulation of stem development in P. lactiflora. Full article
(This article belongs to the Special Issue Feature Papers in Crop Genetics, Genomics and Breeding)
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19 pages, 2576 KB  
Article
Response of Native Grassland Plants to Ageratina adenophora Stress: Seedling Growth Inhibition and Antioxidant Defense
by Longyuan Zhao, Lirong Guan, Xiufeng Huang, Zhen Wang, Cuixian Shi, Yang Wang, Dexi Wu and Yong Xie
Agriculture 2026, 16(17), 1913; https://doi.org/10.3390/agriculture16171913 - 4 Sep 2026
Viewed by 407
Abstract
Ageratina adenophora is a highly aggressive weed that originates from central Mexico and Costa Rica. It has invaded and become naturalized across tropical and subtropical regions, posing significant challenges for biodiversity conservation and ecological restoration. While substantial research has revealed its impacts on [...] Read more.
Ageratina adenophora is a highly aggressive weed that originates from central Mexico and Costa Rica. It has invaded and become naturalized across tropical and subtropical regions, posing significant challenges for biodiversity conservation and ecological restoration. While substantial research has revealed its impacts on diverse ecosystems and enhanced understanding of its phytotoxicity, investigations in grassland landscapes remain limited. Consequently, this study focused on Chengjiang County in southwestern China, a region heavily invaded by A. adenophora. Based on a preliminary survey, five grassland plant species that commonly co-occur and compete with it were assessed through seedling growth bioassays and physiological measurements conducted under the treatments of its aqueous tissue extract. The findings revealed that the recipient plants exhibited dynamic changes dependent on concentration. Specifically, there was a negative correlation between the content of malondialdehyde (MDA) and proline (Pro) and seedling height as well as root length (p < 0.05; p < 0.01). This suggests that higher extract concentrations triggered more pronounced stress-related cellular responses, which were concurrently associated with diminished seedling growth. Notably, Saccharum arundinaceum demonstrated the most significant elevation in peroxidase (POD) and catalase (CAT) activities, along with the highest synthetical allelopathic effect (SE) index of −0.36, whereas Rumex hastatus followed with an SE index of −0.38, indicating that S. arundinaceum was the least sensitive to A. adenophora stress. Our research findings elucidate the responses of indigenous grassland plants to A. adenophora, offering valuable insights into the screening of tolerant native grassland species and developing vegetation replacement strategies aimed at restoring invaded grasslands. Full article
(This article belongs to the Special Issue Ecology, Evolution, and Management of Agricultural Weeds)
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40 pages, 2823 KB  
Article
Digitalizing Sustainability Assessment in Agriculture: The SUSTIN-OLIVA Indicator-Based Application for Tunisian Olive Farming Systems Under Agroecological Transition
by Saida Elfkih, Amel ElKadri, Olfa Boussaadia, Houda Sahnoun, Aymen Laabidi, Ghayth Ghozzi, Sana Bouazza, Ahmed Charfi, Oussema Charfi and Idalina Sardinha
Agriculture 2026, 16(17), 1912; https://doi.org/10.3390/agriculture16171912 - 3 Sep 2026
Viewed by 359
Abstract
With the shift in Tunisian olive growing toward more intensive models, significant sustainability concerns have emerged. Aware of the need for reliable and operational tools to support agricultural policies under climate and socioeconomic pressures, we address a digitalized methodological framework for measuring and [...] Read more.
With the shift in Tunisian olive growing toward more intensive models, significant sustainability concerns have emerged. Aware of the need for reliable and operational tools to support agricultural policies under climate and socioeconomic pressures, we address a digitalized methodological framework for measuring and assessing the sustainability of olive farming systems that are undergoing an agroecological transition. The main contribution lies in the development of the SUSTIN-OLIVA digital indicator-based application, designed as a decision-support tool to enhance the accessibility, scalability, and operationalization of sustainability assessment within a holistic approach that integrates environmental, economic, and socio-territorial indicators tailored to Tunisian olive agroecosystems. The methodology was tested on 61 Tunisian olive farms in two agroecological zones undergoing transition, using face-to-face surveys conducted between April and May 2026 with a structured questionnaire. Principal Component Analysis (PCA) was applied as an exploratory approach to examine the relationships, contribution patterns, and internal structure of the proposed sustainability indicators. The results indicated that, among the 28 indicators considered, 23 made substantial contributions to the main patterns of variation identified by the PCA in the studied context. These findings demonstrate the feasibility and operational applicability of the SUSTIN-OLIVA framework across heterogeneous olive farms and provide insights into differences in sustainability performance and indicator interactions. The framework also enables the identification of under-addressed sustainability dimensions, supporting more targeted interventions and context-specific decision-making. Overall, this work advances digitalized sustainability assessment by linking a holistic indicator framework with an operational digital decision-support tool, thereby supporting adaptive governance in the Mediterranean olive sector. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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27 pages, 12087 KB  
Article
YOLOv11n-SWAQ: A Lightweight Adaptive Compression Method for Agricultural Images
by Kaixuan Zhao, Yue Zhang, Yinan Chen and Jiangtao Ji
Agriculture 2026, 16(17), 1911; https://doi.org/10.3390/agriculture16171911 - 3 Sep 2026
Viewed by 403
Abstract
Agricultural pest and disease images often lose diagnostically important textures during transmission and storage, while conventional codecs struggle to balance high compression ratios and information fidelity. This study proposes YOLOv11n-SWAQ, a lightweight adaptive compression framework integrating shared feature encoding and critical-region-aware quantization. The [...] Read more.
Agricultural pest and disease images often lose diagnostically important textures during transmission and storage, while conventional codecs struggle to balance high compression ratios and information fidelity. This study proposes YOLOv11n-SWAQ, a lightweight adaptive compression framework integrating shared feature encoding and critical-region-aware quantization. The YOLOv11n backbone and feature fusion network are reused as a shared encoder to reduce redundant feature extraction. A lightweight window-based attention module enhances lesion boundaries, insect contours, and local textures. Detection boxes and attention weights are combined to construct a three-level quantization map for background, ordinary critical, and high-importance texture regions, while a hyperprior entropy model improves latent probability estimation. At an actual complete-stream bitrate of approximately 0.31 bpp, the proposed method achieved an ROI-PSNR of 36.970 dB and an ROI-MS-SSIM of 0.9652. Compared with the YOLOv11n+CAE cascaded baseline, the reconstruction error in complex-texture regions decreased by 4.8%, indicating preferential bitrate allocation to diagnostically important regions. The proposed method achieved an end-to-end processing time of 13.44 ms, representing an average reduction of approximately 72% compared with representative advanced learned image compression models. Overall, YOLOv11n-SWAQ provides a favorable trade-off between critical-region fidelity and computational efficiency for high-ratio agricultural image compression, with its principal advantage lying in lightweight task-oriented processing rather than a large absolute gain in global reconstruction quality. Full article
(This article belongs to the Special Issue Advanced Image Collection, Processing, and Analysis in Crop Management—2nd Edition)
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28 pages, 32600 KB  
Article
Three-Dimensional Open-Path Sequencing for Robotic Tea-Shoot Plucking via Hierarchical Ant Colony Optimization and Endpoint Coordination
by Decheng Liu, Baijuan Wang, Pengfei Wang, Zhi Zhang and Yongguang Hu
Agriculture 2026, 16(17), 1910; https://doi.org/10.3390/agriculture16171910 - 3 Sep 2026
Viewed by 376
Abstract
Efficient three-dimensional (3D) open-path sequencing is important for robotic selective plucking because harvestable tea shoots are irregularly distributed in canopy space. This study formulates tea-shoot plucking as an open-path sequencing problem over preparatory plucking points and proposes a hierarchical ant colony optimization framework [...] Read more.
Efficient three-dimensional (3D) open-path sequencing is important for robotic selective plucking because harvestable tea shoots are irregularly distributed in canopy space. This study formulates tea-shoot plucking as an open-path sequencing problem over preparatory plucking points and proposes a hierarchical ant colony optimization framework with endpoint-aware traversal-direction coordination. Preparatory points are decomposed into local operating regions, within which routes are optimized using ant colony optimization and 3-opt refinement. Centroid-level ant colony optimization then determines the cluster visiting order, followed by dynamic programming to select the traversal direction of each local route according to endpoint-to-endpoint connection costs. Across eleven simulated scenarios, including an unstructured Global-random control, the proposed method achieved the shortest mean route length in ten scenarios. In the Large-scale scenario, it achieved a mean route length of 13.973 ± 0.184 m, which was 10.5% and 2.9% lower than those obtained by global ant colony optimization and greedy insertion, respectively. On five real tea-field point-cloud datasets, the proposed method reduced mean route length by 0.57–3.15% relative to greedy insertion, with statistically significant improvements in four datasets. A preliminary CoppeliaSim assessment further showed that the optimized visiting sequence could be geometrically connected using smoothed local transfer trajectories. Overall, the proposed framework improves sequence-level route quality and computational efficiency for clustered 3D tea-shoot distributions, while its field-level operational benefits remain to be established through closed-loop robotic validation. Full article
(This article belongs to the Section Agricultural Technology)
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26 pages, 868 KB  
Article
Metabolic and Physiological Associations with LMD-Based Enteric CH4 Concentration in Early Lactation Dairy Cows: A Multivariate Approach with Parity-Specific Insights
by Justina Krištolaitytė, Karina Džermeikaitė, Samanta Grigė, Akvilė Girdauskaitė, Greta Šertvytytė, Gabija Lembovičiūtė, Arūnas Rutkauskas and Ramūnas Antanaitis
Agriculture 2026, 16(17), 1909; https://doi.org/10.3390/agriculture16171909 - 3 Sep 2026
Viewed by 402
Abstract
Understanding cow-level factors associated with enteric methane (CH4) is important for precision-based methane mitigation in dairy systems. This study evaluated associations between laser methane detector (LMD)-based CH4 concentration, relative CH4 concentration-based indices, biochemical markers, sensor-derived variables, and milk traits [...] Read more.
Understanding cow-level factors associated with enteric methane (CH4) is important for precision-based methane mitigation in dairy systems. This study evaluated associations between laser methane detector (LMD)-based CH4 concentration, relative CH4 concentration-based indices, biochemical markers, sensor-derived variables, and milk traits in clinically healthy Holstein cows, with an emphasis on parity. Ninety-one cows within 100 days in milk (DIM) were examined: 46 primiparous and 45 multiparous cows. Methane concentration was measured using a portable LMD. Data were evaluated using parity comparisons, principal component analysis (PCA), DIM-adjusted multiple linear regression with parity interaction terms, and DIM-adjusted partial correlation analyses with Benjamini–Hochberg false discovery rate (FDR) correction. Mean CH4 concentration did not differ between primiparous and multiparous cows (370.83 vs. 361.16 ppm; p = 0.765), despite higher estimated group-level dry matter intake (DMI; +11.2%) and milk yield (+23.1%) in multiparous cows. The milk-yield-adjusted CH4 concentration index was higher in primiparous cows (11.26 vs. 8.91 ppm CH4/kg milk; p = 0.022). In the primary PCA-based regression model, the energy metabolism/lipid mobilisation component showed a positive but non-significant association with CH4 concentration (p = 0.077), while the overall model’s explanatory capacity was limited (R2 = 0.105; adjusted R2 = 0.018). Exploratory DIM-adjusted subgroup analyses identified associations involving non-esterified fatty acids (NEFA), triglycerides, serum iron, and milk lactose that remained significant after FDR correction. However, formal predictor × parity interaction analyses did not confirm effect modification by parity. These findings suggest that LMD-based CH4 concentration is best interpreted within a broader metabolic, behavioural, and production context and warrants validation in larger longitudinal studies. Full article
(This article belongs to the Special Issue Effects of Feed on Methane Emissions and Microbial Activity in the Rumen of Ruminants)
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20 pages, 3984 KB  
Article
Environmental Drivers and Spatial Suitability of Erosion Control Measures in the Black Soil Region of Northeast China
by Zheng Chen, Yan Xu, Huarui Gong, Yitao Zhang, Jiaxu Fu, Lei Zhang and Jing Li
Agriculture 2026, 16(17), 1908; https://doi.org/10.3390/agriculture16171908 - 3 Sep 2026
Viewed by 462
Abstract
Sloping farmland erosion in cold-region black soil zones threatens global agriculture, yet regional-scale quantitative evidence on the erosion mitigation performance and spatial suitability of control measures remains lacking. This study integrated 959 field observation datasets from 132 peer-reviewed publications, and used meta-analysis and [...] Read more.
Sloping farmland erosion in cold-region black soil zones threatens global agriculture, yet regional-scale quantitative evidence on the erosion mitigation performance and spatial suitability of control measures remains lacking. This study integrated 959 field observation datasets from 132 peer-reviewed publications, and used meta-analysis and Boosted Regression Tree (BRT) models to evaluate tillage, biological, engineering, and combined conservation measures in Northeast China. Results showed that all measures significantly reduced erosion, achieving an average runoff reduction of 73.2% and sediment reduction of 83.6%. Specifically, engineering measures showed the highest runoff reduction, while combined measures achieved more than 95% sediment reduction. Machine learning revealed that runoff reduction was primarily regulated by precipitation, whereas sediment reduction was mainly controlled by soil clay content and bulk density. Spatially, tillage, biological, and engineering measures showed the highest suitability in continuous cultivated plains, plain–hill transition zones, and low hilly and gully regions, respectively. This study moves erosion-control assessment beyond comparisons of average runoff and sediment reduction rates toward environmentally matched spatial allocation, providing a basis for targeted black soil conservation and resilient grain production in cold-region agricultural landscapes. Full article
(This article belongs to the Special Issue Soil Erosion Mechanisms and Water Conservation Processes in Farmland)
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29 pages, 13001 KB  
Article
Design and Testing of a Gravity-Constrained Fertilizer Guide Tube for Stable Fertilizer Cluster Movement
by Xinhe Shan, Jianjun Dong, Bingxin Yan, Liwei Li, Yanxin Yin, Qingzhen Zhu, Chunhong Dong and Guangwei Wu
Agriculture 2026, 16(17), 1907; https://doi.org/10.3390/agriculture16171907 - 3 Sep 2026
Viewed by 380
Abstract
Fertilizer clusters tend to lose their agglomerated state upon ground contact, which reduces fertilizer use efficiency. To address this, we designed a gravity-constrained fertilizer guide tube. Based on the principle of minimum friction, we designed a trapezoidal groove. By analyzing the motion of [...] Read more.
Fertilizer clusters tend to lose their agglomerated state upon ground contact, which reduces fertilizer use efficiency. To address this, we designed a gravity-constrained fertilizer guide tube. Based on the principle of minimum friction, we designed a trapezoidal groove. By analyzing the motion of the fertilizer as it dropped and contacted the ground, we identified the key factors affecting hole formation such as the fertilizer guiding angle, groove angle, and forward velocity. We used the discrete element method to determine the optimal combination of parameters: with a fertilizer guiding angle of 60°, a groove angle of 80°, and a forward velocity of 6 km/h, the average hole length was 87.6 mm, the coefficient of variation of hole length was 6.51%, and the coefficient of variation of hole spacing was 1.76%. Bench test results indicated that under conditions of a target fertilizer dosage per hole of 6.0 g, a forward velocity of 4–10 km/h, and a target hole spacing of 0.20–0.30 m, the fertilizer clusters consistently maintained their agglomerated state during descent. Field test results showed that under conditions of a target fertilizer dosage per hole of 2.0–8.0 g, a forward velocity of 4–10 km/h, and a target hole spacing of 0.20–0.30 m, the average hole length ranged from 94.5 to 223.0 mm, with a coefficient of variation of hole length of 7.17–11.16% and a coefficient of variation of hole spacing of 2.95–6.06%. Although hole formation performance was somewhat reduced in the field, the fertilizer in each hole remained in an aggregated state and was distributed uniformly. The collision between the fertilizer and the soil was the key factor affecting hole formation. The results of this study can provide insights and references for the design and optimization of fertilizer-hole-applied discharge devices. Full article
(This article belongs to the Section Agricultural Technology)
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Review
Bionic Thermodynamic Model of the Stomatal System of a Plant Leaf: Energy Transformation and CO2 Metabolism
by Tomas Ūksas and Simona Paulikienė
Agriculture 2026, 16(17), 1906; https://doi.org/10.3390/agriculture16171906 - 3 Sep 2026
Viewed by 370
Abstract
The mechanisms of plant leaf gas exchange and their relationship to energy transformation remain insufficiently studied quantitatively, limiting the assessment of CO2 gas exchange processes. In this work, the stomatal system of a plant leaf is analyzed as a micro-, macro-, or [...] Read more.
The mechanisms of plant leaf gas exchange and their relationship to energy transformation remain insufficiently studied quantitatively, limiting the assessment of CO2 gas exchange processes. In this work, the stomatal system of a plant leaf is analyzed as a micro-, macro-, or nanoscale thermodynamic system, applying modeling based on bionic principles. An idealized thermodynamic cycle is constructed, allowing for the assessment of the conversion of thermal energy into mechanical work. The theoretical upper limit of the thermal efficiency coefficient is estimated at ηt ≈ 0.003, and the mechanical energy flux, at a temperature difference of approximately 1 °C between the leaf and the ambient temperature, reaches up to 0.6 W/m2, i.e., about 0.3% of the solar radiation flux absorbed by the leaf. The results obtained show that even with low efficiency, this energy transformation is sufficient to influence the intensity of gas exchange. Based on the model, the plant’s CO2 sorption potential, depending on canopy area, was also estimated. It is concluded that the stomatal system of a plant leaf can be interpreted as a theoretical bionic energy transformation model, suitable for the analysis of CO2 exchange processes and the development of bionic micro-, macro-, or nanoscale systems. Full article
(This article belongs to the Special Issue Mass and Energy Fluxes over Agricultural Ecosystems)
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