Computers and Electronics in Agriculture最新文献

{"title":"Novel implementation of Colby’s method for analyzing interactions between mesotrione and diflufenican using hyperspectral and multispectral machine vision","authors":"Zhongzhong Niu ,&nbsp;Abigail Norsworthy ,&nbsp;Julie Young ,&nbsp;Bryan Young ,&nbsp;Tianzhang Zhao ,&nbsp;Xuan Li ,&nbsp;Alden Mo ,&nbsp;Charles Wang ,&nbsp;Jian Jin","doi":"10.1016/j.compag.2025.111018","DOIUrl":"10.1016/j.compag.2025.111018","url":null,"abstract":"<div><div>Herbicides play a crucial role in cropping systems by providing effective weed control strategies that help farmers eliminate yield-reducing weeds. However, crop injury may result from herbicides applied in current or previous cropping systems, and in some instances, this injury may reduce crop yield. Currently, herbicide related crop injury is commonly determined by subjective visual assessments. Spectral imaging provides an alternative solution, which is high-throughput and non-invasive. In this study, a novel machine vision method utilizing hyperspectral imaging (HSI) and multispectral imaging (MSI) was developed and integrated into Colby’s method—a traditional approach in weed science for analyzing the interaction effects of herbicide mixtures. Mesotrione and diflufenican, both herbicides that cause bleaching symptomology, were applied in this study. Two rounds of field experiments were conducted in the summer of 2024, where hyperspectral and multispectral images were collected 26 DAT in each trial. Partial Least Squares Discriminant Analysis (PLS-DA) models were built to identify soybean injury from mesotrione, diflufenican, and the mixture. For Colby’s method to study the interaction effect, spatial-spectral features were generated from MSI. The HSI models achieved an accuracy exceeding 90 %. Thirteen distinct features were identified and selected to illustrate the synergistic effects of the herbicides, showing consistency across two experimental rounds and aligning with findings from traditional methods.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111018"},"PeriodicalIF":8.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219686","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":"An interpretable nondestructive detection model for maize seed viability: Based on grouped hyperspectral image fusion and key biochemical indicators","authors":"Yaoyao Fan ,&nbsp;Ting An ,&nbsp;Xueying Yao ,&nbsp;Yuan Long ,&nbsp;Qingyan Wang ,&nbsp;Zheli Wang ,&nbsp;Xi Tian ,&nbsp;Liping Chen ,&nbsp;Wenqian Huang","doi":"10.1016/j.compag.2025.111036","DOIUrl":"10.1016/j.compag.2025.111036","url":null,"abstract":"<div><div>Seed viability is crucial for ensuring crop quality and yield. However, existing nondestructive detection methods, which primarily rely on spectroscopic techniques and simple data fusion strategies, often suffer from limited accuracy and reliability. To address these limitations, this study proposes a novel, highly accurate, and interpretable nondestructive approach for evaluating maize seed viability. With regard to enhancing the prediction accuracy of seed viability, a grouped hyperspectral image fusion (GHIF) strategy was proposed to more effectively integrate complementary information from visible-near-infrared hyperspectral imaging (VisNIR-HSI) and fluorescence hyperspectral imaging (Fluo-HSI) datasets. With respect to improving model interpretability, eight biochemical components in the embryo of maize seeds were measured, and two key biochemical indicators—catalase (CAT) activity and malondialdehyde (MDA) content—were identified and validated as highly correlated with seed viability and predictable from spectral data. Building on these findings, a two-stage detection model was constructed. In the first stage, the two key biochemical indicators were predicted from the fused data using regression models. In the second stage, seed viability was determined using a dual-threshold strategy based on the predicted biochemical values. Experimental results showed that the proposed method achieved 90 % classification accuracy, comparable to direct spectral models while offering greater interpretability. This approach provides a reliable and explainable solution for nondestructive seed viability evaluation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111036"},"PeriodicalIF":8.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219634","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":"A deep learning-based pin precision weeding machine with densely placed needle nozzles","authors":"Hyungjun Jin ,&nbsp;Dewa Made Sri Arsa ,&nbsp;Talha Ilyas ,&nbsp;Jong-hoon Lee ,&nbsp;Okjae Won ,&nbsp;Seok-Hwan Park ,&nbsp;Kumar Sandesh ,&nbsp;Sang Cheol Kim ,&nbsp;Hyongsuk Kim","doi":"10.1016/j.compag.2025.110990","DOIUrl":"10.1016/j.compag.2025.110990","url":null,"abstract":"<div><div>With advancements in artificial intelligence and robotic technology, the demand for innovative weed control methods has increased. This paper proposes a novel weeding machine concept that integrates artificial intelligence with micro-needle nozzles, enabling precise and selective herbicide application based on weed size and type. The system employs deep learning-based semantic segmentation to accurately identify weeds at the pixel level. Following identification, densely arranged needle nozzles deliver fine streams of herbicide directly to targeted weeds. The herbicide dosage is regulated by time-controlled shooting, facilitated by solenoid valves. The developed pin-precision weeding machine features a 1.20-meter-wide nozzle plate bar equipped with 128 injection needles, enabling simultaneous herbicide application to multiple weeds. In an open bean field, the detection accuracy of the proposed Spray-Net achieved a mean Intersection over Union (mIoU) of 88.6% for bean instances and 90.9% for weed instances. Furthermore, the system demonstrated a detection speed of 28 frames per second (fps) and a hitting accuracy of 86.1%. Notably, the proposed weeding machine boasts a weeding capacity of up to 4266 weeds per second with 128 nozzles in operation. The proposed pin-precision weeding machine represents a pioneering approach in environmentally friendly, intelligent weed management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 110990"},"PeriodicalIF":8.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219632","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":"Real-time soil moisture mapping using scalable RF sensor networks","authors":"Hongjun Yu ,&nbsp;Erik Muller ,&nbsp;Alex Mcbratney ,&nbsp;Salah Sukkarieh","doi":"10.1016/j.compag.2025.110979","DOIUrl":"10.1016/j.compag.2025.110979","url":null,"abstract":"<div><div>Soil moisture mapping is critical for precision irrigation, crop health, and water management, yet traditional approaches are limited by sparse sampling and lack of adaptability in large or dynamic fields. This paper presents a real-time soil moisture mapping framework that leverages scalable RF sensor networks, Gaussian Process Regression (GPR), and a cost-function-based optimization scheme. The system dynamically calculates optimal pixel sizes and positions, enabling smooth transitions between soil moisture maps as the underlying network topology changes due to node failures, communication dropouts, or reconfigurations. GPR is employed to filter noisy Received Signal Strength Indicator (RSSI) values and interpolate missing data, while the cost function balances mapping accuracy with consistency across RSSI-to-moisture projections and probe measurements. The proposed approach was validated through simulation and field trials in Cobbitty, NSW, demonstrating adaptability to asynchronous data streams, scalability with network size, and reliable accuracy, achieving a mean absolute error of 1.28% and a mean bias of -0.277% compared to ground-truth probes. These results highlight the potential of this framework to provide robust, real-time soil moisture monitoring for precision agriculture and large-scale field deployment.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 110979"},"PeriodicalIF":8.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219687","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":"Geometry-based point cloud fusion of dual-layer UAV photogrammetry and a modified unsupervised generative adversarial network for 3D tree reconstruction in semi-arid forests","authors":"Marziye Ghasemi ,&nbsp;Hooman Latifi ,&nbsp;Yaghoub Iranmanesh","doi":"10.1016/j.compag.2025.111024","DOIUrl":"10.1016/j.compag.2025.111024","url":null,"abstract":"<div><div>We present the first application of geometry-based relationship constraints for point-cloud registration and unsupervised 3D reconstruction of tree structure in semi-arid forest using unmanned aerial vehicle (UAV) photogrammetry. Accurate three-dimensional (3D) reconstruction of tree structure is essential for a plethora of subsequent tasks like assessing ecosystem health and informing sustainable forest management strategies, in particular over ecologically sensitive arid and semi-arid ecosystems that increasingly face decline due to prevalence of environmental stressors. This highlights the need for high-resolution geospatial monitoring approaches. While UAV-based photogrammetry offers a flexible and cost-effective means of capturing forest structure, conventional top-of-canopy imaging fails to sufficiently represent critical under-canopy features, including stem morphology and lower crown structure. Here, we suggest an integrated 3D reconstruction framework that combines dual-layer UAV photogrammetry, acquiring data from both above and below the canopy, with an innovative geometry-based point cloud registration method. Unlike conventional approaches like Iterative Closest Point (ICP) and Random Sample Consensus (RANSAC), this method leverages spatial relationships among individual trees to robustly align multi-view point clouds acquired under occluded and variable conditions. To further refine the reconstructed tree models, we suggest an updated unsupervised Generative Adversarial Network (Denoise-GAN), enabling both noise reduction and structural completion without reliance on labeled training data. The resulting models were used to extract key phenotypic features with high accuracy compared to reference data (root collar diameter (DRC) R² = 0.93, height R² = 0.97,Crown area R² = 0.99, number of stems R² = 1), providing vital indicators for quantifying forest structure and health. The presented methodology not only enhances the completeness and accuracy of 3D tree reconstruction in semi-arid forest, but also represents a significant advancement toward a scalable, data-driven semi-arid forest monitoring system. This workflow offers substantial potential for ecological applications, particularly in degraded and topographically complex ecosystems.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111024"},"PeriodicalIF":8.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219573","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":"A lightweight rotating target detection method for rice leaf blast based on improved YOLOv8n","authors":"Qiang Cao ,&nbsp;Jinpeng Li ,&nbsp;Yongsheng Liu ,&nbsp;JinXuan Li ,&nbsp;Sixu Jin ,&nbsp;Fenghua Yu ,&nbsp;Shuai Feng ,&nbsp;Tongyu Xu","doi":"10.1016/j.compag.2025.111026","DOIUrl":"10.1016/j.compag.2025.111026","url":null,"abstract":"<div><div>Rice leaf blast significantly threatens rice quality and yield, necessitating efficient and precise identification methods for effective field management. Current methods face challenges in accurately detecting leaf blast and distinguishing dense targets due to their small size, scale variation, and dense distribution. This paper proposes a lightweight rotational rice leaf blast detection algorithm named Ro-YOLOv8-PKI. The algorithm adopts Oriented Bounding Boxes (OBB) over traditional Horizontal Bounding Boxes (HBB), uses Gaussian transform for target localization, and replaces ProbIoU with CIoU loss function to improve the accuracy of detecting rotated targets. To achieve model lightweight and improve detection performance to small targets, we replace the 32-fold downsampling-based feature fusion network with a 16-fold downsampling multi-scale feature fusion network. An improved C2f-PKI module is introduced to enhance multi-scale feature extraction and increase the model’s perception of critical regions and attention to central features. Experimental results show that Ro-YOLOv8-PKI outperforms the YOLOv8n baseline, improving F1 score and mean Average Precision (mAP) by 5.8 % and 9.6 %, respectively, while reducing parameters and model size by 69.1 % and 62.7 %. Additionally, the model achieves mAP gains of 2.3 %, 2.2 %, and 3.1 % over other rotated target detection algorithms, including ROI-Transformer, ReDet, and S2-Anet. This approach offers a practical reference for lightweight rice disease detection in natural environments and presents a new perspective on traditional parallel bounding box-based detection methods. An application has also been developed to demonstrate the real-world applicability of Ro-YOLOv8-PKI in field conditions. Part of the rice blast test dataset used in this study and the sheath blight dataset for future research are available at: <span><span>https://github.com/qingyun259/RiceLeafBlastDataset</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111026"},"PeriodicalIF":8.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219690","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":"A systematic literature review of wearable sensor technologies used in poultry research","authors":"Manita Kafle ,&nbsp;Supun Chathuranga Nabadawa Hewage ,&nbsp;Anna Bradtmueller ,&nbsp;Blair Caitlin Downey ,&nbsp;Tom Tabler ,&nbsp;Yang Zhao","doi":"10.1016/j.compag.2025.111030","DOIUrl":"10.1016/j.compag.2025.111030","url":null,"abstract":"<div><div>Advancements in precision livestock farming (PLF) have led to the increasing use of wearable sensors in poultry since the 2000s. These sensor technologies allow for continuous, automated, and non-invasive monitoring of individual birds, providing valuable data on their activity, health, behavior, and welfare. This systematic literature review evaluates the use of various wearable sensors in poultry research and identifies potential factors that limit their application. This study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, involving extensive database searches to include all relevant references available as of June 27, 2024. In total, 84 studies published between 2004 and June 2024 were reviewed and analyzed. The studies reported the use of various sensor types, including Radio Frequency Identification (RFID) systems, accelerometers, Ultra-wideband (UWB) tracking systems, and Inertial Measurement Units (IMUs) to monitor individual behaviors such as feeding, drinking, ranging, perching, egg-laying, nesting, and to facilitate activity determination, movement recording, and location tracking. Sensor data helped researchers identify individual birds in group settings that were less active, socially disadvantaged, or at risk of health problems, thereby enhancing our understanding of the welfare and productivity concerns. Despite these benefits, sensors present challenges in terms of cost, as each bird requires an individual sensor. Moreover, issues such as false readings, malfunctioning devices, lost tags, and sensors that are large or heavy, and require frequent adjustments, add labor concerns and device practicality. Future efforts should focus on addressing these limitations by developing more practical, affordable, and suitable sensor systems to improve adoption. Involving farmers and poultry professionals in the design and testing process will be essential to ensure that these technologies meet real-world needs and contribute to more efficient and sustainable poultry production.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111030"},"PeriodicalIF":8.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219575","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":"Hierarchical attention and feature enhancement network for multi-scale small targets in pine wilt disease","authors":"Jiajun Wang ,&nbsp;Li Jin ,&nbsp;Fang Wang ,&nbsp;Hongping Zhou ,&nbsp;Haifeng Lin","doi":"10.1016/j.compag.2025.111037","DOIUrl":"10.1016/j.compag.2025.111037","url":null,"abstract":"<div><div>Pine Wilt Disease (PWD) is a forest disease caused by the pine wood nematode, which poses a serious threat to the health of pine trees. This disease leads to the death of pine trees and disrupts the stability and biodiversity of the forest ecosystem. Early detection and prevention can maximize the success rate of treating pitch canker disease and minimize its impact on the environment. Therefore, early detection and control of pitch canker disease is of utmost importance. However, existing models often struggle to distinguish the early infection characteristics from the noise in the complex background environment, resulting in a high rate of false detections. In response to these challenges, our research introduces HAFENet, a robust detection model tailored for small PWD-infected targets. HAFENet incorporates a Two-Stage Attention Fusion Module, designed to effectively extract target features in complex environments. This module adopts a self-attention cross-mechanism to fuse the global features and local features processed by the weight learning module, thereby paying more attention to small target regions and improving the model’s accuracy in early small target detection tasks. Additionally, to prevent redundant features in the environment from obscuring the subtle early infection characteristics, we designed an Information Integrity Convolution (IIConv). This dual-branch structure processes redundant and important features separately and then merges them using a stacking technique, which suppresses irrelevant information interference; Furthermore, HAFENet introduces a DeCoupled Head to separately optimize the classification and localization heads. We utilize Normalized Wasserstein Distance to provide more effective localization error feedback for detection boxes. Validation on our established small-target PWD dataset shows that HAFENet achieves an accuracy of 86.0% for early-stage detection and 96.7% for late-stage detection, representing improvements of 4.3% and 1.1% over the baseline, respectively. Compared with the existing mainstream models, HAFENet achieves the highest accuracy in the detection of small targets at the early stage of pine wood nematode infection. Additionally, it exhibits strong anti-interference capabilities on test sets with added noise and blur. These results indicate that HAFENet can maintain robust performance even in harsh environments with noisy backgrounds, highlighting its potential for wide application in forest protection and management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111037"},"PeriodicalIF":8.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219685","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":"A dedicated review of robotic pruners: current technologies, challenges, and future directions","authors":"Navid Nouri ,&nbsp;Hussein Gharakhani","doi":"10.1016/j.compag.2025.111013","DOIUrl":"10.1016/j.compag.2025.111013","url":null,"abstract":"<div><div>In light of labor shortages, the transition towards automation and robotic pruning has become indispensable in modern agriculture. Recent advancements in pruning robotic technologies, particularly through integrating advanced machine vision algorithms, have addressed challenges in accurate branch detection for pruning tasks, significantly enhancing pruning operations’ precision and overall performance. While numerous studies have been conducted on pruning robots, comprehensive reviews about the conducted research projects are scant. This review seeks to offer a fresh perspective on the key components of pruning robots, including the perception system, platform, manipulator, end-effector, path-planning algorithms, and control systems. By evaluating existing research, this study identifies the most effective solutions to current challenges and provides forward-looking recommendations for improving the design and functionality of the essential components of a robotic pruning system.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111013"},"PeriodicalIF":8.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219553","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":"Cross-domain adversarial learning for forage mapping and alpha-diversity assessment from UAV hyperspectral imagery in desert rangelands","authors":"Tao Zhang ,&nbsp;Chuanzhong Xuan ,&nbsp;Zhaohui Tang ,&nbsp;Xinyu Gao ,&nbsp;Fei Cheng ,&nbsp;Suhui Liu","doi":"10.1016/j.compag.2025.111001","DOIUrl":"10.1016/j.compag.2025.111001","url":null,"abstract":"<div><div>Accurate assessment of forage distribution and diversity is crucial for sustainable grazing management in desert rangelands. Unmanned aerial vehicle (UAV)-based hyperspectral imagery provides a robust data source for the fine-scale monitoring of forage resources. However, the complex nature of grassland environments, combined with the high cost and time-consuming process of sampling, results in a scarcity of labeled samples, which limits the representational ability of models for downstream tasks. To address these challenges, this study proposes the domain-adversarial multi-view contrastive network (DA-MVCNet) for forage mapping and alpha (<span><math><mi>α</mi></math></span>)-diversity assessment in desert rangelands. The network incorporates a domain adversarial strategy to mitigate discrepancies in feature distribution between the source and target domains, and employs a multi-view supervised contrastive learning module to promote feature alignment, thereby enhancing feature discrimination in the target domain. Experimental results demonstrate that DA-MVCNet achieves an overall accuracy of 92.52% in forage classification, outperforming other state-of-the-art methods, while maintaining low computational complexity with only 0.11 G FLOPs and 0.30 M parameters. Furthermore, we mapped the spatial distributions of <span><math><mi>α</mi></math></span>-diversity indices – including species richness, the Shannon–Wiener index, the Simpson index, and Pielou’s evenness index – using a grid-based approach. Results indicate that with increasing grazing pressure, all four diversity indices significantly decreased (<span><math><mrow><mi>P</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>), effectively revealing the impact of grazing disturbance on community structure. This study provides a new pathway for forage mapping and diversity assessment via UAV hyperspectral remote sensing, offering technical support for the intelligent monitoring and management of rangeland ecosystems. The code is available at <span><span>https://github.com/zhang2508/DA-MVCNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111001"},"PeriodicalIF":8.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219689","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}