[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2320":3,"related-2320":126},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":49,"search_phrases":84,"slug":87,"view_count":88,"doi":89,"paper":90,"created_at":125},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。",null,"Remote Sensing","2026-09-11T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","无人机","农业人工智能","遥感","大豆育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":57},[55,63,70,76],{"code":32,"score":56,"supported":57,"title":58,"abstract":60,"tags":62},15,true,[59],"soybean",[61,59],"大豆",[30],{"code":39,"score":13,"supported":57,"title":64,"abstract":65,"tags":69},[],[66,67,68],"深度学习","deep learning","artificial intelligence",[28],{"code":45,"score":13,"supported":57,"title":71,"abstract":72,"tags":75},[],[29,73,74],"remote sensing","multispectral",[29],{"code":77,"score":78,"supported":57,"title":79,"abstract":80,"tags":83},"I1",7,[],[81,82],"精准农业","precision agriculture",[26],[85,86],"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",4,"10.3390\u002Frs18183121",{"doi":89,"openalex_id":91,"authors":92,"venue":10,"cited_by_count":117,"oa_url":6,"card":118,"direction":122,"ingested_from":124},"W7212223520",[93,96,99,102,105,107,109,112,115],{"name":94,"orcid":95},"Vasit Sagan","https:\u002F\u002Forcid.org\u002F0000-0003-4375-2096",{"name":97,"orcid":98},"Kristen Rhodes","https:\u002F\u002Forcid.org\u002F0000-0002-8469-5043",{"name":100,"orcid":101},"Sourav Bhadra","https:\u002F\u002Forcid.org\u002F0000-0002-5832-4695",{"name":103,"orcid":104},"Haireti Alifu","https:\u002F\u002Forcid.org\u002F0000-0002-7369-6657",{"name":106,"orcid":9},"Aviskar Giri",{"name":108,"orcid":9},"Ashutosh Pawar",{"name":110,"orcid":111},"Bishal Roy","https:\u002F\u002Forcid.org\u002F0000-0001-9912-5505",{"name":113,"orcid":114},"Supria Sarkar","https:\u002F\u002Forcid.org\u002F0000-0001-9467-5786",{"name":116,"orcid":9},"Felix Fritschi",0,{"tldr":119,"method":120,"finding":121,"direction":122,"opportunity":123},"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。","农业遥感与作物表型","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。","openalex","2026-09-13T23:30:22.776177Z",{"total":127,"page":21,"page_size":127,"items":128},6,[129,207,260,339,439,504],{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":9,"source_name":135,"source_url":132,"published_at":136,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":137,"score_detail":138,"sources":144,"tags":146,"primary_topic":148,"secondary_topics":149,"classification":151,"search_phrases":180,"slug":183,"view_count":117,"doi":184,"paper":185,"created_at":206},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":139,"substance":17,"depth":140,"authority":141,"freshness":142,"relevant":21,"comment":143},12,16,13,9,"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。",[145],{"name":135,"url":132},[26,27,28,29,147],"葡萄园管理",{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},[150],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":152},{"candidates":153,"agricultural_evidence":57},[154,158,162,166,173],{"code":45,"score":17,"supported":57,"title":155,"abstract":156,"tags":157},[74],[29,73,74],[29],{"code":39,"score":56,"supported":57,"title":159,"abstract":160,"tags":161},[67],[66,67],[28],{"code":77,"score":78,"supported":57,"title":163,"abstract":164,"tags":165},[],[81,82],[26],{"code":167,"score":127,"supported":57,"title":168,"abstract":169,"tags":172},"B2",[],[170,171],"葡萄","grape",[],{"code":174,"score":127,"supported":57,"title":175,"abstract":176,"tags":179},"I4",[],[177,178],"空间分析","GIS",[],[181,182],"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":184,"openalex_id":186,"authors":187,"venue":135,"cited_by_count":117,"oa_url":132,"card":201,"direction":122,"ingested_from":124},"W7220993218",[188,190,193,195,198],{"name":189,"orcid":9},"Dimitra Passa",{"name":191,"orcid":192},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":194,"orcid":9},"Georgios Nektarios Tselos",{"name":196,"orcid":197},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":199,"orcid":200},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":202,"method":203,"finding":204,"direction":122,"opportunity":205},"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":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":135,"source_url":210,"published_at":213,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":214,"score_detail":215,"sources":219,"tags":221,"primary_topic":224,"secondary_topics":225,"classification":227,"search_phrases":244,"slug":247,"view_count":117,"doi":248,"paper":249,"created_at":259},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","2026-10-21T00:00:00Z",67,{"impact":20,"substance":216,"depth":217,"authority":139,"freshness":13,"relevant":21,"comment":218},20,17,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[220],{"name":135,"url":210},[26,28,222,29,223],"产量预测","可解释性",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[226],{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":228},{"candidates":229,"agricultural_evidence":57},[230,236,240],{"code":39,"score":139,"supported":57,"title":231,"abstract":233,"tags":235},[232],"computer vision",[234],"计算机视觉",[28],{"code":45,"score":20,"supported":57,"title":237,"abstract":238,"tags":239},[],[29,73],[222,29],{"code":77,"score":21,"supported":14,"title":241,"abstract":242,"tags":243},[],[],[26],[245,246],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":248,"openalex_id":250,"authors":251,"venue":135,"cited_by_count":117,"oa_url":210,"card":254,"direction":122,"ingested_from":124},"W7223114002",[252],{"name":253,"orcid":9},"Kadiatou Barry",{"tldr":255,"method":256,"finding":257,"direction":122,"opportunity":258},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":261,"title":262,"url":263,"summary":264,"summary_zh":265,"content":9,"source_name":266,"source_url":263,"published_at":267,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":268,"score_detail":269,"sources":272,"tags":274,"primary_topic":277,"secondary_topics":278,"classification":286,"search_phrases":313,"slug":316,"view_count":117,"doi":317,"paper":318,"created_at":338},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":139,"substance":270,"depth":217,"authority":141,"freshness":142,"relevant":21,"comment":271},21,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[273],{"name":266,"url":263},[26,28,29,275,276],"土壤盐渍化","新疆农业",{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},[279,285],{"code":280,"slug":281,"name":282,"path":283,"parent_code":284},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":287},{"candidates":288,"agricultural_evidence":57},[289,293,298,302,309],{"code":45,"score":56,"supported":57,"title":290,"abstract":291,"tags":292},[73],[29,73],[29],{"code":280,"score":19,"supported":57,"title":294,"abstract":295,"tags":297},[281],[296,281],"土壤",[],{"code":39,"score":142,"supported":57,"title":299,"abstract":300,"tags":301},[67],[],[28],{"code":303,"score":304,"supported":57,"title":305,"abstract":306,"tags":308},"H7",3,[],[307],"干旱",[],{"code":77,"score":21,"supported":14,"title":310,"abstract":311,"tags":312},[],[],[26],[314,315],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":317,"openalex_id":319,"authors":320,"venue":266,"cited_by_count":117,"oa_url":263,"card":333,"direction":122,"ingested_from":124},"W7221816183",[321,323,326,328,330],{"name":322,"orcid":9},"Mingjie Qian",{"name":324,"orcid":325},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":327,"orcid":9},"Peng Wang",{"name":329,"orcid":9},"Hangyuan Liu",{"name":331,"orcid":332},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":334,"method":335,"finding":336,"direction":122,"opportunity":337},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":345,"source_url":342,"published_at":267,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":349,"tags":351,"primary_topic":353,"secondary_topics":354,"classification":361,"search_phrases":410,"slug":413,"view_count":21,"doi":414,"paper":415,"created_at":438},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",81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":142,"relevant":21,"comment":348},"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。",[350],{"name":345,"url":342},[26,28,222,352,29],"多模态融合",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[355,360],{"code":167,"slug":356,"name":357,"path":358,"parent_code":359},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":362},{"candidates":363,"agricultural_evidence":57},[364,371,379,384,388,392,399,405],{"code":39,"score":365,"supported":57,"title":366,"abstract":367,"tags":370},11,[],[66,368,369,67],"机器学习","machine learning",[28,352],{"code":167,"score":142,"supported":57,"title":372,"abstract":373,"tags":378},[],[374,375,376,377],"苹果","芒果","orchard","apple",[],{"code":45,"score":20,"supported":57,"title":380,"abstract":381,"tags":383},[],[382,74],"无人机多光谱",[222,29],{"code":77,"score":78,"supported":57,"title":385,"abstract":386,"tags":387},[],[81,82],[26],{"code":280,"score":127,"supported":57,"title":389,"abstract":390,"tags":391},[],[296,281],[],{"code":393,"score":127,"supported":57,"title":394,"abstract":395,"tags":398},"I5",[],[396,397],"物联网","IoT",[],{"code":400,"score":304,"supported":57,"title":401,"abstract":402,"tags":404},"H6",[],[403],"农业生态",[],{"code":303,"score":304,"supported":57,"title":406,"abstract":407,"tags":409},[],[408],"气候适应",[],[411,412],"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":414,"openalex_id":416,"authors":417,"venue":345,"cited_by_count":117,"oa_url":342,"card":431,"direction":437,"ingested_from":124},"W7221795850",[418,421,423,425,427,429],{"name":419,"orcid":420},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":422,"orcid":9},"Umarfarooque Momin",{"name":424,"orcid":9},"Sudeep Kumar E",{"name":426,"orcid":9},"Itfaq Ahmad Mir",{"name":428,"orcid":9},"Ab Rouf Khan",{"name":430,"orcid":9},"Ghulam Mohammad Mir",{"tldr":432,"method":433,"finding":434,"direction":435,"opportunity":436},"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":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":9,"source_name":445,"source_url":442,"published_at":446,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":447,"score_detail":448,"sources":450,"tags":452,"primary_topic":455,"secondary_topics":456,"classification":459,"search_phrases":478,"slug":481,"view_count":21,"doi":482,"paper":483,"created_at":503},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",77,{"impact":140,"substance":270,"depth":17,"authority":141,"freshness":142,"relevant":21,"comment":449},"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。",[451],{"name":445,"url":442},[26,27,28,453,454],"遥感监测","病害预警",{"code":167,"slug":356,"name":357,"path":358,"parent_code":359},[457,458],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":460},{"candidates":461,"agricultural_evidence":57},[462,466,470,474],{"code":167,"score":217,"supported":57,"title":463,"abstract":464,"tags":465},[377],[374,170,377],[],{"code":39,"score":56,"supported":57,"title":467,"abstract":468,"tags":469},[67],[66,67],[28],{"code":45,"score":139,"supported":57,"title":471,"abstract":472,"tags":473},[74],[74],[453],{"code":77,"score":78,"supported":57,"title":475,"abstract":476,"tags":477},[],[81,82],[26],[479,480],"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":482,"openalex_id":484,"authors":485,"venue":445,"cited_by_count":117,"oa_url":497,"card":498,"direction":437,"ingested_from":124},"W7221558726",[486,489,492,494],{"name":487,"orcid":488},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":490,"orcid":491},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":493,"orcid":9},"Fatih Sinan Bir",{"name":495,"orcid":496},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":499,"method":500,"finding":501,"direction":122,"opportunity":502},"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":505,"title":506,"url":507,"summary":508,"summary_zh":9,"content":9,"source_name":509,"source_url":507,"published_at":136,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":510,"score_detail":511,"sources":513,"tags":515,"primary_topic":518,"secondary_topics":522,"classification":525,"search_phrases":544,"slug":547,"view_count":21,"doi":548,"paper":549,"created_at":581},4500,"Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11163942\u002Fv1","Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice。Research Square","Research Square",74,{"impact":17,"substance":216,"depth":17,"authority":13,"freshness":20,"relevant":21,"comment":512},"将无人机遥感与深度学习结合用于水稻白叶枯病早期检测，方法有创新、结论具应用价值，但属预印本且为细分技术进展，适合进入主题聚合而非头条精选。",[514],{"name":509,"url":507},[26,28,516,29,517],"水稻","病害检测",{"code":519,"slug":520,"name":516,"path":521,"parent_code":36},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[523,524],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":526},{"candidates":527,"agricultural_evidence":57},[528,532,536,540],{"code":519,"score":139,"supported":57,"title":529,"abstract":530,"tags":531},[520],[520],[516],{"code":39,"score":139,"supported":57,"title":533,"abstract":534,"tags":535},[67],[67],[28],{"code":45,"score":139,"supported":57,"title":537,"abstract":538,"tags":539},[73],[73],[29],{"code":77,"score":21,"supported":14,"title":541,"abstract":542,"tags":543},[],[],[26],[545,546],"无人机遥感 水稻 白叶枯病","深度学习 水稻病害 早期检测","无人机遥感水稻白叶枯病-4500","10.21203\u002Frs.3.rs-11163942\u002Fv1",{"doi":548,"openalex_id":550,"authors":551,"venue":509,"cited_by_count":117,"oa_url":507,"card":9,"direction":122,"ingested_from":124},"W7221055917",[552,555,558,560,562,565,567,569,572,574,576,578],{"name":553,"orcid":554},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":556,"orcid":557},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":559,"orcid":9},"Ian  Carlo Perilla",{"name":561,"orcid":9},"Stephen Klassen",{"name":563,"orcid":564},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":566,"orcid":9},"Raphael  Gabriel Carvajal",{"name":568,"orcid":9},"Kendrix Ortega",{"name":570,"orcid":571},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":573,"orcid":9},"Nurmi Pangesti",{"name":575,"orcid":9},"Caesar Arloo R. Centeno",{"name":577,"orcid":9},"Arnel Rala",{"name":579,"orcid":580},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","2026-10-09T23:30:18.390549Z"]