[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4808":3,"related-4808":116},{"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":50,"search_phrases":86,"slug":89,"view_count":22,"doi":90,"paper":91,"created_at":115},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。",null,"Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","无人机","农业人工智能","遥感监测","病害预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[39,45],{"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",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,65,72,78],{"code":33,"score":57,"supported":58,"title":59,"abstract":61,"tags":64},17,true,[60],"apple",[62,63,60],"苹果","葡萄",[],{"code":40,"score":66,"supported":58,"title":67,"abstract":69,"tags":71},15,[68],"deep learning",[70,68],"深度学习",[29],{"code":46,"score":73,"supported":58,"title":74,"abstract":76,"tags":77},12,[75],"multispectral",[75],[30],{"code":79,"score":80,"supported":58,"title":81,"abstract":82,"tags":85},"I1",7,[],[83,84],"精准农业","precision agriculture",[27],[87,88],"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":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":105,"oa_url":106,"card":107,"direction":113,"ingested_from":114},"W7221558726",[94,97,100,102],{"name":95,"orcid":96},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":98,"orcid":99},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":101,"orcid":9},"Fatih Sinan Bir",{"name":103,"orcid":104},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428",0,"https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"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。","农业遥感与作物表型","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。","智慧农业 \u002F 农业物联网","openalex","2026-10-13T23:30:10.242467Z",{"total":117,"page":22,"page_size":117,"items":118},6,[119,223,299,371,436,507],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":132,"tags":134,"primary_topic":138,"secondary_topics":139,"classification":142,"search_phrases":195,"slug":198,"view_count":22,"doi":199,"paper":200,"created_at":222},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":129,"depth":19,"authority":130,"freshness":21,"relevant":22,"comment":131},22,14,"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。",[133],{"name":125,"url":122},[27,29,135,136,137],"产量预测","多模态融合","遥感",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[140,141],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":143},{"candidates":144,"agricultural_evidence":58},[145,152,158,164,168,175,182,189],{"code":40,"score":146,"supported":58,"title":147,"abstract":148,"tags":151},11,[],[70,149,150,68],"机器学习","machine learning",[29,136],{"code":33,"score":21,"supported":58,"title":153,"abstract":154,"tags":157},[],[62,155,156,60],"芒果","orchard",[],{"code":46,"score":159,"supported":58,"title":160,"abstract":161,"tags":163},8,[],[162,75],"无人机多光谱",[135,137],{"code":79,"score":80,"supported":58,"title":165,"abstract":166,"tags":167},[],[83,84],[27],{"code":169,"score":117,"supported":58,"title":170,"abstract":171,"tags":174},"H2",[],[172,173],"土壤","soil",[],{"code":176,"score":117,"supported":58,"title":177,"abstract":178,"tags":181},"I5",[],[179,180],"物联网","IoT",[],{"code":183,"score":184,"supported":58,"title":185,"abstract":186,"tags":188},"H6",3,[],[187],"农业生态",[],{"code":190,"score":184,"supported":58,"title":191,"abstract":192,"tags":194},"H7",[],[193],"气候适应",[],[196,197],"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":199,"openalex_id":201,"authors":202,"venue":125,"cited_by_count":105,"oa_url":122,"card":216,"direction":113,"ingested_from":114},"W7221795850",[203,206,208,210,212,214],{"name":204,"orcid":205},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":207,"orcid":9},"Umarfarooque Momin",{"name":209,"orcid":9},"Sudeep Kumar E",{"name":211,"orcid":9},"Itfaq Ahmad Mir",{"name":213,"orcid":9},"Ab Rouf Khan",{"name":215,"orcid":9},"Ghulam Mohammad Mir",{"tldr":217,"method":218,"finding":219,"direction":220,"opportunity":221},"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。","2026-10-15T23:30:08.750140Z",{"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":234,"tags":236,"primary_topic":238,"secondary_topics":239,"classification":242,"search_phrases":269,"slug":272,"view_count":184,"doi":273,"paper":274,"created_at":298},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports","2026-09-22T00:00:00Z",78,{"impact":130,"substance":129,"depth":19,"authority":130,"freshness":13,"relevant":22,"comment":233},"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[235],{"name":229,"url":226},[27,149,155,30,237],"作物估产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[240,241],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":243},{"candidates":244,"agricultural_evidence":58},[245,249,253,258,263],{"code":33,"score":66,"supported":58,"title":246,"abstract":247,"tags":248},[156],[155,156],[155],{"code":40,"score":66,"supported":58,"title":250,"abstract":251,"tags":252},[150],[149,150],[149],{"code":46,"score":159,"supported":58,"title":254,"abstract":255,"tags":257},[],[137,256],"remote sensing",[237,30],{"code":79,"score":259,"supported":58,"title":260,"abstract":261,"tags":262},4,[],[83],[27],{"code":264,"score":184,"supported":58,"title":265,"abstract":266,"tags":268},"J1",[],[267],"农业规划",[],[270,271],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":273,"openalex_id":275,"authors":276,"venue":229,"cited_by_count":105,"oa_url":226,"card":293,"direction":111,"ingested_from":114},"W7213920056",[277,279,282,284,286,289,291],{"name":278,"orcid":9},"V. Raju",{"name":280,"orcid":281},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":283,"orcid":9},"Dr. V. S. Thorat",{"name":285,"orcid":9},"V. T. Shinde",{"name":287,"orcid":288},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":290,"orcid":9},"Alok Shrivastava",{"name":292,"orcid":9},"A. P. Chaudhary",{"tldr":294,"method":295,"finding":296,"direction":111,"opportunity":297},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","2026-09-22T23:30:22.619289Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":9,"source_name":305,"source_url":302,"published_at":306,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":307,"score_detail":308,"sources":310,"tags":312,"primary_topic":314,"secondary_topics":315,"classification":317,"search_phrases":344,"slug":347,"view_count":105,"doi":348,"paper":349,"created_at":370},4505,"Application of Deep Learning Techniques for Vineyard Mapping and Characterization Using Multispectral UAV Imagery: A Case Study from Northern Greece","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23232412","Developments in unmanned aerial vehicle (UAV) technology and the rise of Deep Learning (DL) have drastically boosted the capacities of precision viticulture to monitor the environments of farms in a scalable and detailed manner. Vineyard mappings and extraction of vine-specific features are crucial for yield estimation, plant health assessment, and management decisions. This study examines the application of Deep Learning-based image analysis techniques for vineyard mapping and characterization using multispectral UAV image data (Deep Learning). The proposed approach integrates spatial and spectral information to delineate vineyard structures and capture key canopy characteristics relevant to precision agriculture. Multimodal datasets, including RGB and multispectral bands, facilitate improved discrimination between vines and surrounding elements. Geospatial analysis and data processing are further performed through ArcGIS Pro, enabling spatial validation of results, identifying extracted features, and creating appropriate interpretative thematic layers. The combination of UAV imagery, DL tools and GIS based analysis offers a strong and flexible workflow that can be adjusted for different grape cultivars, growth conditions and circumstances. Overall, the study demonstrates the potential benefits of the rigid union of UAV-based remote sensing with Deep Learning and GIS techniques to create a flexible, operational, and data-driven framework for vineyard management. Based on this, sustainable and fruitful management practices can be elaborated, thus optimizing resources and vineyard management decisions in precision viticulture,underlining the importance of such a technological integration for monitoring and managing modern vineyards.","无人机（UAV）技术的发展与深度学习（DL）的兴起，极大地提升了精准葡萄栽培以可扩展且精细的方式监测农场环境的能力。葡萄园制图与葡萄植株特异性特征的提取对于产量估算、植株健康评估和管理决策至关重要。本研究探讨了基于深度学习的图像分析技术在多光谱无人机影像数据（深度学习）用于葡萄园制图与表征中的应用。所提出的方法整合了空间与光谱信息，以 delineate 葡萄园结构并捕捉与精准农业相关的关键冠层特征。多模态数据集（包括RGB和多光谱波段）有助于更好地区分葡萄藤与周围元素。通过ArcGIS Pro进一步进行地理空间分析与数据处理，从而实现结果的空间验证、提取特征的识别以及适当的解释性专题图层的创建。无人机影像、深度学习工具与基于GIS的分析相结合，提供了一套强大且灵活的工作流程，可针对不同葡萄品种、生长条件和环境进行调整。总体而言，本研究展示了将基于无人机的遥感与深度学习和GIS技术 rigidly 结合，以创建灵活、可操作且数据驱动的葡萄园管理框架的潜在优势。在此基础上，可制定可持续且富有成效的管理实践，从而优化精准葡萄栽培中的资源与葡萄园管理决策，凸显了这种技术整合对现代葡萄园监测与管理的重要性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",68,{"impact":73,"substance":19,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":309},"希腊北部葡萄园的多光谱无人机深度学习制图案例，方法新颖但属区域性研究，对国内智慧农业有参考价值。",[311],{"name":305,"url":302},[27,28,29,137,313],"葡萄园管理",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},[316],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":318},{"candidates":319,"agricultural_evidence":58},[320,324,328,332,337],{"code":46,"score":19,"supported":58,"title":321,"abstract":322,"tags":323},[75],[137,256,75],[137],{"code":40,"score":66,"supported":58,"title":325,"abstract":326,"tags":327},[68],[70,68],[29],{"code":79,"score":80,"supported":58,"title":329,"abstract":330,"tags":331},[],[83,84],[27],{"code":33,"score":117,"supported":58,"title":333,"abstract":334,"tags":336},[],[63,335],"grape",[],{"code":338,"score":117,"supported":58,"title":339,"abstract":340,"tags":343},"I4",[],[341,342],"空间分析","GIS",[],[345,346],"希腊 葡萄园 无人机 深度学习","多光谱 UAV 葡萄园 制图","希腊葡萄园无人机深度学习-4505","10.5281\u002Fzenodo.23232412",{"doi":348,"openalex_id":350,"authors":351,"venue":305,"cited_by_count":105,"oa_url":302,"card":365,"direction":111,"ingested_from":114},"W7220993218",[352,354,357,359,362],{"name":353,"orcid":9},"Dimitra Passa",{"name":355,"orcid":356},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":358,"orcid":9},"Georgios Nektarios Tselos",{"name":360,"orcid":361},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":363,"orcid":364},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":366,"method":367,"finding":368,"direction":111,"opportunity":369},"用多光谱无人机影像和深度学习实现葡萄园制图与冠层特征提取。","多光谱无人机影像、RGB与多光谱融合、深度学习、ArcGIS Pro空间分析。","UAV+DL+GIS可灵活构建葡萄园精准管理的数据驱动框架。","可探索跨品种、跨生长阶段的迁移学习与轻量化模型，提升葡萄园实时监测泛化能力。","2026-10-09T23:30:25.912664Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":376,"content":9,"source_name":377,"source_url":374,"published_at":378,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":379,"score_detail":380,"sources":382,"tags":384,"primary_topic":387,"secondary_topics":388,"classification":390,"search_phrases":411,"slug":414,"view_count":184,"doi":415,"paper":416,"created_at":435},3550,"Asymmetric Vegetation Responses to Flood Exposure in the Chi River Basin: A Multi-Temporal Remote Sensing and Machine Learning Investigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18101606","Flooding and drought alternate in many tropical floodplains, but how vegetation responds to both hazards over multiple years remains poorly quantified. We examined vegetation–flood associations in the Chi River Basin, Northeast Thailand, drawing on Sentinel-2 imagery from 2020, 2023, and 2024, flood records from 2017, 2018, 2021, and 2022, and machine learning methods across 230,911 point-based spatial units. Nine forms of asymmetry emerged from the analysis. The most striking was recovery asymmetry: areas that flooded at least twice between 2017 and 2022 gained vegetation during 2020–2023 (mean NDVI change = +0.0270), whereas areas with little or no flood exposure lost vegetation (mean change = −0.0430). This gap was statistically significant (Cohen’s d = 0.5586, p \u003C 0.001) and suggests that repeated flooding may buffer vegetation against subsequent drought. Agricultural land declined less (−0.0316) than forest (−0.0937; ANOVA F = 1717.7, p \u003C 0.001). Baseline vegetation condition, measured as NDVI in 2020, contributed 32.9% to model importance, more than twice the contribution of elevation (14.5%). Spectral indices together accounted for 76.6% of importance, compared with 23.5% for topographic variables. The 2023 El Niño year produced the largest difference between high-flood and low-flood areas (+0.0364); because only one year per ENSO phase was available, we treat this as a case-based comparison rather than a general ENSO response. Threshold analysis identified two distinct values: an operational cut-off at NDVI = 0.05 (overall accuracy 82.67%) and an ecological transition around 0.25–0.30. Spatial clustering was weak but significant (Moran’s I = 0.2179, p \u003C 0.001). Spatial block cross-validation gave lower accuracy (0.597) than random cross-validation (0.627), pointing to spatial autocorrelation in the data. High-flood areas had 1.67 times the vulnerability index of low-flood areas (0.4306 vs. 0.2573). These patterns support differentiated management: elevation-based zoning, warning systems calibrated to local flood regimes, and focused interventions at hotspots.","在许多热带洪泛平原，洪水与干旱交替发生，但植被如何在多年间同时响应这两种灾害，仍缺乏充分的定量研究。我们以泰国东北部栖河（Chi River）流域为研究区，考察植被与洪水的关联，数据来源包括2020年、2023年和2024年的Sentinel-2影像，2017年、2018年、2021年和2022年的洪水记录，并采用机器学习方法，覆盖230 911个基于点的空间单元。分析中出现了九种不对称形式。其中最显著的是恢复不对称：2017—2022年间至少发生两次洪水的区域，在2020—2023年间植被增加（NDVI平均变化=+0.0270），而洪水暴露很少或没有洪水暴露的区域则植被减少（平均变化=−0.0430）。这一差异具有统计显著性（Cohen’s d=0.5586，p\u003C0.001），表明反复洪水可能缓冲植被对随后干旱的响应。农田的下降幅度（−0.0316）小于森林（−0.0937；ANOVA F=1717.7，p\u003C0.001）。以2020年NDVI衡量的基线植被状况对模型重要性的贡献为32.9%，是海拔贡献（14.5%）的两倍多。光谱指数合计占重要性的76.6%，而地形变量占23.5%。2023年厄尔尼诺年（El Niño year）高洪水区与低洪水区之间的差异最大（+0.0364）；由于每个ENSO相位仅有1年数据，我们将其视为基于个案的比较，而非一般性的ENSO响应。阈值分析识别出两个不同的值：操作性截断值为NDVI=0.05（总体精度82.67%），生态过渡值约为0.25~0.30。空间聚类较弱但显著（Moran’s I=0.2179，p\u003C0.001）。空间分块交叉验证的精度（0.597）低于随机交叉验证（0.627），表明数据中存在空间自相关。高洪水区的脆弱性指数是低洪水区的1.67倍（0.4306对0.2573）。这些格局支持差异化治理：基于海拔的分区、根据当地洪水情势校准的预警系统，以及在热点区域的重点干预。","Symmetry","2026-09-25T00:00:00Z",70,{"impact":159,"substance":129,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":381},"基于Sentinel-2与机器学习的多时相洪涝-植被响应研究，数据规模大、结论具体，对农业遥感与灾害风险管理有参考价值，但属区域案例研究，公共影响有限。",[383],{"name":377,"url":374},[27,29,30,385,386],"洪涝灾害","植被恢复",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[389],{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":391},{"candidates":392,"agricultural_evidence":58},[393,397,401,407],{"code":40,"score":66,"supported":58,"title":394,"abstract":395,"tags":396},[150],[149,150],[29],{"code":46,"score":13,"supported":58,"title":398,"abstract":399,"tags":400},[256],[],[386,30],{"code":190,"score":80,"supported":58,"title":402,"abstract":403,"tags":406},[],[404,405],"干旱","drought",[385],{"code":79,"score":22,"supported":14,"title":408,"abstract":409,"tags":410},[],[],[27],[412,413],"湄公河支流 流域 遥感 洪水","Sentinel-2 NDVI 洪涝 植被","湄公河支流流域遥感洪水-3550","10.3390\u002Fsym18101606",{"doi":415,"openalex_id":417,"authors":418,"venue":377,"cited_by_count":105,"oa_url":374,"card":430,"direction":111,"ingested_from":114},"W7214283372",[419,421,424,427],{"name":420,"orcid":9},"Jiradech Majandang",{"name":422,"orcid":423},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":425,"orcid":426},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":428,"orcid":429},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":431,"method":432,"finding":433,"direction":111,"opportunity":434},"利用多时相遥感和机器学习揭示泰国湄公河支流流域植被对洪水的非对称响应。","Sentinel-2影像、洪水记录与机器学习，分析23万个空间单元。","重复洪水区植被增加，低洪水区减少，表明洪水可缓冲干旱影响。","可探索洪水-干旱交替下植被恢复机制，并开发基于阈值的差异化预警系统。","2026-09-26T23:30:29.514361Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":442,"source_url":439,"published_at":443,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":444,"sources":446,"tags":448,"primary_topic":451,"secondary_topics":452,"classification":454,"search_phrases":485,"slug":488,"view_count":259,"doi":489,"paper":490,"created_at":506},2906,"UAV remote sensing for crop lodging monitoring: A comprehensive review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112446","Global agriculture is facing increasing pressure to improve productivity while minimizing losses caused by extreme climate events. Crop lodging poses a major threat to food security by reducing crop yield, deteriorating grain quality, and increasing harvesting costs. As a flexible, high-resolution remote sensing platform, unmanned aerial vehicles (UAVs) have emerged as an effective solution for eliminating the gap between labor-intensive ground surveys and low-resolution satellite observations. This review systematically synthesizes the current state of research on UAV-based crop lodging monitoring through a comprehensive analysis of peer-reviewed literature. It covers the complete technical workflow, from lodging mechanisms to lodging quantification. The capabilities of diverse sensing modalities, including RGB, multispectral imagery (MSI), hyperspectral imagery (HSI), thermal infrared (TIR), light detection and ranging (LiDAR), and synthetic aperture radar (SAR), are systematically evaluated for detecting lodging-induced variations in color, texture, spectral characteristics, canopy temperature, structural attributes, and radar backscatter. In addition, recent advances in analytical approaches are reviewed, encompassing traditional statistical analyses, machine learning techniques, and state-of-the-art deep learning models. Furthermore, the review summarizes UAV-based lodging assessments across a wide range of crop species and examines methods for quantifying key lodging parameters, including lodging severity, lodging type, and affected area. By identifying current research gaps and emerging opportunities, this review outlines future research directions and provides valuable guidance for advancing accurate, efficient, and real-time crop lodging monitoring in smart agriculture.","全球农业正面临日益增大的压力，既要提高生产力，又要尽量减少极端气候事件造成的损失。作物倒伏通过降低作物产量、恶化籽粒品质并增加收获成本，对粮食安全构成重大威胁。作为一种灵活的高分辨率遥感平台，无人机（UAV）已成为弥合劳动密集型地面调查与低分辨率卫星观测之间差距的有效解决方案。本文通过系统分析同行评议文献，全面综述了基于无人机的作物倒伏监测研究现状，涵盖从倒伏机理到倒伏量化的完整技术流程。系统评估了多种传感模态的能力，包括RGB影像、多光谱影像（MSI）、高光谱影像（HSI）、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR），用于检测倒伏引起的颜色、纹理、光谱特征、冠层温度、结构属性和雷达后向散射变化。此外，综述了分析方法的最新进展，涵盖传统统计分析、机器学习技术和前沿深度学习模型。同时，本文总结了针对多种作物类型的基于无人机的倒伏评估研究，并探讨了关键倒伏参数的量化方法，包括倒伏严重程度、倒伏类型和受影响面积。通过识别当前研究空白和新兴机遇，本文展望了未来研究方向，为推进智慧农业中准确、高效、实时的作物倒伏监测提供了有价值的指导。","Computers and Electronics in Agriculture","2026-09-18T00:00:00Z",{"impact":19,"substance":129,"depth":19,"authority":66,"freshness":159,"relevant":22,"comment":445},"核心期刊发表的无人机遥感作物倒伏监测综述，系统梳理多模态传感与深度学习分析路径，对智慧农业精准监测有实质参考价值。",[447],{"name":442,"url":439},[27,28,29,449,450],"农业遥感","作物倒伏",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},[453],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":455},{"candidates":456,"agricultural_evidence":58},[457,462,466,471,478],{"code":46,"score":19,"supported":58,"title":458,"abstract":459,"tags":461},[256],[137,256,75,460],"hyperspectral",[449],{"code":40,"score":13,"supported":58,"title":463,"abstract":464,"tags":465},[],[70,149,150,68],[29],{"code":79,"score":80,"supported":58,"title":467,"abstract":468,"tags":470},[],[27,469],"smart agriculture",[27],{"code":472,"score":117,"supported":58,"title":473,"abstract":474,"tags":477},"A8",[],[475,476],"粮食安全","food security",[],{"code":479,"score":117,"supported":58,"title":480,"abstract":481,"tags":484},"J3",[],[482,483],"全球农业","global agriculture",[],[486,487],"无人机 遥感 作物倒伏","UAV 倒伏 监测","无人机遥感作物倒伏-2906","10.1016\u002Fj.compag.2026.112446",{"doi":489,"openalex_id":491,"authors":492,"venue":442,"cited_by_count":105,"oa_url":439,"card":501,"direction":113,"ingested_from":114},"W7213542473",[493,496,498],{"name":494,"orcid":495},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":497,"orcid":9},"Changxing Geng",{"name":499,"orcid":500},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":502,"method":503,"finding":504,"direction":111,"opportunity":505},"综述无人机遥感在作物倒伏监测中的技术流程、传感器与分析模型。","系统综述RGB、多光谱、高光谱、热红外、LiDAR、SAR及机器学习深度学习方法","多模态遥感结合深度学习可实现倒伏程度、类型与面积的精准量化。","多源数据融合与实时轻量化模型是倒伏监测的潜在突破方向。","2026-09-19T23:30:01.740139Z",{"id":508,"title":509,"url":510,"summary":511,"summary_zh":512,"content":9,"source_name":513,"source_url":510,"published_at":514,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":515,"score_detail":516,"sources":518,"tags":520,"primary_topic":522,"secondary_topics":528,"classification":531,"search_phrases":553,"slug":556,"view_count":259,"doi":557,"paper":558,"created_at":590},2320,"Genotype-Aware Prediction of Soybean Seed Composition from Multimodal UAV Imagery of the Standing Crop","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183121","Geospatial artificial intelligence (GeoAI) integrates multimodal remote sensing with deep learning to model complex agricultural systems at scale. Within this framework, accurate and non-destructive prediction of seed composition from in-season standing crops is essential for breeding and precision agriculture. This study developed an end-to-end convolutional neural network (CNN) framework to estimate eight seed traits (protein, oil, sucrose, fiber, starch, ash, complex and simple carbohydrates) from UAV-based multisensor imagery and associated genotype and phenological metadata. A total of 372 soybean samples were collected over two growing seasons (2020–2021) from two fields in Missouri, with UAV flights capturing multispectral (MSI), thermal (THR), and LiDAR (LDR) data at four time points spanning vegetative to reproductive growth stages. CNN models were trained in single- and multi-date configurations, incorporating genotype (GEN) and days after sowing (DAS) as additional features. The highest accuracy was achieved for sucrose (R2 = 0.80), followed by simple carbohydrate (R2 = 0.70) and starch (R2 = 0.55), with notable gains from GEN and DAS. Multi-date models incorporating earlier acquisitions often matched or outperformed later or all-date combinations. Among modalities, MSI provided the most robust estimates, with limited added value from LDR or THR. Unlike feature-based pipelines prone to multicollinearity, this image-to-trait approach enables automated, scalable prediction of soybean seed composition for in-season, field-level assessment.","地理空间人工智能（GeoAI）将多模态遥感与深度学习相结合，以在大尺度上对复杂农业系统进行建模。在该框架下，对当季未收获作物进行准确且无损的种子成分预测，对于育种和精准农业至关重要。本研究开发了一种端到端的卷积神经网络（CNN）框架，利用基于无人机（UAV）的多传感器影像以及相关的基因型和物候元数据，估算八种种子性状（蛋白质、油分、蔗糖、纤维、淀粉、灰分、复杂碳水化合物和简单碳水化合物）。研究在2020—2021两个生长季内，从密苏里州两块田地共采集372份大豆样本，无人机飞行在从营养生长期到生殖生长期的四个时间点获取了多光谱（MSI）、热红外（THR）和激光雷达（LDR）数据。CNN模型以单日期和多日期配置进行训练，并将基因型（GEN）和播种后天数（DAS）作为附加特征纳入。蔗糖的估算精度最高（R² = 0.80），其次为简单碳水化合物（R² = 0.70）和淀粉（R² = 0.55），且GEN和DAS的加入带来了显著提升。纳入早期采集数据的多日期模型往往与后期或全日期组合的表现相当，甚至更优。在各模态中，MSI提供了最稳健的估算结果，而LDR或THR的附加价值有限。与易受多重共线性影响的特征工程流程不同，这种从图像到性状的方法能够实现大豆种子成分的自动化、可扩展预测，用于当季田块尺度的评估。","Remote Sensing","2026-09-11T00:00:00Z",80,{"impact":19,"substance":129,"depth":19,"authority":130,"freshness":159,"relevant":22,"comment":517},"基于多模态无人机影像与基因型信息的大豆籽粒成分无损预测研究，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[519],{"name":513,"url":510},[27,28,29,137,521],"大豆育种",{"code":523,"slug":524,"name":525,"path":526,"parent_code":527},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[529,530],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":532},{"candidates":533,"agricultural_evidence":58},[534,540,545,549],{"code":523,"score":66,"supported":58,"title":535,"abstract":537,"tags":539},[536],"soybean",[538,536],"大豆",[521],{"code":40,"score":13,"supported":58,"title":541,"abstract":542,"tags":544},[],[70,68,543],"artificial intelligence",[29],{"code":46,"score":13,"supported":58,"title":546,"abstract":547,"tags":548},[],[137,256,75],[137],{"code":79,"score":80,"supported":58,"title":550,"abstract":551,"tags":552},[],[83,84],[27],[554,555],"农业人工智能 大豆育种 智慧农业 无人机","农业人工智能 大豆育种","农业人工智能大豆育种智慧农业无人机-2320","10.3390\u002Frs18183121",{"doi":557,"openalex_id":559,"authors":560,"venue":513,"cited_by_count":105,"oa_url":510,"card":585,"direction":111,"ingested_from":114},"W7212223520",[561,564,567,570,573,575,577,580,583],{"name":562,"orcid":563},"Vasit Sagan","https:\u002F\u002Forcid.org\u002F0000-0003-4375-2096",{"name":565,"orcid":566},"Kristen Rhodes","https:\u002F\u002Forcid.org\u002F0000-0002-8469-5043",{"name":568,"orcid":569},"Sourav Bhadra","https:\u002F\u002Forcid.org\u002F0000-0002-5832-4695",{"name":571,"orcid":572},"Haireti Alifu","https:\u002F\u002Forcid.org\u002F0000-0002-7369-6657",{"name":574,"orcid":9},"Aviskar Giri",{"name":576,"orcid":9},"Ashutosh Pawar",{"name":578,"orcid":579},"Bishal Roy","https:\u002F\u002Forcid.org\u002F0000-0001-9912-5505",{"name":581,"orcid":582},"Supria Sarkar","https:\u002F\u002Forcid.org\u002F0000-0001-9467-5786",{"name":584,"orcid":9},"Felix Fritschi",{"tldr":586,"method":587,"finding":588,"direction":111,"opportunity":589},"用无人机多模态影像与基因型数据训练CNN，预测大豆八种种子成分。","372份大豆样本，多光谱\u002F热红外\u002FLiDAR影像加基因型与播后天数，端到端CNN","蔗糖预测最优(R²=0.80)，基因型与播期提升精度，多光谱最稳健。","可探索基因型-表型-环境交互建模，并迁移至其他作物与多时相早期预测。","2026-09-13T23:30:22.776177Z"]