[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3811":3,"related-3811":100},{"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":45,"search_phrases":71,"slug":74,"view_count":22,"doi":75,"paper":76,"created_at":99},3811,"Automated extraction of architectural traits from apple tree branches via 3D instance segmentation and skeletonisation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104607","Accurate and efficient extraction of 3D phenotypic traits of apple tree branches is crucial for apple tree breeding and growth monitoring. Current methods, however, rely heavily on manual measurement, which are time-consuming, costly, and difficult to scale. Moreover, existing approaches based on deep learning and skeleton extraction still face critical challenges when processing high-density, large-scale point cloud data, including insufficient branch segmentation accuracy, suboptimal skeleton extraction quality, and a lack of category-specific fine-grained phenotypic quantification capabilities. To overcome these limitations, a dedicated framework for automated phenotypic trait extraction was presented. This approach consists of three key steps: (1) instance segmentation of branches from 3D data using the Relation3D model to distinguish individual branches; (2) skeletonisation of each segmented branch based on geometric characteristics and the L1-median algorithm to obtain fine-grained skeletal structures; and (3) quantitative trait extraction, where trunk height, branch count by category, and branch length are automatically computed. Experimental results demonstrate that Relation3D achieves high-precision instance segmentation, and our skeletonisation method reliably recovers branch geometry. Consequently, the framework accurately extracts key phenotypic traits, including branch number and length. This study provides an efficient and accurate solution for branch-level phenotyping, with significant potential to enhance apple tree breeding and precision horticulture.","准确高效地提取苹果树枝条的三维表型性状对于苹果树育种和生长监测至关重要。然而，现有方法严重依赖人工测量，耗时、成本高且难以规模化。此外，基于深度学习和骨架提取的现有方法在处理高密度、大规模点云数据时仍面临关键挑战，包括枝条分割精度不足、骨架提取质量欠佳以及缺乏类别特定的细粒度表型量化能力。为克服这些局限，本文提出了一种用于自动化表型性状提取的专用框架。该方法包括三个关键步骤：（1）利用Relation3D模型从三维数据中对枝条进行实例分割，以区分单个枝条；（2）基于几何特征和L1中位数算法对每个分割后的枝条进行骨架化，以获得细粒度骨架结构；（3）定量性状提取，自动计算主干高度、按类别统计的枝条数量和枝条长度。实验结果表明，Relation3D实现了高精度的实例分割，且本文的骨架化方法能够可靠地恢复枝条几何形态。因此，该框架能够准确提取关键表型性状，包括枝条数量和长度。本研究为枝条级表型分析提供了一种高效且准确的解决方案，在提升苹果树育种和精准园艺方面具有显著潜力。",null,"Biosystems Engineering","2026-09-29T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,21,18,14,9,1,"提出基于Relation3D实例分割与L1中值骨架化的苹果树枝条三维表型自动提取框架，方法新颖、结论可靠，对果树育种与精准园艺有实用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","三维点云","果树表型","苹果育种",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,59,66],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":58},17,true,[55],"apple",[35,57,55],"苹果",[],{"code":40,"score":60,"supported":53,"title":61,"abstract":62,"tags":65},8,[],[63,64],"深度学习","deep learning",[29,28],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"I1",[],[],[27],[72,73],"苹果树 三维点云 表型","苹果树 分枝 骨架提取","苹果树三维点云表型-3811","10.1016\u002Fj.biosystemseng.2026.104607",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":91,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7214750336",[79,82,85,87,89],{"name":80,"orcid":81},"Anjian Fei","https:\u002F\u002Forcid.org\u002F0009-0000-9349-9223",{"name":83,"orcid":84},"Jintao Wu","https:\u002F\u002Forcid.org\u002F0000-0003-4257-2288",{"name":86,"orcid":9},"Jian Zhuang",{"name":88,"orcid":9},"Leyou Yang",{"name":90,"orcid":9},"Hao Yang",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"提出3D实例分割与骨架化框架，自动提取苹果树枝条表型性状。","Relation3D实例分割、L1中值骨架化、高密度点云处理。","框架能高精度分割枝条并可靠提取枝长、枝数等表型性状。","农业遥感与作物表型","可扩展至多树种、多尺度动态监测，并与基因组关联推动智能育种。","openalex","2026-09-30T23:30:05.626390Z",{"total":101,"page":22,"page_size":101,"items":102},6,[103,214,283,349,404,462],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":115,"tags":117,"primary_topic":121,"secondary_topics":122,"classification":129,"search_phrases":185,"slug":188,"view_count":22,"doi":189,"paper":190,"created_at":213},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science","2026-10-14T00:00:00Z",81,{"impact":19,"substance":113,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":114},22,"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[116],{"name":109,"url":106},[27,28,118,119,120],"产量预测","多模态融合","遥感",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[123,124],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":125,"slug":126,"name":127,"path":128,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":46,"locked":14,"version":47,"method":48,"evidence":130},{"candidates":131,"agricultural_evidence":53},[132,139,145,151,158,165,172,179],{"code":40,"score":133,"supported":53,"title":134,"abstract":135,"tags":138},11,[],[63,136,137,64],"机器学习","machine learning",[28,119],{"code":33,"score":21,"supported":53,"title":140,"abstract":141,"tags":144},[],[57,142,143,55],"芒果","orchard",[],{"code":125,"score":60,"supported":53,"title":146,"abstract":147,"tags":150},[],[148,149],"无人机多光谱","multispectral",[118,120],{"code":67,"score":152,"supported":53,"title":153,"abstract":154,"tags":157},7,[],[155,156],"精准农业","precision agriculture",[27],{"code":159,"score":101,"supported":53,"title":160,"abstract":161,"tags":164},"H2",[],[162,163],"土壤","soil",[],{"code":166,"score":101,"supported":53,"title":167,"abstract":168,"tags":171},"I5",[],[169,170],"物联网","IoT",[],{"code":173,"score":174,"supported":53,"title":175,"abstract":176,"tags":178},"H6",3,[],[177],"农业生态",[],{"code":180,"score":174,"supported":53,"title":181,"abstract":182,"tags":184},"H7",[],[183],"气候适应",[],[186,187],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":189,"openalex_id":191,"authors":192,"venue":109,"cited_by_count":91,"oa_url":106,"card":206,"direction":212,"ingested_from":98},"W7221795850",[193,196,198,200,202,204],{"name":194,"orcid":195},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":197,"orcid":9},"Umarfarooque Momin",{"name":199,"orcid":9},"Sudeep Kumar E",{"name":201,"orcid":9},"Itfaq Ahmad Mir",{"name":203,"orcid":9},"Ab Rouf Khan",{"name":205,"orcid":9},"Ghulam Mohammad Mir",{"tldr":207,"method":208,"finding":209,"direction":210,"opportunity":211},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","农业人工智能与决策模型","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:08.750140Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":9,"source_name":220,"source_url":217,"published_at":221,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":222,"sources":226,"tags":228,"primary_topic":232,"secondary_topics":233,"classification":236,"search_phrases":257,"slug":260,"view_count":22,"doi":261,"paper":262,"created_at":282},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","2026-10-12T00:00:00Z",{"impact":223,"substance":18,"depth":19,"authority":224,"freshness":21,"relevant":22,"comment":225},16,13,"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。",[227],{"name":220,"url":217},[27,229,28,230,231],"无人机","遥感监测","病害预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[234,235],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":125,"slug":126,"name":127,"path":128,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":237},{"candidates":238,"agricultural_evidence":53},[239,244,248,253],{"code":33,"score":52,"supported":53,"title":240,"abstract":241,"tags":243},[55],[57,242,55],"葡萄",[],{"code":40,"score":17,"supported":53,"title":245,"abstract":246,"tags":247},[64],[63,64],[28],{"code":125,"score":249,"supported":53,"title":250,"abstract":251,"tags":252},12,[149],[149],[230],{"code":67,"score":152,"supported":53,"title":254,"abstract":255,"tags":256},[],[155,156],[27],[258,259],"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":261,"openalex_id":263,"authors":264,"venue":220,"cited_by_count":91,"oa_url":276,"card":277,"direction":212,"ingested_from":98},"W7221558726",[265,268,271,273],{"name":266,"orcid":267},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":269,"orcid":270},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":272,"orcid":9},"Fatih Sinan Bir",{"name":274,"orcid":275},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":278,"method":279,"finding":280,"direction":96,"opportunity":281},"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":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":9,"source_name":289,"source_url":286,"published_at":290,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":291,"score_detail":292,"sources":295,"tags":297,"primary_topic":300,"secondary_topics":301,"classification":303,"search_phrases":319,"slug":322,"view_count":91,"doi":323,"paper":324,"created_at":348},4744,"An empirical performance comparison of deep learning models for apple leaf disease recognition","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10791-026-10573-4","Currently, Biotic and abiotic factors, in agriculture, are estimated to be responsible for around 22% of the total losses in crop production. It is therefore very important, but at the same time very difficult for farmers to identify these constraints at the earliest possible stage. The most recent developments in computer vision have made it possible to identify as well as classify plant lesions at a primary level. Various deep learning techniques have made it possible to detect biotic stress in apple plant leaves with a very high degree of accuracy. Hence, automated segmentation of images of diseased leaves has become a leading research theme all over the world. In this paper, a dataset from FGVC7, which is an open, source dataset and contains four different classes of leaf diseases of the apple crop is used to measure the performance of the proposed model. A three, step image preprocessing procedure is conducted, which involves image enhancement, removal of background noise, and object identification through a variety of approaches. Using UNet, PSPNet, and UNet++, disease areas are identified. UNet + + delivering the most precise object segmentation. The segmented patch data are then fed into a fusion, based classification model as input. Patch segmentation makes it possible to separate the various disease areas, thus increasing the classification accuracy. The proposed framework combines several CNN architectures, namely, VGG16, InceptionV3, AlexNet, and GoogleNet, in order to take advantage of their complementary strengths, then a Vision Transformer (ViT) is used to identify distinct local features via early feature fusion. After this, the efficiency of the proposed model is check in comparison with the pre-trained CNN models individually. Comparing with all other models the proposed model has shown the accuracy of highest order i.e. 98.85%, clearly out performing individual CNN models.","目前，农业中的生物和非生物因素估计约占作物生产总损失的22%。因此，农民尽早识别这些限制因素非常重要，但同时也非常困难。计算机视觉的最新发展使得在初级水平上识别和分类植物病变成为可能。各种深度学习技术使得以极高准确度检测苹果植株叶片的生物胁迫成为可能。因此，患病叶片图像的自动分割已成为世界范围内的前沿研究主题。本文使用来自FGVC7的数据集来衡量所提出模型的性能，该数据集是一个开放源数据集，包含苹果作物的四类不同叶片病害。进行了三步图像预处理，包括图像增强、背景噪声去除以及通过多种方法进行目标识别。使用UNet、PSPNet和UNet++识别病害区域，其中UNet++提供了最精确的目标分割。然后将分割后的图像块数据作为输入馈入基于融合的分类模型。图像块分割使得能够分离不同的病害区域，从而提高分类准确率。所提出的框架结合了多种CNN架构，即VGG16、InceptionV3、AlexNet和GoogleNet，以利用它们互补的优势，然后使用视觉Transformer（ViT）通过早期特征融合来识别不同的局部特征。此后，将所提出模型的效率与各个预训练CNN模型进行比较。与所有其他模型相比，所提出的模型显示出最高阶的准确率，即98.85%，明显优于各个单独的CNN模型。","Discover Computing","2026-10-10T00:00:00Z",69,{"impact":249,"substance":293,"depth":223,"authority":224,"freshness":60,"relevant":22,"comment":294},20,"方法组合（UNet++分割+CNN与ViT融合）与98.85%准确率有参考价值，但属实验室数据集验证，产业落地影响有限，可作为技术前沿资讯收录。",[296],{"name":289,"url":286},[27,28,298,63,299],"计算机视觉","苹果病害识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[302],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":304},{"candidates":305,"agricultural_evidence":53},[306,311,315],{"code":40,"score":293,"supported":53,"title":307,"abstract":308,"tags":310},[64],[63,298,64,309],"computer vision",[28,63,298],{"code":33,"score":20,"supported":53,"title":312,"abstract":313,"tags":314},[55],[57,55],[],{"code":67,"score":22,"supported":14,"title":316,"abstract":317,"tags":318},[],[],[27],[320,321],"苹果叶片病害 深度学习","FGVC7 苹果病害 数据集","苹果叶片病害深度学习-4744","10.1007\u002Fs10791-026-10573-4",{"doi":323,"openalex_id":325,"authors":326,"venue":289,"cited_by_count":91,"oa_url":342,"card":343,"direction":212,"ingested_from":98},"W7221353656",[327,329,332,334,336,339],{"name":328,"orcid":9},"Apoorva Arora",{"name":330,"orcid":331},"Vinay Gautam","https:\u002F\u002Forcid.org\u002F0000-0002-0258-5132",{"name":333,"orcid":9},"Anand Muni Mishra",{"name":335,"orcid":9},"Santar Pal Singh",{"name":337,"orcid":338},"Yadvendra Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-6396-3355",{"name":340,"orcid":341},"Mukund Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-5877-9510","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10791-026-10573-4.pdf",{"tldr":344,"method":345,"finding":346,"direction":210,"opportunity":347},"用UNet++分割苹果病叶并融合多种CNN与ViT分类，实现98.85%识别准确率。","FGVC7数据集，UNet\u002FPSPNet\u002FUNet++分割，VGG16等CNN与","UNet++分割最准，融合模型准确率达98.85%，优于单个CNN模型。","可探索轻量化分割-分类联合模型在田间移动端的实时部署与多作物泛化。","2026-10-12T23:30:09.091475Z",{"id":350,"title":351,"url":352,"summary":353,"summary_zh":354,"content":9,"source_name":355,"source_url":352,"published_at":356,"category":12,"cover_url":9,"hotness":357,"is_selected":14,"score":358,"score_detail":359,"sources":361,"tags":365,"primary_topic":367,"secondary_topics":368,"classification":370,"search_phrases":385,"slug":388,"view_count":22,"doi":389,"paper":390,"created_at":403},4241,"Detection of Apple Plant Diseases Using Leaf Images through Convolutional Neural Network","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142591","Plant diseases have major social, ecological and economic impact on agriculture and frequently results in large financial losses for farmers. Therefore early and precise disease detection is crucial for efficient crop management. The severity of leaf diseases and their direct impact on yield quality have been highlighted in recent studies. Both digital cameras and flatbed scanners can be used to take leaf images but they have certain drawbacks. Although scanners offer a more secure and regulated method of acquiring images they are comparatively slow and may cause edge shadows. This study uses a hybrid deep learning approach to identify diseases in apple plant leaves. To improve classification performance; the proposed approach combines the advantages of Residual Networks (ResNet), Genetic Algorithms (GA) and Convolutional Neural Networks (CNN). CNNs are used to extract features and classify diseases whereas ResNet enhances the learning efficiency of deeper networks. The feature set is optimized using a genetic algorithm which improves accuracy and lowers computational complexity. The model also calculates the percentage of the leaf area that is impacted. According to experimental results the proposed method detects leaf diseases with high accuracy of percentage and low computational cost making it a dependable and effective precision agriculture solution. The proposed model exhibited strong classification efficiency across various leaf conditions that pertaining an accuracy of 0.97 for healthy leaves, 0.994 for apple scab, 0.89 for rust, and 0.91 for powdery mildew.","植物病害对农业产生重大的社会、生态和经济影响，并经常给农民带来巨大的经济损失。因此，早期且精确的病害检测对于高效的作物管理至关重要。近期研究强调了叶部病害的严重程度及其对产量品质的直接影响。数码相机和平板扫描仪均可用于拍摄叶片图像，但它们各有一定缺陷。尽管扫描仪提供了更安全、更可控的图像获取方式，但其速度相对较慢，且可能产生边缘阴影。本研究采用混合深度学习方法识别苹果植株叶部病害。为提高分类性能，所提方法结合了残差网络（ResNet）、遗传算法（GA）和卷积神经网络（CNN）的优势。CNN用于提取特征并分类病害，而ResNet增强了更深网络的学习效率。利用遗传算法对特征集进行优化，从而提高准确率并降低计算复杂度。该模型还计算了受影响的叶面积百分比。实验结果表明，所提方法以高准确率和低计算成本检测叶部病害，使其成为可靠且有效的精准农业解决方案。所提模型在多种叶片条件下均表现出较强的分类效率，健康叶片准确率为0.97，苹果黑星病为0.994，锈病为0.89，白粉病为0.91。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-04T00:00:00Z",25,67,{"impact":249,"substance":19,"depth":223,"authority":249,"freshness":21,"relevant":22,"comment":360},"基于ResNet+GA+CNN的苹果叶病识别研究，准确率数据具体，方法有增量，但属实验室论文，产业影响有限。",[362,363],{"name":355,"url":352},{"name":355,"url":364},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142592",[27,28,63,155,366],"苹果病害",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[369],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":371},{"candidates":372,"agricultural_evidence":53},[373,377,381],{"code":33,"score":17,"supported":53,"title":374,"abstract":375,"tags":376},[55],[57,55],[366],{"code":67,"score":60,"supported":53,"title":378,"abstract":379,"tags":380},[],[155,156],[27,155],{"code":40,"score":60,"supported":53,"title":382,"abstract":383,"tags":384},[],[63,64],[28,63],[386,387],"苹果叶片病害 卷积神经网络","苹果锈病 白粉病 识别","苹果叶片病害卷积神经网络-4241","10.5281\u002Fzenodo.23142591",{"doi":389,"openalex_id":391,"authors":392,"venue":355,"cited_by_count":91,"oa_url":352,"card":397,"direction":402,"ingested_from":98},"W7219637192",[393,395],{"name":394,"orcid":9},"Mohd Arshad Raza",{"name":396,"orcid":9},"Kiran Pandey",{"tldr":398,"method":399,"finding":400,"direction":210,"opportunity":401},"提出ResNet+GA+CNN混合模型，用苹果叶片图像检测病害并计算病斑面积占比。","ResNet提取深层特征，遗传算法优化特征集，CNN分类，苹果叶片图像数据。","健康叶、黑星病、锈病、白粉病准确率分别达0.97、0.994、0.89、0.91，计算成本低。","可探索轻量化模型在田间移动端实时检测，并融合多病害严重度分级与产量损失预测。","数字乡村与农业信息化","2026-10-05T23:30:30.830366Z",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":409,"content":9,"source_name":410,"source_url":407,"published_at":411,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":412,"score_detail":413,"sources":415,"tags":417,"primary_topic":419,"secondary_topics":420,"classification":422,"search_phrases":437,"slug":440,"view_count":441,"doi":442,"paper":443,"created_at":461},2910,"Precision Diagnosis of Apple Leaf Diseases Across Infection Stages via Web-Based Analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10451-5","Abstract Purpose Throughout the growing season, leaf diseases pose a huge risk to apple fruit yield and quality. While early detection targets timely intervention, understanding disease progression across different stages is essential for effective treatment and resistance breeding. Methods This study proposes a deep learning-based detection system that identifies apple leaf diseases at various infection stages in real-world environments. Most existing methods have focused on disease detection in controlled conditions, while the developed system operates in complex field settings with variable lighting and background noise. Two YOLOv8-based variants integrated with a multi-head self-attention (MHSA) module were developed to improve detection of dense, tiny, and feature-similar lesions. In addition, a web-based interactive tool was built by combining the Segment Anything Model (SAM) with the detection model to support leaf isolation, disease localization, infection estimation, severity categorization, and automatic report generation. Results The training results on proposed PA-ALeaf dataset demonstrated that YOLOv8-MHSA_b_h varaint achieved the highest performance (Precision 60.5%, mAP50 56.3%, F1 confidence 56.0%), while maintaining real-time inference (12.38 ms per image), compared to state-of-the-art models. Conclusion By bridging practical disease monitoring and scientific research, our system offers a comprehensive, scalable solution for apple leaf disease detection in real-world orchards. This system could benefit both farmers by enabling multi-stage intervention and researchers by providing insights into disease progression.","摘要 目的 在整个生长季中，叶片病害对苹果果实产量和品质构成巨大风险。虽然早期检测有助于及时干预，但了解病害在不同阶段的进展对于有效治疗和抗性育种至关重要。方法 本研究提出了一种基于深度学习的检测系统，可在真实环境中识别不同感染阶段的苹果叶片病害。现有大多数方法侧重于受控条件下的病害检测，而所开发的系统可在光照变化和背景噪声复杂的田间环境中运行。研究开发了两种基于YOLOv8的变体，并集成了多头自注意力（MHSA）模块，以提高对密集、微小和特征相似病斑的检测能力。此外，通过将分割一切模型（SAM）与检测模型相结合，构建了一个基于网络的交互式工具，以支持叶片分离、病害定位、感染估计、严重程度分类和自动报告生成。结果 在所提出的PA-ALeaf数据集上的训练结果表明，与最先进的模型相比，YOLOv8-MHSA_b_h变体取得了最高性能（精确率60.5%，mAP50 56.3%，F1置信度56.0%），同时保持实时推理（每张图像12.38 ms）。结论 通过连接实际病害监测与科学研究，我们的系统为真实果园中的苹果叶片病害检测提供了一种全面、可扩展的解决方案。该系统既可使农民受益，实现多阶段干预，也可为研究人员提供病害进展的见解。","Precision Agriculture","2026-09-18T00:00:00Z",80,{"impact":19,"substance":113,"depth":19,"authority":20,"freshness":60,"relevant":22,"comment":414},"提出面向真实果园的多阶段苹果叶病检测系统并配套网页工具，方法新颖、数据与性能指标明确，对智慧植保具有参考价值。",[416],{"name":410,"url":407},[27,28,63,418,366],"病害识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[421],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":423},{"candidates":424,"agricultural_evidence":53},[425,429,433],{"code":33,"score":17,"supported":53,"title":426,"abstract":427,"tags":428},[55],[57,55],[366],{"code":40,"score":21,"supported":53,"title":430,"abstract":431,"tags":432},[],[63,64],[28,63,418],{"code":67,"score":22,"supported":14,"title":434,"abstract":435,"tags":436},[],[],[27],[438,439],"苹果叶部病害 深度学习 识别","YOLOv8 苹果病害 检测","苹果叶部病害深度学习识别-2910",5,"10.1007\u002Fs11119-026-10451-5",{"doi":442,"openalex_id":444,"authors":445,"venue":410,"cited_by_count":91,"oa_url":407,"card":456,"direction":210,"ingested_from":98},"W7213537759",[446,448,451,453],{"name":447,"orcid":9},"Kangrui Han",{"name":449,"orcid":450},"Hao Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9879-2960",{"name":452,"orcid":9},"Kari Peter",{"name":454,"orcid":455},"Long He","https:\u002F\u002Forcid.org\u002F0000-0001-9781-6062",{"tldr":457,"method":458,"finding":459,"direction":210,"opportunity":460},"提出基于YOLOv8-MHSA与SAM的网页系统，实现苹果叶片病害多感染阶段实时检测与分级。","YOLOv8+多头自注意力，结合SAM分割，构建PA-ALeaf数据集与网页工具","YOLOv8-MHSA_b_h变体精度60.5%、mAP50 56.3%，单图推理12.38ms，可","可探索轻量化模型在移动端的部署，并融合时序数据预测病害发展轨迹。","2026-09-19T23:30:03.474350Z",{"id":463,"title":464,"url":465,"summary":466,"summary_zh":467,"content":9,"source_name":468,"source_url":465,"published_at":469,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":470,"sources":472,"tags":474,"primary_topic":477,"secondary_topics":478,"classification":480,"search_phrases":501,"slug":504,"view_count":22,"doi":505,"paper":506,"created_at":542},2656,"DeepPhenoTree-Apple Edition: a multi-site apple phenology RGB annotated dataset with deep learning baseline models","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13007-026-01591-w","Abstract In machine learning-driven plant phenotyping, well-annotated image datasets are essential for developing robust models capable of capturing phenological variability across environments. Here, we introduce DeepPhenoTree-Apple Edition , a multi-site and multi-variety RGB image dataset dedicated to the detection of key phenological stages in apple trees. The dataset includes 48,320 time-stamped RGB images acquired across four European orchards of the Apple REFPOP consortium under biological, agronomic, and environmental variability, including differences in genotypes, orchard architectures, phenological development, temperature, and humidity conditions. From this large corpus, a carefully curated subset of 808 representative images was manually annotated. It includes 241,600 expert annotations covering developmental stages from dormant bud to fruit maturity. Images were acquired using a standardized tractor-mounted phenotyping platform equipped with active flash illumination. Active flash illumination was used to reduce illumination variability and homogenize exposure, shadows, and sunlight differences across sites. Phenological structures were annotated following the BBCH scale, with bounding boxes adapted to organ visibility and developmental stage. In addition to the dataset, we provide deep-learning baseline experiments to illustrate detection performance and detection performance across locations.","在机器学习驱动的植物表型分析中，标注良好的图像数据集对于开发能够捕捉不同环境下物候变异的稳健模型至关重要。在此，我们介绍DeepPhenoTree-Apple Edition，一个用于检测苹果树关键物候阶段的多地点、多品种RGB图像数据集。该数据集包含48,320张带有时间戳的RGB图像，采集自Apple REFPOP联盟的四个欧洲果园，涵盖了生物学、农艺学和环境变异，包括基因型、果园结构、物候发育、温度和湿度条件的差异。从这一大型语料库中，我们精心挑选了808张具有代表性的图像子集进行人工标注。该子集包含241,600条专家标注，覆盖从休眠芽到果实成熟的发育阶段。图像使用配备主动闪光照明（active flash illumination）的标准化拖拉机搭载表型分析平台采集。主动闪光照明用于减少光照变异性，并统一不同地点的曝光、阴影和阳光差异。物候结构按照BBCH scale（BBCH尺度）进行标注，边界框根据器官可见性和发育阶段进行调整。除数据集外，我们还提供了深度学习基线实验，以展示检测性能以及不同地点的检测性能。","Plant Methods","2026-09-15T00:00:00Z",{"impact":19,"substance":113,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":471},"多站点苹果物候RGB标注数据集，规模大、标注专业并附深度学习基线，对作物表型与智慧果园研究有实质参考价值。",[473],{"name":468,"url":465},[27,28,57,475,476],"表型组学","图像数据集",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[479],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":481},{"candidates":482,"agricultural_evidence":53},[483,487,491,497],{"code":33,"score":19,"supported":53,"title":484,"abstract":485,"tags":486},[55],[35,57,143,55],[57],{"code":40,"score":19,"supported":53,"title":488,"abstract":489,"tags":490},[64],[63,136,137],[28],{"code":492,"score":174,"supported":53,"title":493,"abstract":494,"tags":496},"F5",[],[495],"tractor",[],{"code":67,"score":22,"supported":14,"title":498,"abstract":499,"tags":500},[],[],[27],[502,503],"农业人工智能 图像数据集 智慧农业 表型组学","农业人工智能 图像数据集","农业人工智能图像数据集智慧农业表型组学-2656","10.1186\u002Fs13007-026-01591-w",{"doi":505,"openalex_id":507,"authors":508,"venue":468,"cited_by_count":91,"oa_url":465,"card":537,"direction":96,"ingested_from":98},"W7213246841",[509,512,514,516,519,522,525,527,529,531,533,535],{"name":510,"orcid":511},"Herearii Metuarea","https:\u002F\u002Forcid.org\u002F0009-0008-2716-0617",{"name":513,"orcid":9},"Abdoul-Djalil Ousseini-Hamza",{"name":515,"orcid":9},"Walter Guerra",{"name":517,"orcid":518},"Francesca Zuffa","https:\u002F\u002Forcid.org\u002F0009-0009-8797-0205",{"name":520,"orcid":521},"Francesco Panzeri","https:\u002F\u002Forcid.org\u002F0009-0005-8774-5201",{"name":523,"orcid":524},"Andrea Patocchi","https:\u002F\u002Forcid.org\u002F0000-0002-0919-2702",{"name":526,"orcid":9},"Lidia Lozano",{"name":528,"orcid":9},"Shauny Van Hoye",{"name":530,"orcid":9},"François Laurens",{"name":532,"orcid":9},"Jeremy Labrosse",{"name":534,"orcid":9},"Pejman Rasti",{"name":536,"orcid":9},"David Rousseau",{"tldr":538,"method":539,"finding":540,"direction":96,"opportunity":541},"发布多站点多品种苹果物候RGB图像数据集，并给出深度学习基线检测模型。","采集4个欧洲果园48320张图像，精选808张按BBCH标框标注，用主动闪光和拖","数据集覆盖从休眠芽到果实成熟的241600个标注，基线模型可跨地点检测物候期。","可基于该多站点数据研究跨环境域适应与物候期细粒度检测，提升模型泛化能力。","2026-09-16T23:30:24.293828Z"]