[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1970":3,"related-1970":94},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":69,"slug":72,"view_count":64,"doi":73,"paper":74,"created_at":93},1970,"Real-time hass avocado ripeness detection using close-range UAV imagery and YOLO-based deep learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102547","Rapid and accurate assessment of Hass avocado ripeness is essential for improving harvest planning, optimizing picking time, increasing productivity, and reducing postharvest losses. This study presents a real-time system for Hass avocado ripeness detection using close-range UAV imagery and YOLO-based deep learning models. A UAV equipped with a camera captured field images, which were transmitted in real time to a processing station through SCRCPY for fruit detection and ripeness classification. The proposed approach consists of two sequential stages. First, a YOLOv8 object detection model was trained using a dataset of 340 manually annotated images generated through real-world adaptation and labeled in Roboflow. The detector achieved a precision of 0.7951, a recall of 0.7849, an mAP@0.5 of 0.8388, and an mAP@0.5:0.95 of 0.6308, demonstrating reliable fruit localization under field conditions. Second, a YOLOv8 classification model was trained on a dataset of 20,231 cropped fruit images derived from an initial collection of 14,710 images categorized into five ripeness stages (unripe, midripe, ripe, fullripe, and overripe). The classification model achieved an accuracy of 0.79 , a macro-average F1-score of 0.77, a weighted-average F1-score of 0.78, a Top-1 accuracy of 0.78, and a Top-5 accuracy of 1.00, confirming its effectiveness for ripeness assessment. The complete system was evaluated under real agricultural conditions with variable illumination and partial fruit occlusion, demonstrating that the integration of close-range UAV imagery and deep learning provides an effective tool for automated avocado ripeness assessment in precision agriculture. The proposed approach was evaluated using close-range UAV imagery acquired in a commercial orchard; therefore, additional validation under different orchards, environmental conditions, and acquisition scenarios is recommended to further assess its generalization capability.","对哈斯鳄梨成熟度的快速准确评估对于改进采收规划、优化采摘时间、提高生产率以及减少采后损失至关重要。本研究提出了一种基于近距无人机影像和YOLO深度学习模型的哈斯鳄梨成熟度实时检测系统。搭载摄像头的无人机采集田间图像，并通过SCRCPY实时传输至处理站进行果实检测和成熟度分类。所提出的方法包括两个连续阶段。首先，利用340张通过实际场景适配生成并在Roboflow中标注的人工标注图像数据集训练YOLOv8目标检测模型。该检测器实现了0.7951的精确率、0.7849的召回率、0.8388的mAP@0.5和0.6308的mAP@0.5:0.95，表明其在田间条件下具有可靠的果实定位能力。其次，利用从初始14,710张图像中提取的20,231张裁剪果实图像数据集训练YOLOv8分类模型，这些图像被分为五个成熟度阶段（未熟、半熟、成熟、完熟和过熟）。分类模型实现了0.79的准确率、0.77的宏平均F1分数、0.78的加权平均F1分数、0.78的Top-1准确率和1.00的Top-5准确率，证实了其在成熟度评估中的有效性。整个系统在光照变化和果实部分遮挡的真实农业条件下进行了评估，结果表明近距无人机影像与深度学习的集成为精准农业中的鳄梨成熟度自动评估提供了有效工具。所提出的方法是在商业果园中获取的近距无人机影像上评估的，因此建议在不同果园、环境条件和采集场景下进行额外验证，以进一步评估其泛化能力。",null,"Smart Agricultural Technology","2026-09-06T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,12,4,1,"研究提出基于无人机和YOLO的鳄梨成熟度实时检测系统，方法新颖且数据详实，对精准农业有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","无人机","农业人工智能","目标检测","鳄梨",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,54,62],{"code":33,"score":46,"supported":47,"title":48,"abstract":51,"tags":53},27,true,[49,50],"deep learning","YOLO",[30,52,49,50],"深度学习",[29,30],{"code":55,"score":56,"supported":47,"title":57,"abstract":58,"tags":61},"I1",7,[],[59,60],"精准农业","precision agriculture",[27],{"code":63,"score":64,"supported":47,"title":65,"abstract":66,"tags":68},"B2",3,[],[67],"orchard",[],[70,71],"农业人工智能 智慧农业 目标检测 无人机","农业人工智能 智慧农业","农业人工智能智慧农业目标检测无人机-1970","10.1016\u002Fj.atech.2026.102547",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":85,"oa_url":6,"card":86,"direction":90,"ingested_from":92},"W7210263003",[77,80,82],{"name":78,"orcid":79},"Verner Leonidas TUDELA TACO","https:\u002F\u002Forcid.org\u002F0009-0000-2214-3377",{"name":81,"orcid":9},"Wilson Emerson QUISPE ACCOSTUPA",{"name":83,"orcid":84},"Erasmo Sulla Espinoza","https:\u002F\u002Forcid.org\u002F0000-0002-1223-1223",0,{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"用无人机近景图像和YOLO深度学习实时检测哈斯鳄梨成熟度，实现精准农业应用。","YOLOv8检测与分类模型，340张标注图像和20231张裁剪图像，通过SCRC","检测mAP@0.5达0.8388，分类准确率0.79，在田间条件下有效评估成熟度。","智慧农业 \u002F 农业物联网","可研究多果园、多环境下的泛化性，或结合多光谱图像提升成熟度分类精度。","openalex","2026-09-09T23:30:03.462565Z",{"total":95,"page":22,"page_size":95,"items":96},6,[97,163,223,288,357,432],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":112,"tags":114,"primary_topic":116,"secondary_topics":117,"classification":118,"search_phrases":136,"slug":139,"view_count":56,"doi":140,"paper":141,"created_at":162},2798,"Deep Learning-Based Small-Object Detection in UAV Imagery: A Symmetry-Informed Review of Scale Variation and Scenario Constraints","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18091548","As UAV platforms are increasingly employed in military reconnaissance, disaster monitoring, precision agriculture, and other remote-sensing applications, small-object detection in UAV imagery is a critical yet challenging task. Existing reviews primarily address either general small-object detection or general object detection in UAV imagery, whereas reviews specifically devoted to small-object detection in UAV imagery remain scarce. This review examines existing deep learning-based research on small-object detection in UAV imagery from a problem-driven and deployment-oriented perspective. A symmetry-informed perspective was also adopted to analyze how variations in object-scale, viewpoint, imaging quality, and scene conditions affected feature representation and detection stability. Building on five major challenges in UAV small-object detection, a three-tier framework encompassing detection paradigms, key technical strategies, and scenario-specific constraints was established. Representative UAV datasets were further examined to characterize object-scale distributions, while existing methods were reviewed in terms of multiscale representation, feature enhancement, training optimization, and lightweight deployment. The applicability of different technical approaches was then discussed across five typical UAV scenarios. In addition, under relatively consistent experimental conditions, representative detection models based on two-stage, one-stage, and Transformer-based paradigms were analyzed in terms of detection accuracy, computational efficiency, and deployment feasibility. Finally, current limitations and future directions were summarized for scale-robust, scenario-adaptive, and resource-aware small-object detection in UAV imagery.","随着无人机平台在军事侦察、灾害监测、精准农业等遥感应用中的日益普及，无人机图像中的小目标检测成为一项关键但具有挑战性的任务。现有综述主要关注通用小目标检测或无人机图像中的通用目标检测，而专门针对无人机图像中小目标检测的综述仍然匮乏。本综述从问题驱动和部署导向的视角，审视了现有的基于深度学习的无人机图像小目标检测研究。同时采用对称性视角，分析了目标尺度、视角、成像质量和场景条件的变化如何影响特征表示和检测稳定性。基于无人机小目标检测中的五大挑战，构建了一个涵盖检测范式、关键技术策略和场景特定约束的三层框架。进一步考察了代表性无人机数据集以刻画目标尺度分布，同时从多尺度表示、特征增强、训练优化和轻量化部署等方面综述了现有方法。随后讨论了不同技术方法在五种典型无人机场景中的适用性。此外，在相对一致的实验条件下，对基于两阶段、单阶段和Transformer范式的代表性检测模型在检测精度、计算效率和部署可行性方面进行了分析。最后，总结了当前局限性及未来方向，以推动无人机图像中尺度鲁棒、场景自适应和资源感知的小目标检测研究。","Symmetry","2026-09-16T00:00:00Z",76,{"impact":107,"substance":108,"depth":19,"authority":109,"freshness":110,"relevant":22,"comment":111},16,20,13,9,"无人机小目标检测的系统性综述，提出对称性视角与三层框架，对农业遥感与精准农业部署有实质参考价值。",[113],{"name":103,"url":100},[27,28,29,115,30],"农业遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":119},{"candidates":120,"agricultural_evidence":47},[121,126,130],{"code":33,"score":122,"supported":47,"title":123,"abstract":124,"tags":125},19,[49],[30,52,49],[29,30],{"code":55,"score":56,"supported":47,"title":127,"abstract":128,"tags":129},[],[59,60],[27],{"code":131,"score":21,"supported":47,"title":132,"abstract":133,"tags":135},"I3",[],[134],"遥感",[115],[137,138],"农业人工智能 农业遥感 智慧农业 目标检测","农业人工智能 农业遥感","农业人工智能农业遥感智慧农业目标检测-2798","10.3390\u002Fsym18091548",{"doi":140,"openalex_id":142,"authors":143,"venue":103,"cited_by_count":85,"oa_url":100,"card":156,"direction":160,"ingested_from":92},"W7213430256",[144,146,149,151,153],{"name":145,"orcid":9},"Ling Wen",{"name":147,"orcid":148},"Anmin Gong","https:\u002F\u002Forcid.org\u002F0000-0002-2715-2296",{"name":150,"orcid":9},"Caihong Ma",{"name":152,"orcid":9},"Shengzhou Ma",{"name":154,"orcid":155},"Yanzhe Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-6529-1247",{"tldr":157,"method":158,"finding":159,"direction":160,"opportunity":161},"综述无人机图像小目标检测的深度学习研究，提出对称性视角与三层框架。","文献综述，分析代表性数据集与两阶段、单阶段、Transformer模型。","尺度变化和场景约束是核心挑战，需尺度鲁棒、场景自适应和资源感知检测。","农业遥感与作物表型","可探索农业场景下尺度自适应与轻量化部署的平衡，以及跨场景泛化的小目标检测。","2026-09-17T23:30:37.479759Z",{"id":164,"title":165,"url":166,"summary":167,"summary_zh":9,"content":9,"source_name":168,"source_url":9,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":175,"tags":177,"primary_topic":180,"secondary_topics":186,"classification":188,"search_phrases":210,"slug":213,"view_count":85,"doi":9,"paper":214,"created_at":222},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",69,{"impact":107,"substance":172,"depth":173,"authority":110,"freshness":95,"relevant":22,"comment":174},21,17,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[176],{"name":168,"url":166},[27,29,30,178,179],"无人机遥感","棉花病害",{"code":181,"slug":182,"name":183,"path":184,"parent_code":185},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[187],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":189},{"candidates":190,"agricultural_evidence":47},[191,195,202,206],{"code":33,"score":19,"supported":47,"title":192,"abstract":193,"tags":194},[52,50],[],[29,30],{"code":181,"score":196,"supported":47,"title":197,"abstract":199,"tags":201},11,[198],"棉花",[200],"cotton",[],{"code":55,"score":22,"supported":14,"title":203,"abstract":204,"tags":205},[],[],[27],{"code":131,"score":22,"supported":14,"title":207,"abstract":208,"tags":209},[],[],[178],[211,212],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",{"doi":9,"openalex_id":9,"authors":215,"venue":9,"cited_by_count":85,"oa_url":9,"card":216,"direction":160,"ingested_from":221},[],{"tldr":217,"method":218,"finding":219,"direction":160,"opportunity":220},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","agent","2026-10-22T00:07:09.816790Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":228,"content":9,"source_name":229,"source_url":226,"published_at":230,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":231,"score_detail":232,"sources":235,"tags":237,"primary_topic":238,"secondary_topics":239,"classification":240,"search_phrases":258,"slug":261,"view_count":85,"doi":262,"paper":263,"created_at":287},5393,"IMPLEMENTATION OF META-HEURISTIC OPTIMIZATION ALGORITHMS IN CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION AND CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr251214006i","CNNs have grown to form the backbone of modern computer vision systems, producing outstanding object detection and classification performance within diverse application domains. However, the manual development of suitable CNN architectures and, correspondingly, the selection of optimal hyperparameter sets are still inconvenient and time-consuming processes. The aim of this survey is to contribute a comprehensive review regarding how meta-heuristic optimization algorithms such as Genetic Algorithms, Particle Swarm Optimization, and other swarm intelligence-based methods are embedded in the CNN-based pipelines with the intent to achieve better detection and classification results. We gather recent progress regarding automated architecture search, hyperparameter tuning, model compression, and ensemble learning. We identify how hybrid optimization approaches consistently yield superior accuracy with computational efficiency by systematically reviewing applications in medical imaging, agriculture, vehicular recognition, and remote sensing. Optimized CNN models outperform their baseline versions many times, especially in health diagnostics, where several fine-tuned architectures reach accuracies greater than 99%. This survey provides a structured and updated overview of methodological developments, implementation practices, and emerging research directions for those practitioners and researchers who will apply meta-heuristic optimization within deep learning-based computer vision systems.","卷积神经网络（CNNs）已发展成为现代计算机视觉系统的核心骨干，在多个应用领域中展现出卓越的目标检测与分类性能。然而，人工设计合适的CNN架构并相应地选择最优超参数集仍然是繁琐且耗时的过程。本综述旨在全面回顾遗传算法、粒子群优化及其他群体智能方法等元启发式优化算法如何嵌入基于CNN的流程中以获得更优的检测与分类结果。我们汇集了自动化架构搜索、超参数调优、模型压缩和集成学习方面的最新进展。通过系统梳理在医学影像、农业、车辆识别和遥感领域的应用，我们揭示了混合优化方法如何以计算效率持续实现更优的精度。优化后的CNN模型在多数情况下远超其基线版本，尤其在健康诊断领域，若干精细调优的架构达到了超过99%的准确率。本综述为将在基于深度学习的计算机视觉系统中应用元启发式优化的从业者和研究者，提供了关于方法学进展、实施实践和新兴研究方向的结构化且更新的概述。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",61,{"impact":233,"substance":19,"depth":17,"authority":20,"freshness":233,"relevant":22,"comment":234},8,"综述系统梳理元启发式优化在CNN视觉任务中的嵌入方法，含农业应用，但属通用方法综述，非农业专属突破。",[236],{"name":229,"url":226},[27,29,30,134],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":241},{"candidates":242,"agricultural_evidence":47},[243,249,254],{"code":33,"score":196,"supported":47,"title":244,"abstract":245,"tags":248},[],[30,52,246,49,247],"计算机视觉","computer vision",[29,30],{"code":131,"score":56,"supported":47,"title":250,"abstract":251,"tags":253},[],[134,252],"remote sensing",[134],{"code":55,"score":22,"supported":14,"title":255,"abstract":256,"tags":257},[],[],[27],[259,260],"元启发式优化 卷积神经网络","CNN 目标检测","元启发式优化卷积神经网络-5393","10.22190\u002Ffuacr251214006i",{"doi":262,"openalex_id":264,"authors":265,"venue":229,"cited_by_count":85,"oa_url":226,"card":281,"direction":160,"ingested_from":92},"W7222920697",[266,269,272,275,278],{"name":267,"orcid":268},"Nikola Ivačko","https:\u002F\u002Forcid.org\u002F0000-0003-4367-119X",{"name":270,"orcid":271},"Ivan S Ciric","https:\u002F\u002Forcid.org\u002F0000-0003-0430-8937",{"name":273,"orcid":274},"Milica Tasić","https:\u002F\u002Forcid.org\u002F0009-0001-9160-6379",{"name":276,"orcid":277},"Ljiljana Radović","https:\u002F\u002Forcid.org\u002F0000-0002-4099-1289",{"name":279,"orcid":280},"Žarko Ćojbašić","https:\u002F\u002Forcid.org\u002F0000-0002-4581-1048",{"tldr":282,"method":283,"finding":284,"direction":285,"opportunity":286},"综述元启发式算法嵌入CNN流程以优化目标检测与分类的研究进展。","系统综述遗传算法、粒子群等群智能方法用于CNN架构搜索与超参调优。","混合优化方法在医学影像、农业等领域兼顾精度与效率，部分模型准确率超99%。","农业人工智能与决策模型","农业场景下元启发式优化CNN的架构搜索与轻量化部署仍缺乏系统基准与田间验证。","2026-10-20T23:30:19.233167Z",{"id":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":9,"source_name":10,"source_url":291,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":299,"tags":301,"primary_topic":304,"secondary_topics":305,"classification":312,"search_phrases":330,"slug":333,"view_count":22,"doi":334,"paper":335,"created_at":356},4975,"Detection of mounting behavior and mounted ewes in group-housed environments using HSM-YOLO algorithm","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102639","The detection of mounting behavior and the mounted ewe is critical for estrus monitoring in sheep. However, existing methods suffer from low detection efficiency and high false detection rates due to the similarity between non-sexual and sexual mounting behaviors and the complexity of group-housed environments. To address these challenges, this study introduced a fine-grained classification of mounting behaviors into sexual and non-sexual categories, and further proposed an improved detection algorithm named Hu Sheep Mounting-YOLO (HSM-YOLO) based on the YOLOv11n architecture for detecting sheep mounting behavior and the corresponding mounted ewes. The HSM-YOLO algorithm employed depthwise separable convolution (DWSConv) to reduce the model complexity. To improve behavioral feature extraction and pose discrimination capabilities, it further integrated the C3k2_RepViTBlock-RepBottleneck (C3k2_BiRep) module, an efficient multi-scale attention (EMA) module, and the Inner-SIoU loss function. To evaluate the performance of the proposed algorithm, comparative experiments were conducted on a multiple scenarios dataset. The experimental results showed that the HSM-YOLO model achieved an F1 score of 88.1%, a mean Average Precision (mAP) of 90.2%, with 2.2M parameters and 5.4G floating-point operations (FLOPs). Compared to the baseline YOLOv11n model, HSM-YOLO improved the F1 score by 5.8% and the mAP by 5.7%, while reducing both model parameters and computational complexity. When deployed and tested on an RTX 4090, the system delivered an average detection speed of 47.9 frames per second (FPS). Moreover, despite considerable variation in detection performance across the four different imaging scenarios, HSM-YOLO consistently demonstrated superior detection accuracy. These results confirm that the HSM-YOLO provides a robust and efficient solution for detecting sheep mounting behaviors and mounted ewes in complex and variable scenarios, paving the way for the development of automated estrus monitoring systems.","检测羊只的爬跨行为及被爬跨母羊对于绵羊发情监测至关重要。然而，由于非性爬跨行为与性爬跨行为之间的相似性以及群养环境的复杂性，现有方法存在检测效率低和误检率高的问题。为解决这些挑战，本研究将爬跨行为细粒度分类为性爬跨和非性爬跨两类，并进一步提出了一种基于YOLOv11n架构的改进检测算法——湖羊爬跨-YOLO（HSM-YOLO），用于检测绵羊爬跨行为及相应的被爬跨母羊。HSM-YOLO算法采用深度可分离卷积（DWSConv）以降低模型复杂度。为提高行为特征提取和姿态判别能力，该算法进一步集成了C3k2_RepViTBlock-RepBottleneck（C3k2_BiRep）模块、高效多尺度注意力（EMA）模块以及Inner-SIoU损失函数。为评估所提算法的性能，在多场景数据集上进行了对比实验。实验结果表明，HSM-YOLO模型的F1分数达到88.1%，平均精度均值（mAP）达到90.2%，参数量为2.2M，浮点运算量为5.4G。与基线YOLOv11n模型相比，HSM-YOLO的F1分数提高了5.8%，mAP提高了5.7%，同时降低了模型参数量和计算复杂度。在RTX 4090上部署测试时，系统平均检测速度达到47.9帧每秒（FPS）。此外，尽管在四种不同成像场景下检测性能存在较大差异，HSM-YOLO始终展现出优越的检测精度。这些结果证实，HSM-YOLO为复杂多变场景下检测绵羊爬跨行为和被爬跨母羊提供了一种鲁棒且高效的解决方案，为自动化发情监测系统的开发奠定了基础。","2026-10-12T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":297,"freshness":110,"relevant":22,"comment":298},14,"提出HSM-YOLO算法实现羊只爬跨行为与受体母羊检测，为自动化发情监测提供高效方案，方法新颖、指标扎实，具行业参考价值。",[300],{"name":10,"url":291},[27,29,30,302,303],"畜牧养殖","羊只发情监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[306],{"code":307,"slug":308,"name":309,"path":310,"parent_code":311},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",{"status":40,"locked":14,"version":41,"method":42,"evidence":313},{"candidates":314,"agricultural_evidence":47},[315,319,326],{"code":33,"score":109,"supported":47,"title":316,"abstract":317,"tags":318},[50],[50],[29,30],{"code":307,"score":110,"supported":47,"title":320,"abstract":321,"tags":325},[],[322,323,324],"湖羊","绵羊","sheep",[],{"code":55,"score":22,"supported":14,"title":327,"abstract":328,"tags":329},[],[],[27],[331,332],"HSM-YOLO 羊只爬跨行为 检测","绵羊 发情监测 目标检测","HSM-YOLO羊只爬跨行为检测-4975","10.1016\u002Fj.atech.2026.102639",{"doi":334,"openalex_id":336,"authors":337,"venue":10,"cited_by_count":85,"oa_url":291,"card":351,"direction":285,"ingested_from":92},"W7221573347",[338,340,342,344,347,349],{"name":339,"orcid":9},"Yanchao Wang",{"name":341,"orcid":9},"Ming Gao",{"name":343,"orcid":9},"Zhixin Hua",{"name":345,"orcid":346},"Asim Shakeel","https:\u002F\u002Forcid.org\u002F0000-0003-2942-9081",{"name":348,"orcid":9},"Chengyu Ren",{"name":350,"orcid":9},"Kaiying Wang",{"tldr":352,"method":353,"finding":354,"direction":285,"opportunity":355},"提出HSM-YOLO算法，在群养环境下检测羊爬跨行为及被爬跨母羊，用于发情监测。","基于YOLOv11n改进，引入DWSConv、C3k2_BiRep、EMA和In","F1达88.1%、mAP达90.2%，参数量2.2M，较基线提升且更轻量，检测速度47.9 FPS。","可延伸至多物种发情行为细粒度识别与轻量化边缘部署，结合时序信息提升监测实时性。","2026-10-15T23:30:03.393878Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":9,"source_name":363,"source_url":360,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":364,"score_detail":365,"sources":367,"tags":369,"primary_topic":372,"secondary_topics":377,"classification":383,"search_phrases":406,"slug":409,"view_count":22,"doi":410,"paper":411,"created_at":431},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi",77,{"impact":107,"substance":172,"depth":19,"authority":109,"freshness":110,"relevant":22,"comment":366},"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[368],{"name":363,"url":360},[27,28,29,370,371],"遥感监测","病害预警",{"code":63,"slug":373,"name":374,"path":375,"parent_code":376},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[378,379],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":131,"slug":380,"name":381,"path":382,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":384},{"candidates":385,"agricultural_evidence":47},[386,393,397,402],{"code":63,"score":173,"supported":47,"title":387,"abstract":389,"tags":392},[388],"apple",[390,391,388],"苹果","葡萄",[],{"code":33,"score":17,"supported":47,"title":394,"abstract":395,"tags":396},[49],[52,49],[29],{"code":131,"score":20,"supported":47,"title":398,"abstract":400,"tags":401},[399],"multispectral",[399],[370],{"code":55,"score":56,"supported":47,"title":403,"abstract":404,"tags":405},[],[59,60],[27],[407,408],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":410,"openalex_id":412,"authors":413,"venue":363,"cited_by_count":85,"oa_url":425,"card":426,"direction":90,"ingested_from":92},"W7221558726",[414,417,420,422],{"name":415,"orcid":416},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":418,"orcid":419},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":421,"orcid":9},"Fatih Sinan Bir",{"name":423,"orcid":424},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":427,"method":428,"finding":429,"direction":160,"opportunity":430},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":9,"content":9,"source_name":437,"source_url":9,"published_at":438,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":439,"score_detail":440,"sources":442,"tags":444,"primary_topic":447,"secondary_topics":448,"classification":449,"search_phrases":471,"slug":474,"view_count":475,"doi":9,"paper":476,"created_at":483},4649,"[预印本]面向自主熟果采摘的智能语音引导UAV系统:基于视觉分类与自适应路径规划(IVG-UAV: An Intelligent Voice-Guided UAV System for Autonomous Ripe Fruit Harvesting with Vision-Based Classification and Adaptive Path Planning)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.09398","arXiv论文(2610.09398,2026年10月7日提交)提出IVG-UAV系统集成Whisper语音识别、LLM、计算机视觉成熟度分类与Gazebo仿真中的自适应路径规划,实现热带大型种植园自动采摘任务。系统在受控仿真环境验证,实际田间作业、复杂光照与多机协同性能待测。[预印本未经同行评议,仅仿真验证]。","arXiv 2026-10-07 [预印本]","2026-10-07T00:00:00Z",62,{"impact":20,"substance":19,"depth":107,"authority":233,"freshness":233,"relevant":22,"comment":441},"预印本提出语音引导无人机自主采摘系统，方法集成有新意但仅仿真验证，属细分领域技术进展。",[443],{"name":437,"url":435},[27,28,29,445,446],"采摘机器人","语音交互",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":450},{"candidates":451,"agricultural_evidence":47},[452,456,462,467],{"code":33,"score":21,"supported":47,"title":453,"abstract":454,"tags":455},[],[246],[29],{"code":457,"score":64,"supported":47,"title":458,"abstract":459,"tags":461},"A7",[],[460],"间作",[],{"code":463,"score":22,"supported":14,"title":464,"abstract":465,"tags":466},"F6",[],[],[445],{"code":55,"score":22,"supported":14,"title":468,"abstract":469,"tags":470},[],[],[27],[472,473],"IVG-UAV 采摘无人机","Whisper 语音识别 农业机器人","IVG-UAV采摘无人机-4649",2,{"doi":9,"openalex_id":9,"authors":477,"venue":9,"cited_by_count":85,"oa_url":9,"card":478,"direction":285,"ingested_from":221},[],{"tldr":479,"method":480,"finding":481,"direction":285,"opportunity":482},"提出IVG-UAV系统，用语音、视觉与路径规划实现仿真中自主采摘熟果。","集成Whisper语音识别、LLM、视觉成熟度分类与Gazebo自适应路径规划。","仿真环境验证可行，但真实田间、复杂光照与多机协同性能待测。","可研究真实果园复杂光照下的鲁棒视觉分类与多机协同采摘调度。","2026-10-11T00:09:47.894542Z"]