Artificial Intelligence in Agriculture最新文献

{"title":"UAV-based monitoring of residual plastic film pollution in cotton fields before sowing with AIC-UNet","authors":"Lupeng Miao,&nbsp;Zhiqiang Zhai,&nbsp;Yue Chen,&nbsp;Huting Wang,&nbsp;Ruoyu Zhang,&nbsp;Yuxuan Wang","doi":"10.1016/j.aiia.2026.01.007","DOIUrl":"10.1016/j.aiia.2026.01.007","url":null,"abstract":"<div><div>This study optimizes UAV sampling methods and path planning, and constructs the AIC-UNet (Improved ASPP-InceptionV4-CBAM UNet) model to mitigate the interference of illuminance variations with residual plastic film (RPF) extraction from cotton field images. This achieves rapid and automatic monitoring of RPF pollution in pre-sowing cotton fields. Current research faces challenges such as low RPF extraction accuracy, poor alignment between the UAV sampling range and the plot boundaries, and low path planning efficiency. To tackle these challenges, this study optimizes the UAV sampling process and innovatively proposes an RPF extraction model. Firstly, this study designs a UAV adaptive sampling method by combining the geometric center-based radiation method and the Held-Karp algorithm. This achieves dynamic adjustment of sampling intervals and accurate matching with plot boundaries, providing high-quality image samples for AIC-UNet modeling. Then, this study constructs the AIC-UNet model that integrates the improved ASPP, CBAM, and InceptionV4 structure to efficiently extract RPF features under complex illuminance conditions. The results show that the proposed scheme not only enhances RPF pollution assessment accuracy under different illuminance conditions but also significantly enhances the monitoring efficiency and practicality. The AIC-UNet achieves a mIoU of 88.59% for RPF extraction under different illuminance conditions, and the <em>R</em>² reaches 0.968 in the linear fitting with manually labelled values. Additionally, the average UAV sampling path planning time is only 0.06963 s, and the average <em>D</em> (superiority degree of the path) reaches 100%, significantly enhancing the efficiency of RPF monitoring. This study provides technical support for farmland RPF pollution assessment and contributes to sustainable agricultural development.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 2","pages":"Pages 1296-1316"},"PeriodicalIF":16.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148625308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hierarchical framework for cotton boll opening rate monitoring using UAV RGB imagery","authors":"Lechun Zhang,&nbsp;Pengchao Chen,&nbsp;Zhicheng Tang,&nbsp;Ruitao Ma,&nbsp;Meiting He,&nbsp;Hang Zhu,&nbsp;Yubin Lan","doi":"10.1016/j.aiia.2026.02.002","DOIUrl":"10.1016/j.aiia.2026.02.002","url":null,"abstract":"<div><div>The cotton boll opening rate (CBOR) is a critical agronomic indicator determining yield and fiber quality. This study developed a hierarchical framework integrating unmanned aerial vehicle (UAV) RGB imagery to automate CBOR classification. To address blurred boll edges and complex backgrounds, an improved TransUNet augmented with the Convolutional Block Attention Module (CBAM) and Dynamic Snake Convolution (DySnakeConv) was proposed to extract the cotton pixel ratio (CPR). CPR, combined with vegetation indices and texture features, served as inputs for CBOR classification. Two classifiers, Random Forest (RF) and Support Vector Machine (SVM), were evaluated. Results indicated that the enhanced TransUNet significantly outperformed baseline models (e.g., U-Net, DeepLabV3+), achieving state-of-the-art segmentation (Precision 92.15%, Recall 91.93%, F1-score 92.04%, IoU 85.27%). Correlation analysis revealed that CPR served as a robust quantitative proxy for CBOR across multiple sampling dates. In classification tasks, RF consistently outperformed SVM in overall F1-scores across three dates (80.78% vs. 72.74%; 84.32% vs. 80.54%; 83.09% vs. 78.42%), likely due to its robustness in handling high-dimensional feature interactions. These findings validate a practical UAV-based framework for accurate CBOR monitoring, providing actionable support for precision harvest scheduling and variable-rate defoliant application.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 2","pages":"Pages 1347-1362"},"PeriodicalIF":16.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148625378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From pixels to points: An AI framework with weaker-and-fewer-labels for lightweight 3D phenotyping using 2D-3D coordinate mapping and VLMs","authors":"Lei Zhou,&nbsp;Yingjun Xu,&nbsp;Chu Zhang,&nbsp;Xiya Zhang,&nbsp;Qian Wu,&nbsp;Liming Bian,&nbsp;Osama Elsherbiny,&nbsp;Huichun Zhang","doi":"10.1016/j.aiia.2026.01.009","DOIUrl":"10.1016/j.aiia.2026.01.009","url":null,"abstract":"<div><div>3D phenotyping of seedlings is crucial to tomato cultivation in greenhouse facilities. Current studies focus on high-quality point cloud reconstruction and artificial intelligence (AI) 3D segmentation to derive phenotypic traits like plant height and crown width, which heavily rely on manual annotation and possess high complexity in deployment. This study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings. Through the integration of 2D-3D coordinate mapping and AI vision language models, the proposed method enables accurate reconstruction and analysis of 3D phenotypic traits from single-view data. Top-down RGB images and corresponding point clouds with spatial alignment are captured using a binocular camera. Vision language models are employed with the text prompt “plant” to automatically generate bounding boxes and masks, thereby minimizing manual annotation. These outputs are further transferred to a lightweight YOLO11-segment model. The core innovation is established in our 2D-3D mapping strategy, through which plant-specific 3D points are efficiently extracted using only 2D masks. Non-plant points within initial masks are repurposed to determine ground height for improved plant height estimation, while masks are refined using the Excess Green Index to enhance crown width measurement. An mAP₅₀ of 96.0% is achieved by the YOLO11-segment model. Concerning sparse canopy, highly accurate results are yielded by our phenotyping approach, with RMSE values of 1.7 cm for plant height and 1.0 cm for crown width, and R² values of 0.93 and 0.95 against manual measurements. For dense canopy, the usage of a reference chessboard improves the performance (RMSE was reduced from 9.57 cm to 2.07 cm). Annotation dependency is significantly reduced, computational complexity is decreased, edge deployment is supported, and efficient technology transfer is enabled by the presented method. Considerable potential is offered for high-throughput screening of elite tomato varieties with desirable agronomic traits.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 2","pages":"Pages 1317-1331"},"PeriodicalIF":16.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148625376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"YOLOSc-SAM: An acceptable extraction method for farmland regions in remote sensing images using SAM","authors":"Rui Gao,&nbsp;Weifan Cao,&nbsp;Zhigang Zhang,&nbsp;Xiaodong Cheng,&nbsp;Zeguang Ji,&nbsp;Qingming Kong,&nbsp;Zhongbin Su","doi":"10.1016/j.aiia.2026.02.004","DOIUrl":"10.1016/j.aiia.2026.02.004","url":null,"abstract":"<div><div>Farmland is a core spatial unit in agricultural production management, and accurately delineating farmland boundaries is of great significance for agricultural resource surveys and cropland monitoring. In recent years, remote sensing imagery has been widely used for farmland extraction. However, traditional semantic segmentation models are limited in their ability to precisely identify semantic contours, making it difficult to effectively extract and interpret the semantic information of cropland from remote sensing images, which in turn affects the statistical accuracy of cropland detection. To enhance the central representation of cultivation semantics, this study proposes a high-precision segmentation method based on the Segment Anything Model (SAM) to address the above challenges. The overall process includes: first, using the YOLOSc network to detect farmland objects and obtain category information; then, using the detection boxes as prompts to guide SAM in performing high-quality segmentation within the specified regions. The YOLOSc neural network is built upon the YOLOv8 architecture and incorporates the C2f_ScConv and C2f modules to enhance semantic feature encoding and instance-level decoding, respectively. The method was validated on three types of remote sensing data: 1 m resolution UAV imagery, a 2 m resolution mountainous remote sensing dataset, and 3 m resolution Planet satellite imagery. Experimental results show that the proposed method achieves excellent segmentation performance across multiple resolutions, with mIoU scores of 0.9240 (1 m), 0.9449 (2 m), and 0.8211 (3 m), respectively. The method outperforms several mainstream semantic segmentation models on the challenging 2 m mountainous dataset, characterized by complex terrain and blurred plot boundaries. Moreover, the overall method significantly reduces the annotation workload for training data. This offers a new approach for building lightweight and efficient frameworks for farmland extraction from remote sensing imagery.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 2","pages":"Pages 1384-1404"},"PeriodicalIF":16.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148625381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of artificial intelligence in insect pest identification - A review","authors":"Sourav Chakrabarty ,&nbsp;Chandan Kumar Deb ,&nbsp;Sudeep Marwaha ,&nbsp;Md. Ashraful Haque ,&nbsp;Deeba Kamil ,&nbsp;Raju Bheemanahalli ,&nbsp;Pathour Rajendra Shashank","doi":"10.1016/j.aiia.2025.06.005","DOIUrl":"10.1016/j.aiia.2025.06.005","url":null,"abstract":"<div><div>The increasing danger of insect pests to agriculture and ecosystems calls for quick, and precise diagnosis. Conventional techniques that depend on human observation and taxonomic knowledge are frequently labour-intensive and time-consuming. Incorporating artificial intelligence (AI) into detection has emerged as an effective approach in agriculture, including entomology. AI-based detection methods use machine learning, deep learning algorithms, and computer vision techniques to automate and improve the identification of insects. Deep learning algorithms, such as convolutional neural networks (CNNs), are primarily used for AI-powered insect pest identification by categorizing insects based on their visual features through image-based classification methodology. These methods have revolutionized insect identification by analyzing large databases of insect images and identifying distinct patterns and features linked to different species. AI-powered systems can improve insect pest identification by utilizing other data modalities. However, there are obstacles to overcome, such as the scarcity of high-quality labelled datasets and scalability and affordability issues. Despite these challenges, there is significant potential for AI-powered insect pest identification and pest management. Cooperation among researchers, practitioners, and policymakers is necessary to utilize AI in pest management fully. AI technology is transforming the field of entomology by enabling high-precision identification of insect pests, leading to more efficient and eco-friendly pest management strategies. This can enhance food safety and reduce the need for continuous insecticide spraying, ensuring the purity and safety of the food supply chains. This review updates AI-powered insect pest identification, covering its significance, methods, challenges, and prospects.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 44-61"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight model based on knowledge distillation for free-range chickens detection in complex commercial farming environments","authors":"Xiaoxin Li ,&nbsp;Mingrui Cai ,&nbsp;Zhen Liu ,&nbsp;Chengcheng Yin ,&nbsp;Xinjie Tan ,&nbsp;Jiangtao Wen ,&nbsp;Yuxing Han","doi":"10.1016/j.aiia.2025.10.010","DOIUrl":"10.1016/j.aiia.2025.10.010","url":null,"abstract":"<div><div>Side-view imaging for monitoring free-range chickens on edge devices faces significant challenges due to complex backgrounds, occlusions, and limited computational resources, which particularly affect the performance of lightweight models in terms of their representational capacity and generalization ability. To address these limitations, this study proposes a Lightweight Free-range Chickens Detection Model based on YOLOv8n and knowledge distillation (LCD-YOLOv8n-KD), establishing an optimal balance between detection performance and model efficiency. The YOLOv8n architecture is enhanced by incorporating DualConv, CCFF, PCC2f, and SAHead modules to create LCD-YOLOv8n, significantly reducing model parameters and computational complexity. Further improvement is achieved through knowledge distillation, where a pre-trained large-scale model developed by our team as the teacher network, and LCD-YOLOv8n functioned as the student network, resulting in the LCD-YOLOv8n-KD model. Experimental validation is conducted using a comprehensive dataset comprising 6000 images with 162,864 labeled chicken targets, collected from various side-view angles in commercial farming environments. LCD-YOLOv8n-KD achieves AP₅₀ values of 95.9 %, 90.2 %, 82.7 %, and 69.3 % on the test set and three independent test sets, respectively. Compared to the original YOLOv8n, the proposed model demonstrates a 16.13 % improvement in AP₅₀ while reducing parameters by 47.84 % and GFLOPs by 41.46 %. The proposed model outperforms other state-of-the-art lightweight models in terms of detection efficiency, accuracy, and generalization capability, demonstrating strong potential for practical deployment in real-world free-range chicken farming environments.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 266-283"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of obstacle avoidance for autonomous agricultural machinery in multi-operational environment","authors":"Zhijian Chen ,&nbsp;Jianjun Yin ,&nbsp;Sheikh Muhammad Farhan ,&nbsp;Lu Liu ,&nbsp;Ding Zhang ,&nbsp;Maile Zhou ,&nbsp;Junhui Cheng","doi":"10.1016/j.aiia.2025.10.001","DOIUrl":"10.1016/j.aiia.2025.10.001","url":null,"abstract":"<div><div>As automation becomes increasingly adopted to mitigate labor shortages and boost productivity, autonomous technologies such as tractors, drones, and robotic devices are being utilized for various tasks that include plowing, seeding, irrigation, fertilization, and harvesting. Successfully navigating these changing agricultural landscapes necessitates advanced sensing, control, and navigation systems that can adapt in real time to guarantee effective and safe operations. This review focuses on obstacle avoidance systems in autonomous farming machinery, highlighting multi-functional capabilities within intricate field settings. It analyzes various sensing technologies, LiDAR, visual cameras, radar, ultrasonic sensors, GPS/GNSS, and inertial measurement units (IMU) for their individual and collective contributions to precise obstacle detection in fluctuating field conditions. The review examines the potential of multi-sensor fusion to enhance detection accuracy and reliability, with a particular emphasizing on achieving seamless obstacle recognition and response. It addresses recent advancements in control and navigation systems, particularly focusing on path-planning algorithms and real-time decision-making. It enables autonomous systems to adjust dynamically across multi-functional agricultural environments. The methodologies used for path planning, including adaptive and learning-based strategies, are discussed for their ability to optimize navigation in complicated field conditions. Real-time decision-making frameworks are similarly evaluated for their capacity to provide prompt, data-driven reactions to changing obstacles, which is critical for maintaining operational efficiency. Moreover, this review discusses environmental and topographical challenges like variable terrain, unpredictable weather, complex crop arrangements, and interference from co-located machinery that hinder obstacle detection and necessitate adaptive, resilient system responses. In addition, the paper emphasizes future research opportunities, highlighting the significance of advancements in multi-sensor fusion, deep learning for perception, adaptive path planning, model-free control strategies, artificial intelligence, and energy-efficient designs. Enhancing obstacle avoidance systems enables autonomous agricultural machinery to transform modern farming by increasing efficiency, precision, and sustainability. The review highlights the potential of these technologies to support global efforts for sustainable agriculture and food security, aligning agricultural innovation with the needs of a swiftly growing population.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 139-163"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of navigation technology in agricultural machinery: A review and prospects","authors":"Liuyan Feng ,&nbsp;Changsu Xu ,&nbsp;Han Tang ,&nbsp;Zhongcai Wei ,&nbsp;Xiaodong Guan ,&nbsp;Jingcheng Xu ,&nbsp;Mingjin Yang ,&nbsp;Yunwu Li","doi":"10.1016/j.aiia.2025.10.003","DOIUrl":"10.1016/j.aiia.2025.10.003","url":null,"abstract":"<div><div>With the rapid advancement of information technology, the intelligent and unmanned applications of agricultural machinery and equipment have become a central focus of current research. Navigation technology is central to achieving autonomous driving in agricultural machinery and plays a key role in advancing intelligent agriculture. However, although some studies have reviewed aspects of agricultural machinery navigation technologies, a comprehensive and systematic overview that clearly outlines the developmental trajectory of these technologies is still lacking. At the same time, there is an urgent need to break through traditional navigation frameworks to address the challenges posed by complex agricultural environments. Addressing this gap, this study provides a comprehensive overview of the evolution of navigation technologies in agricultural machinery, categorizing them into three stages: assisted navigation, autonomous navigation, and intelligent navigation, based on the level of autonomy in agricultural machinery. Special emphasis is placed on the brain-inspired navigation technology, which is an important branch of intelligent navigation and has attracted widespread attention as an emerging direction. It innovatively mimics the cognitive and learning abilities of the brain, demonstrating high adaptability and robustness to better handle uncertainty and complex environments. Importantly, this paper innovatively explores six potential applications of brain-inspired navigation technology in the agricultural field, highlighting its significant potential to enhance the intelligence of agricultural machinery. The review concludes by discussing current limitations and future research directions. The findings of this study aim to guide the development of more intelligent, adaptive, and resilient navigation systems, accelerating the transformation toward fully autonomous agricultural operations.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 94-123"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of wheat stem biomass using a new unified model driven by phenological variable under remote-sensed canopy vegetation index constraints","authors":"Weinan Chen ,&nbsp;Guijun Yang ,&nbsp;Yang Meng ,&nbsp;Haikuan Feng ,&nbsp;Hongrui Wen ,&nbsp;Aohua Tang ,&nbsp;Jing Zhang ,&nbsp;Hao Yang ,&nbsp;Heli Li ,&nbsp;Xingang Xu ,&nbsp;Changchun Li ,&nbsp;Zhenhong Li","doi":"10.1016/j.aiia.2025.11.007","DOIUrl":"10.1016/j.aiia.2025.11.007","url":null,"abstract":"<div><div>Timely and accurate prediction of stem dry biomass (SDB) is crucial for monitoring crop growing status. However, conventional biomass estimation models are often limited by the influence of crop growth phase, which significantly restricts their temporal and spatial transferability. This study aimed to develop a semi-mechanistic stem biomass prediction model (PVWheat-SDB) using phenological variable (PV) to accurately predict winter wheat SDB across different growth stages. The core of the model is to predict SDB using PV under remote-sensed canopy vegetation indices (VIs) constraint. The results demonstrated that VIs can quantify the variations in stem growth equations under different planting conditions and varieties. The developed a PVWheat-SDB model using normalized difference red edge (NDRE) and accumulated growing degree days (AGDD) performed well for SDB predictions, with R², RMSE, nRMSE and MAE values of 0.88, 75.48 g/m², 8.04 % and 55.36 g/m² for the validation datasets of field spectral reflectance, and 0.82, 81.76 g/m², 11.22 % and 62.82 g/m² when transferred to unmanned aerial vehicle (UAV) hyperspectral images. Furthermore, the model can not only estimate SDB at the current growth stage, but also predict SDB of subsequent phenological stages. The growth stage stacking strategy indicated that the model accuracy improves significantly as more growth stages are incorporated, especially during the reproductive stages. These results all highlight the robustness and transferability of the PVWheat-SDB model in accurately predicting SDB across different growing seasons and growth stages.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 658-671"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing interpretable machine learning algorithms and multiple features from multi-temporal Sentinel-2 imagery for predicting wheat fusarium head blight","authors":"Hui Wang ,&nbsp;Chao Ruan ,&nbsp;Jinling Zhao ,&nbsp;Yunran Wang ,&nbsp;Ying Li ,&nbsp;Yingying Dong ,&nbsp;Linsheng Huang","doi":"10.1016/j.aiia.2025.10.012","DOIUrl":"10.1016/j.aiia.2025.10.012","url":null,"abstract":"<div><div>Wheat <em>Fusarium</em> head blight (FHB) severely affects wheat yields, and predicting its occurrence and spatial distribution is essential for safeguarding crop production. This study presents an interpretable machine learning method designed to predict FHB by leveraging multi-temporal and multi-feature information obtained from Sentinel-2 imagery. During the regreening and grain-filling stages, we extracted vegetation indices (VIs), texture features (TFs), and color indices (CIs). Single-temporal features were derived from the grain-filling stage, while multi-temporal features combined data from grain-filling and regreening stages. The synthetic minority over-sampling technique (SMOTE) was employed to adjust the class imbalance, while the most significant characteristics were found using the sequential forward selection (SFS) approach. The extreme gradient boosting (XGBoost) model, optimized using simulated annealing (SA) algorithm and explained via SHapley Additive exPlanation (SHAP) method, integrated VIs, TFs, and CIs as input features. The presented model demonstrated exceptional results, achieving a prediction accuracy of 89.9 % across multi-temporal and a Kappa coefficient of 0.797. It outperformed random forests (RF), backpropagation neural networks (BPNN), and support vector machines (SVM) model. This study indicates that an interpretable machine learning approach, which utilizes both multi-temporal and multi-feature data, is effective in forecasting FHB, thereby providing a valuable tool for agricultural management and disease prevention strategies.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 224-239"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}