Computers and Electronics in Agriculture最新文献

{"title":"Innovative photosynthesis model twinning after intelligent interpretation of complex sensor analytics","authors":"Xiaotong Wang ,&nbsp;Xuejiao Tong ,&nbsp;Bingguang Han ,&nbsp;Zhulin Li ,&nbsp;Qingji Li ,&nbsp;Xianmin Liu ,&nbsp;Zhouping Sun ,&nbsp;Nick Sigrimis ,&nbsp;Tianlai Li","doi":"10.1016/j.compag.2026.111496","DOIUrl":"10.1016/j.compag.2026.111496","url":null,"abstract":"<div><div>Accurate canopy photosynthesis modeling is essential for understanding and optimizing crop growth and yield in greenhouse agriculture. Current models have limited predictive capability due to inadequate responsiveness to dynamic environments and delays in parameter acquisition, making accurate predictions challenging under the complex conditions of solar greenhouses. This study aimed to develop a dynamic canopy photosynthesis model for greenhouse tomatoes, leveraging an IoT sensor network for real-time biological feedback and parameterization. By integrating real-time monitoring with dynamic feedback, the model facilitates precision management of greenhouse tomato cultivation, thereby optimizing plant growth, resource use efficiency, and yield predictability. To achieve this, a non-destructive inversion method based on a dual weighing system was developed, enabling accurate dynamic monitoring of tomato canopy leaf area index (LAI, R² ≥ 0.94) and the photosynthetic leaf area index (LAI_p, R² ≥ 0.91), continuously providing parameters for updating modelling (validated against destructive sampling and actual measurements for trait specifics). Based on accurate parameter acquisition, a dynamic canopy photosynthesis model was developed using LAI_p as the core variable, integrating above-canopy radiation. A newly developed parameter, which integrates the radiation component of transpiration, serves as a key factor for estimating photosynthesis. This innovative approach allows for accurate daily prediction and assessment of assimilated biomass. Experimental results from 2022 and 2023 showed that the LAI_p model performed better than the comparison model, showing higher accuracy and adaptability (R² = 0.87 and 0.89, NRMSE = 0.17 and 0.12 vs. R² = 0.70 and 0.80, NRMSE = 0.26 and 0.15). These results confirmed the reliability of the integrated modeling framework, which forms a closed-loop system connecting real-time plant monitoring, statistical parameter inversion, online model adaptation, and biomass feedback verification. This modeling approach provides a solid foundation for precise growth simulation, sustainably improving yield and quality in solar greenhouse tomatoes, and advancing digital twin-enabled intelligent production.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111496"},"PeriodicalIF":8.9,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WeedCAM: An edge-computing camera system for multi-species weed detection in sugar beet production fields","authors":"Zonglin Yang ,&nbsp;Wei-Zhen Liang ,&nbsp;Nevin Lawrence ,&nbsp;Xin Qiao ,&nbsp;Benjamin Riggan ,&nbsp;Robert Harveson ,&nbsp;Chi-En Chiang ,&nbsp;Joseph Oboamah ,&nbsp;Diwenitissiou Philipine Andjawo","doi":"10.1016/j.compag.2026.111498","DOIUrl":"10.1016/j.compag.2026.111498","url":null,"abstract":"<div><div>This study introduces WeedCAM, a low-cost, near real-time, edge-computing camera system for multi-species weed detection, built on a Raspberry Pi 5 and integrated with a GPS module and LoRa board for geolocation and data transmission. A three-phase framework, including data acquisition, model fine-tuning, and deployment, is proposed to implement detection models on WeedCAM. A total of 5734 high-resolution (4K) images were automatically collected using WeedCAM, producing a dataset with a long-tail distribution that poses challenges for model training. To address this, Repeat Factor Sampling and Focal Loss were applied during the fine-tuning. Seven object detection models were evaluated, including YOLOX-S, YOLOX-L, Faster R-CNN, Cascade R-CNN, Deformable-DETR, and DINO. Finally, WeedCAMs with embedded trained models were deployed in the field on pivot-mounted and ground-based installations, detecting weeds at 30-min intervals and transmitting results to a customized gateway via LoRa. The gateway parsed and mapped these results to our custom-designed website for visualization. Our best model, DINO-Swin/L, set the performance benchmark with a 76.0 overall mAP (IoU = 0.5) and strong per-species scores for kochia (76.4 mAP), Palmer amaranth (77.3 mAP), and volunteer corn (75.9 mAP) at 4K resolution image. Despite this, YOLOX-L was deployed on the WeedCAM, as its efficient 8-min processing cycle represented the better trade-off between accuracy and speed. Field evaluation confirmed that WeedCAM effectively identified weed species and quantities under varying lighting conditions, camera angles, and soil moisture levels during irrigation events. These results demonstrate the practicality of deploying WeedCAM edge-computing deep learning systems for near real-time, multi-species weed detection under sugar beet fields.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111498"},"PeriodicalIF":8.9,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Potato Virus Y in plant foliage using convolutional neural network classifiers and hyperspectral imagery","authors":"L.M. Griffel,&nbsp;D. Delparte","doi":"10.1016/j.compag.2026.111499","DOIUrl":"10.1016/j.compag.2026.111499","url":null,"abstract":"<div><div><em>Solanum tuberosum</em> (potato) is one of the most important global food crops relative to economic opportunities and food security. <em>Potato Virus Y</em> (<em>Potyviridae</em>, PVY), a detrimental plant pathogen propagated by insect vectors, negatively affects tuber yield and quality. This has forced industry stakeholders to adopt many different types of mitigation strategies including pesticide applications, manual field scouting, and potato seed certification programs. Despite these efforts, PVY continues to disrupt industry production regions resulting in significant economic losses due to the lack of robust diagnostic tools. Machine learning algorithms trained on remotely sensed spectral features show promise as a diagnostic tool for many plant diseases including PVY. This study proposes a novel Convolutional Neural Network (CNN) architecture to detect potato plant canopy regions of plants infected with PVY based on unmanned aerial system (UAS) hyperspectral pixel features comprised of bands matching the center wavelengths of nine spectral channels captured by the European Space Agency’s Sentinel 2 multispectral instrument. Accuracy and F1 metrics of 0.815 and 0.766 respectively were achieved on test data collected over multiple growing seasons and locations. Additionally, efforts were made to identify optimal combinations of spectral bands that are most beneficial for the CNN classifier by evaluating every possible combination of the nine spectral wavelengths in groups ranging from 3 to 9 channels. Results show that hyperspectral channels centered on 783 nm, 739 nm, and 560 nm are the most important features for the CNN architecture. Additionally, six hyperspectral features consisting of the three previously mentioned along with 665 nm, 704 nm, and 864 nm yielded the best results of all possible combinations achieving accuracy and F1 Score metrics of 0.833 and 0.791 respectively.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111499"},"PeriodicalIF":8.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic segmentation–based detection of exposed soil regions in paddy fields for a floating-type puddling and leveling operation","authors":"Zian Liang ,&nbsp;Jun Zhou ,&nbsp;Yongpeng Chen ,&nbsp;Yinghua Zhang ,&nbsp;Tamiru Tesfaye Gemechu ,&nbsp;Lei Li ,&nbsp;Huayu Zhou ,&nbsp;Muhammad Aurangzaib","doi":"10.1016/j.compag.2026.111494","DOIUrl":"10.1016/j.compag.2026.111494","url":null,"abstract":"<div><div>Integrated puddling–leveling operation is a critical step in paddy field preparation, typically conducted between plowing and rice transplanting. However, the accuracy of elevation measurements in existing automatic leveling technologies is often constrained by limited operating ranges or susceptibility to electromagnetic interference, resulting in inconsistent leveling performance. Because the water surface naturally reflects terrain undulations in paddy fields, this study proposes a semantic segmentation–based approach to detect exposed soil regions for guiding a floating-type puddling and leveling implement. To this end, a lightweight semantic segmentation model, PL_DeepLabV3+_0.8, was developed specifically for integrated puddling–leveling operation. The model combines a MobileNetV2_S backbone, a Low-Level Feature Fusion Module (LFM), and structured pruning. These components collectively enable the rapid and accurate detection of exposed soil in paddy fields under computationally constrained conditions. The PL_DeepLabV3+_0.8 model was successfully deployed in the control system of a floating-type implement, and its effectiveness was validated through field tests conducted at different operating<!--> <!-->speeds and modes. On a paddy field image dataset, PL_DeepLabV3+_0.8 achieved a mean Pixel Accuracy (mPA) of 92.23 ± 0.22%, a mean Intersection over Union (mIoU) of 84.18 ± 0.31%, and an inference speed of 7.73 frames per second (FPS), outperforming the original DeepLabV3 + model, which achieved 91.90%, 83.81%, and 0.88 FPS, respectively. In field tests at operating speeds of 1.1 m/s and 1.5 m/s, the surface flatness (standard deviation of elevation) in two paddy fields was improved from 3.61 cm and 4.07 cm to 2.11 cm and 2.42 cm, respectively. These results indicate that the deployed model not only satisfies the flatness requirement for rice transplanting (&lt; 3 cm) but also delivers a productivity increase of 0.28 ha/h compared with conventional manual operation. Overall, this study provides a useful reference for the development of intelligent puddling and leveling technologies in paddy field preparation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111494"},"PeriodicalIF":8.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and experiment of a film-breaking robot for sweet potato horizontal transplantation with plastic mulch","authors":"Wanzhi Zhang ,&nbsp;Yangqian Zhang ,&nbsp;Xuyang Wang ,&nbsp;Yulu Sun ,&nbsp;Hongjuan Liu ,&nbsp;Zhigang Li","doi":"10.1016/j.compag.2026.111483","DOIUrl":"10.1016/j.compag.2026.111483","url":null,"abstract":"<div><div>Plastic film mulch cultivation technology is a crucial agronomic measure for enhancing early-spring sweet potato yields. However, prolonged film coverage can scorch seedlings beneath the mulch, adversely affecting their normal growth and subsequent yield. Therefore, timely film-breaking to guide seedling emergence is essential. Currently, manual film-breaking is the primary method. To address the high labor intensity associated with manual operations, this paper designs a sweet potato seedling film-breaking robot based on deep learning and a Delta parallel robot. First, a calibration method was proposed for scenarios where the camera field of view is separated from the Delta parallel robot’s workspace, which avoids missed detection issues caused by manipulator occlusion. Subsequently, images of sweet potato seedlings captured under various environmental conditions were selected as the data basis for the deep learning model, and the BW-YOLO sweet potato seedling detection model was constructed. This model replaces the CIoU loss function with the Wise-IoU v3 loss function and incorporates a BiFPN module into the neck network. Testing results show that the model achieved a mean Average Precision (mAP) of 96.8% and a detection speed of 76.34 FPS, demonstrating significant improvements in both detection accuracy and speed. Finally, the detection model was deployed on the sweet potato seedling film-breaking robot, and field trials were conducted. The model achieved an average recognition success rate of 90.56%, the film-breaking robot attained a film-breaking qualification rate of 84.56%, and the seedling emergence rate reached 83.74%. The proposed sweet potato film-breaking robot for flat cultivation enables unmanned operation during seedling emergence, providing a valuable reference for the design of intelligent agricultural equipment.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111483"},"PeriodicalIF":8.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a new Single-Tree-Row-Tracking robot navigation for intra-row weeding operations in orchards using a Machine stereo vision system and LiDAR","authors":"Rizky Mulya Sampurno ,&nbsp;Zifu Liu ,&nbsp;Victor Massaki Nakaguchi ,&nbsp;Ailian Jiang ,&nbsp;Tofael Ahamed","doi":"10.1016/j.compag.2026.111491","DOIUrl":"10.1016/j.compag.2026.111491","url":null,"abstract":"<div><div>Driven by the need for efficient intra-row weed management in orchards, a new robotic system is designed and proposed to operate in narrow spaces and low-hanging branches with minimal soil compaction and no reliance on Global Navigation Satellite System (GNSS). To enable the tree-row following that is required for intra-row weeding, we introduced a vision-based framework that combined a 3D camera and a lightweight YOLOv8 instance segmentation model to detect tree trunks and extracted the navigation path from a single tree row through the frontal side of the view of the robot. The trajectory of the robot had offset by 0.8 m from the tree row, enabling a new Light Detection and Ranging (LiDAR)-triggered side-shift mechanism to target uncut weeds between trees in intra-rows. An experimental evaluation in simulated environments demonstrated stable navigation performance, with a 0.329 m RMSE. Furthermore, the side-shift actuation mechanism for weeding achieved 84.04% accuracy at a lower speed (0.5 m/s) and 76.85% accuracy at a faster speed (0.8 m/s); these results were due in part to the processing latency in real-time LiDAR point cloud analysis. These findings highlight the importance of optimizing computational efficiency and actuation timing for better field performance. Finally, the developed robotic system effectively integrated 3D vision, deep learning, and LiDAR-triggered actuation to perform autonomous intra-row weeding, demonstrated strong potential to address operational efficiency for intra-row weed management in orchards.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111491"},"PeriodicalIF":8.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning driven edge inference for pest detection in potato crops using the AgriScout robot","authors":"Yuvraj Singh Gill ,&nbsp;Hassan Afzaal ,&nbsp;Charanpreet Singh ,&nbsp;Gurjit S. Randhawa ,&nbsp;Kritikiran Angrish ,&nbsp;Navpreet Jaura ,&nbsp;Zarnab Qamar ,&nbsp;Aitazaz A. Farooque","doi":"10.1016/j.compag.2026.111492","DOIUrl":"10.1016/j.compag.2026.111492","url":null,"abstract":"<div><div>Early field-scale surveillance of Colorado potato beetle remains a persistent bottleneck for sustainable potato production because conventional scouting is labor-intensive and provides limited spatial resolution for timely intervention. Here, we present AgriScout, a battery-powered autonomous scouting robot equipped with RGB imaging, controlled lighting, and RTK-GPS geotagging for continuous row-to-row data collection. Using AgriScout, we curated a field dataset of 832 georeferenced images and manually annotated adult beetles with tight bounding boxes to support tiny-object detection under real canopy conditions. We benchmarked six YOLO object detectors (YOLOv5s, YOLOv8s, YOLOv9s, YOLOv10s, YOLOv11s, and YOLOv12s) using transfer learning, high-resolution inputs (1280 × 1280), and an augmentation strategy tailored to small targets (including mosaic, scaling, and translation). To address training variability on the modest dataset, models were evaluated across multiple random seeds (7, 42, 123, 999, and 2024) and compared using precision, recall, mAP, F1, confidence behavior, and statistical tests of between-model differences. Across runs, YOLOv11s provided the most reliable overall balance for deployment, exhibiting strong precision and robust localization performance. For edge deployment, inference throughput was measured on an NVIDIA Jetson Orin Nano across multiple export formats; TensorRT consistently delivered the highest FPS, reaching 46.5 FPS (YOLOv5s) and exceeding 40 FPS for several variants, confirming real-time feasibility under FP32 inference. Finally, YOLOv11s detections were fused with RTK-GPS coordinates to generate centimeter-level infestation maps that visualize spatial clustering of beetle activity and support hotspot-driven, targeted management. Collectively, this work demonstrates an end-to-end, robot-to-map pipeline for beetle monitoring and provides a reproducible benchmark of accuracy, stability, and edge deployability for YOLO-based pest detection in commercial potato systems.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111492"},"PeriodicalIF":8.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-dimensional behavioral signature analysis of laying hens under heat stress: development of a behavior-based level assessment model","authors":"Zixuan Zhou ,&nbsp;Lihua Li ,&nbsp;Hao Xue ,&nbsp;Yuchen Jia ,&nbsp;Yao Yu ,&nbsp;Zongkui Xie ,&nbsp;Yuhan Gu","doi":"10.1016/j.compag.2026.111433","DOIUrl":"10.1016/j.compag.2026.111433","url":null,"abstract":"<div><div>Accurate heat stress assessment is pivotal for early prevention and safeguarding poultry welfare. However, current protocols relying on the Temperature-Humidity Index (THI) often fail to capture the true physiological thermal load of laying hens. Conversely, animal behavior serves as a direct phenotypic response to environmental stressors, offering unique insights into adaptive mechanisms. Consequently, this study proposes a Behavior-based Heat Stress Assessment (BHSA) method driven by behavioral feedback. To achieve precise, non-invasive detection and automated feature extraction of individual heat stress behaviors, we developed YOLO-SPS, an enhanced architecture based on YOLOv12. By integrating SPD-Conv modules, A2C2F-PPA structures, and a Slide Loss function, the model effectively mitigates missed and false detections associated with fine-grained features and significant postural variations. We established a behavior-environment association model under controlled conditions (20-38 °C at 60% and 80% RH), identifying six heat stress-associated behaviors quantified by skewness and occurrence intensity. K-means clustering categorized these data into five distinct patterns, which were biologically validated by significant differences in corticosterone (CORT) and Heat Shock Protein 70 (HSP70) levels across clusters (P &lt; 0.05). Accordingly, a five-level BHSA model was established, stratifying stress into Normal, Alert, Impact, Harm, and Disaster levels. Results demonstrated that YOLO-SPS improved detection accuracy by 3.8% and inference speed by 22.1% compared to the baseline. In comparison to the traditional THI methods, the BHSA triggered Alert and Harm warnings at temperatures 2 ± 1 °C lower, enabling earlier detection. Furthermore, under extreme heat, the BHSA successfully differentiated between Harm and Disaster states. This study realizes a paradigm shift in heat stress assessment from “environment-driven” to “animal behavior-driven,” providing robust technical support for precision livestock management and early intervention strategies.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111433"},"PeriodicalIF":8.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LMTRNet: Lightweight Multi-scale Temperature-Regulated Network For real-time detection of multiple species pests","authors":"Taiyu Xu","doi":"10.1016/j.compag.2026.111487","DOIUrl":"10.1016/j.compag.2026.111487","url":null,"abstract":"<div><div>Pests cause significant losses to global agricultural production. However, their concealment and mobility pose considerable challenges for real-time pest detection. In this paper, we propose a Lightweight Multi-scale Temperature-Regulated Network (LMTRNet) for real-time multi-pest detection. LMTRNet consists of three key components: a lightweight feature extraction network, a multi-scale fusion network (DMFN), and an adaptive temperature-modulated head (AITMH). To improve feature learning efficiency, we introduce the Adaptive Feature Sparsity Block (AFSBlock) and the Spatial-Channel Decoupled Downsampling (SCDown) module in the lightweight feature extraction network, reducing computational cost while preserving accuracy. The DMFN employs skip connections for enhanced multi-scale feature integration, while AITMH leverages a temperature-aware fusion strategy to refine feature representation. Additionally, LMTRNet utilizes an anchor-free detection head with a dynamic inner loss (DILoss) function to improve localization accuracy, particularly for small pests in cluttered environments. To address data scarcity, we propose a Synthetic Object Projection Augmentation method, enriching training diversity by projecting multiple species of pest onto complex backgrounds. Experiments are conducted on a proprietary dataset and the Pest24 dataset to evaluate LMTRNet’s performance. On the proprietary dataset, LMTRNet-l, with only 23.03M parameters, achieved the precision of 96.02%, mAP50 of 95.7%, and mAP50-95 of 63.33%. On the Pest24 dataset, it attained the precision of 77.49%, mAP50 of 70.1%, and mAP50-95 of 45.71%. These results demonstrate that LMTRNet achieves state-of-the-art accuracy and real-time performance, making it a robust solution for practical pest monitoring.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111487"},"PeriodicalIF":8.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of spraying quality and drift risk in unmanned aerial spraying systems (UASS) based on Multi-Gradient droplet size control","authors":"Pengchao Chen ,&nbsp;Jiapei Wu ,&nbsp;Zhihao Bian ,&nbsp;Jean Paul Douzals ,&nbsp;Yingdong Qin ,&nbsp;Hanbing Liu ,&nbsp;Juan Wang ,&nbsp;Yubin Lan","doi":"10.1016/j.compag.2026.111481","DOIUrl":"10.1016/j.compag.2026.111481","url":null,"abstract":"<div><div>Unmanned aerial spraying systems (UASS) are widely used in agriculture; however, spray drift remains a significant barrier to their broader adoption. Conventional drift-control measures—such as nozzle optimization and adjuvants—primarily act by altering droplet size. This study introduces a dynamic droplet-size control approach for UASS equipped with centrifugal nozzles to balance spray quality and drift risk. We established the relationship between nozzle rotational speed and droplet size and developed an embedded, variable droplet-size UASS. The system utilizes differential RTK to acquire real-time UAV position data, enabling dynamic adjustment of droplet size during operation. Field trials demonstrated the system’s stability and reliability: the UASS responded promptly to ground commands for droplet size changes and accurately logged the corresponding adjustment locations. Data analysis indicated that increasing droplet size markedly reduces drift volume and droplet density in the downwind drift zone. Relative to a baseline without droplet-size control, a three-stage adjustment strategy reduced the drift ratio by 89.18%, shortened the 90% drift distance to 3.36 m, and delivered the highest drift-mitigation rate. To minimize drift while maintaining effective penetration, we propose using the very fine (VF) droplet size (82.4 μm) for the first two flight paths and increasing to 300–350 μm for the third. These findings demonstrate that dynamic droplet-size adjustment via pulse-width modulation can effectively reduce drift. External factors, such as wind and terrain, continue to be influential, underscoring the need for further research to refine and optimize drift-control strategies under diverse operating conditions.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111481"},"PeriodicalIF":8.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}