[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2160":3,"related-2160":142},{"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":49,"search_phrases":113,"slug":116,"view_count":117,"doi":118,"paper":119,"created_at":141},2160,"An Optimized Temporal Attention-based Deep Learning Framwork for Accurate Crop Yield Prediction from Multi-source Agricultural Data","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fag.d-6558","Background: In precision agriculture, crop yield prediction is crucial for supporting effective management of crops, timely settlement of farmers crop insurances, food security planning and serve as decision tool for sustainable agricultural achievements. However, prediction of crop yield is difficult due to the complex interaction behavior among diverse conditions in weather, soil properties, multispectral remote sensing observations and the dynamics in crop growth. Despite existing AI techniques like machine learning and deep learning models outperformed in predicting crop yield, they are often not able to capture the temporal dependencies. Furthermore, determining optimal hyperparameters for model training remains a challenging task, significantly influencing prediction accuracy, model convergence and generalization performance. Methods: To overcome this, the present study aimed to predict the crop yield using temporal attention (TA) based Artificial bee colony (ABC) optimized Bi - LSTM model using multi source agricultural data. The proposed model learns the sequential and temporal dependencies from both past and future time steps simultaneously, from the historical weather variables, soil properties, remote sensing derived normalized difference vegetation indices (NDVI) and historical yield data. The ABC algorithm was employed to automatically optimize the model hyperparameters with minimum run time. Result: The model is trained using district wise rice yield records of Tamil Nadu, historical weather variables, Sentinel-2 derived NDVI and soil properties. The dataset collected for major rice growing districts from 1995 to 2025 (30 years). The model shows the superior performance over conventional methods like random forest, support vector regression, deep neural network, convolutional neural network, long short-term memory models with lesser root mean square error (RMSE = 298.01 kg ha-1) and higher coefficient of determination, R2 (81.76%). Also, the training time of TA - ABC - Bi-LSTM model is comparatively less than the comparative models because of ABC optimization algorithm tuned the best hyperparameters compared to traditional trial and error manual method.","背景：在精准农业中，作物产量预测对于支持有效的作物管理、农民作物保险的及时理赔、粮食安全规划以及作为实现可持续农业的决策工具至关重要。然而，由于天气、土壤属性、多光谱遥感观测等多种条件之间复杂的交互作用以及作物生长的动态变化，作物产量预测仍然困难。尽管现有的机器学习、深度学习等人工智能技术在作物产量预测方面表现优异，但往往无法捕捉时间依赖性。此外，确定模型训练的最优超参数仍是一项具有挑战性的任务，显著影响预测精度、模型收敛性和泛化性能。方法：为解决上述问题，本研究旨在利用基于时间注意力（TA）的人工蜂群（ABC）算法优化的双向长短期记忆（Bi-LSTM）模型，结合多源农业数据预测作物产量。所提出的模型能够同时从历史和未来时间步中学习序列和时间依赖关系，数据来源包括历史气象变量、土壤属性、遥感衍生的归一化植被指数（NDVI）以及历史产量数据。采用ABC算法以最短运行时间自动优化模型超参数。结果：该模型使用泰米尔纳德邦各地区的稻米产量记录、历史气象变量、Sentinel-2衍生的NDVI和土壤属性进行训练。数据集收集了1995年至2025年（30年）主要水稻种植区的数据。该模型相比随机森林、支持向量回归、深度神经网络、卷积神经网络、长短期记忆模型等传统方法表现出更优的性能，均方根误差更低（RMSE = 298.01 kg ha-1），决定系数R²更高（81.76%）。此外，TA-ABC-Bi-LSTM模型的训练时间相比对比模型更短，因为ABC优化算法相比传统的试错手动方法能够调优出最佳超参数。",null,"Agricultural Science Digest - A Research Journal","2026-09-10T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,12,9,1,"提出时序注意力结合人工蜂群优化的Bi-LSTM产量预测模型，基于30年多源数据取得较高精度，方法新颖且数据规模可观，对精准农业与农业保险决策有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","水稻","遥感","作物产量预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39,44],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":45,"slug":46,"name":47,"path":48,"parent_code":37},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":56},[55,64,71,76,83,91,99,106],{"code":33,"score":19,"supported":56,"title":57,"abstract":59,"tags":63},true,[58],"deep learning",[60,61,62,58],"深度学习","机器学习","machine learning",[28],{"code":40,"score":65,"supported":56,"title":66,"abstract":67,"tags":70},11,[],[30,68,69],"remote sensing","multispectral",[31,30],{"code":45,"score":65,"supported":56,"title":72,"abstract":74,"tags":75},[73],"agricultural data",[73],[],{"code":77,"score":13,"supported":56,"title":78,"abstract":79,"tags":82},"A1",[],[29,80,81],"稻米","rice",[29],{"code":84,"score":85,"supported":56,"title":86,"abstract":87,"tags":90},"I1",7,[],[88,89],"精准农业","precision agriculture",[27],{"code":92,"score":93,"supported":56,"title":94,"abstract":95,"tags":98},"A8",6,[],[96,97],"粮食安全","food security",[],{"code":100,"score":93,"supported":56,"title":101,"abstract":102,"tags":105},"H2",[],[103,104],"土壤","soil",[],{"code":107,"score":108,"supported":56,"title":109,"abstract":110,"tags":112},"H8",3,[],[111],"可持续农业",[],[114,115],"作物产量预测 农业人工智能 智慧农业 水稻","作物产量预测 农业人工智能","作物产量预测农业人工智能智慧农业水稻-2160",2,"10.18805\u002Fag.d-6558",{"doi":118,"openalex_id":120,"authors":121,"venue":10,"cited_by_count":132,"oa_url":6,"card":133,"direction":139,"ingested_from":140},"W7212119230",[122,124,126,128,130],{"name":123,"orcid":9},"M. Lokeshwari",{"name":125,"orcid":9},"Nivethitha Manavalagan",{"name":127,"orcid":9},"P. Amrutha",{"name":129,"orcid":9},"S. Harinee",{"name":131,"orcid":9},"N.R. Divyasree",0,{"tldr":134,"method":135,"finding":136,"direction":137,"opportunity":138},"提出TA-ABC-Bi-LSTM模型，用多源农业数据精准预测水稻产量。","基于时间注意力与人工蜂群优化的Bi-LSTM，融合气象、土壤、Sentinel-","模型RMSE为298.01 kg\u002Fha，R²达81.76%，优于RF、SVR、DNN、CNN、LST","农业人工智能与决策模型","可探索轻量化注意力机制与迁移学习，将模型推广至多作物、小样本及实时预测场景。","农业遥感与作物表型","openalex","2026-09-11T23:30:29.551995Z",{"total":93,"page":22,"page_size":93,"items":143},[144,223,307,370,420,493],{"id":145,"title":146,"url":147,"summary":148,"summary_zh":9,"content":9,"source_name":149,"source_url":147,"published_at":150,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":151,"score_detail":152,"sources":156,"tags":158,"primary_topic":160,"secondary_topics":163,"classification":166,"search_phrases":185,"slug":188,"view_count":22,"doi":189,"paper":190,"created_at":222},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","2026-10-08T00:00:00Z",74,{"impact":19,"substance":153,"depth":19,"authority":13,"freshness":154,"relevant":22,"comment":155},20,8,"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。",[157],{"name":149,"url":147},[27,28,29,30,159],"病害检测",{"code":77,"slug":81,"name":29,"path":161,"parent_code":162},"\u002Ftopic\u002Ffield-crops\u002Frice","A",[164,165],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":167},{"candidates":168,"agricultural_evidence":56},[169,173,177,181],{"code":77,"score":20,"supported":56,"title":170,"abstract":171,"tags":172},[81],[81],[29],{"code":33,"score":20,"supported":56,"title":174,"abstract":175,"tags":176},[58],[58],[28],{"code":40,"score":20,"supported":56,"title":178,"abstract":179,"tags":180},[68],[68],[30],{"code":84,"score":22,"supported":14,"title":182,"abstract":183,"tags":184},[],[],[27],[186,187],"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":189,"openalex_id":191,"authors":192,"venue":149,"cited_by_count":132,"oa_url":147,"card":9,"direction":139,"ingested_from":140},"W7221055917",[193,196,199,201,203,206,208,210,213,215,217,219],{"name":194,"orcid":195},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":197,"orcid":198},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":200,"orcid":9},"Ian  Carlo Perilla",{"name":202,"orcid":9},"Stephen Klassen",{"name":204,"orcid":205},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":207,"orcid":9},"Raphael  Gabriel Carvajal",{"name":209,"orcid":9},"Kendrix Ortega",{"name":211,"orcid":212},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":214,"orcid":9},"Nurmi Pangesti",{"name":216,"orcid":9},"Caesar Arloo R. Centeno",{"name":218,"orcid":9},"Arnel Rala",{"name":220,"orcid":221},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","2026-10-09T23:30:18.390549Z",{"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":231,"is_selected":14,"score":232,"score_detail":233,"sources":237,"tags":241,"primary_topic":243,"secondary_topics":244,"classification":247,"search_phrases":291,"slug":294,"view_count":132,"doi":295,"paper":296,"created_at":306},4310,"Comprehensive remote sensing and machine learning framework for multi-regional rice yield forecasting under hydroclimatic variability in Sahelian Niger","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23149579","It is crucial to forecast rice yields effectively in order to improve agricultural planning and resilience to climate change in the Sahel, where production is greatly affected by hydroclimatic variability. This research has developed an integrated framework combining remote sensing analysis and machine learning to assess the variability in rice production in Niamey, Dosso, Tillaberi and Diffa, in Niger, over the period 2010–2025. They combined Landsat-derived NDVI and EVI with parameters such as evapotranspiration, precipitation, temperature, humidity, solar radiation, soil moisture, cultivated area, year and season. We processed and harmonised the datasets using Google Earth Engine and Python. We then evaluated four regression algorithms separately, Random Forest (RF), k-nearest neighbours (KNN), support vector regression (SVR) and XGBoost (XGB), for each region. The correlation analysis revealed modest positive links between rice yield and the NDVI (r = 0.27) and the EVI (r = 0.14), while a strong correlation was observed between the NDVI and the EVI (r = 0.80). The vegetation indices showed marked seasonal and interannual variability, generally exhibiting higher values during the wet season. Model performance varied considerably from one region to another","在萨赫勒地区，有效预测水稻产量对于改善农业规划和增强气候变化适应能力至关重要，因为该地区的生产在很大程度上受到水文气候变率的制约。本研究开发了一个集成框架，将遥感分析与机器学习相结合，以评估尼日尔尼亚美、多索、蒂拉贝里和迪法在2010—2025年期间水稻产量的变异性。研究将基于Landsat的归一化植被指数（NDVI）和增强型植被指数（EVI）与蒸散发、降水、温度、湿度、太阳辐射、土壤水分、耕地面积、年份和季节等参数相结合。利用Google Earth Engine和Python对数据集进行了处理和协调。随后，针对每个地区分别评估了四种回归算法：随机森林（RF）、k近邻（KNN）、支持向量回归（SVR）和XGBoost（XGB）。相关分析显示，水稻产量与NDVI（r = 0.27）和EVI（r = 0.14）之间存在弱正相关，而NDVI与EVI之间则观察到强相关（r = 0.80）。植被指数表现出明显的季节性和年际变异性，通常在湿季呈现较高值。模型性能在不同地区之间差异显著。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-05T00:00:00Z",25,72,{"impact":20,"substance":18,"depth":234,"authority":235,"freshness":21,"relevant":22,"comment":236},17,13,"基于Landsat植被指数与四种机器学习模型的多区域水稻产量预测研究，方法扎实、数据跨度长，但属区域性学术成果，对国内农业信息化有方法借鉴价值。",[238,239],{"name":229,"url":226},{"name":229,"url":240},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23149580",[27,28,29,242,30],"产量预测",{"code":77,"slug":81,"name":29,"path":161,"parent_code":162},[245,246],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":248},{"candidates":249,"agricultural_evidence":56},[250,254,259,263,267,274,281,287],{"code":40,"score":17,"supported":56,"title":251,"abstract":252,"tags":253},[68],[30,68],[242,30],{"code":77,"score":255,"supported":56,"title":256,"abstract":257,"tags":258},15,[81],[29,81],[29],{"code":33,"score":255,"supported":56,"title":260,"abstract":261,"tags":262},[62],[61,62],[28],{"code":100,"score":93,"supported":56,"title":264,"abstract":265,"tags":266},[],[103,104],[],{"code":268,"score":93,"supported":56,"title":269,"abstract":270,"tags":273},"H3",[],[271,272],"蒸散发","evapotranspiration",[],{"code":275,"score":93,"supported":56,"title":276,"abstract":277,"tags":280},"H7",[],[278,279],"气候变化","climate change",[],{"code":282,"score":108,"supported":56,"title":283,"abstract":284,"tags":286},"J1",[],[285],"农业规划",[],{"code":84,"score":22,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[27],[292,293],"尼日尔 水稻 遥感 产量预测","Landsat NDVI 水稻 机器学习","尼日尔水稻遥感产量预测-4310","10.5281\u002Fzenodo.23149579",{"doi":295,"openalex_id":297,"authors":298,"venue":229,"cited_by_count":132,"oa_url":226,"card":301,"direction":139,"ingested_from":140},"W7220391503",[299],{"name":300,"orcid":9},"Battula Vijaya Saradhi Halarou Saidou Souley",{"tldr":302,"method":303,"finding":304,"direction":139,"opportunity":305},"结合遥感植被指数与机器学习，预测尼日尔萨赫勒四地区2010–2025年水稻产量。","Landsat NDVI\u002FEVI 加气象土壤变量，GEE与Python处理，比较","NDVI\u002FEVI与产量仅弱相关，模型精度区域差异大，植被指数呈明显季节与年际波动。","可引入时序深度学习与迁移学习，解决跨区域泛化差及植被指数解释力弱的问题。","2026-10-06T23:30:20.077977Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":312,"content":9,"source_name":313,"source_url":310,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":319,"tags":321,"primary_topic":323,"secondary_topics":324,"classification":327,"search_phrases":348,"slug":351,"view_count":117,"doi":352,"paper":353,"created_at":369},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","Agriculture","2026-09-21T00:00:00Z",80,{"impact":19,"substance":317,"depth":19,"authority":235,"freshness":21,"relevant":22,"comment":318},22,"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[320],{"name":313,"url":310},[27,28,29,30,322],"作物表型",{"code":77,"slug":81,"name":29,"path":161,"parent_code":162},[325,326],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":328},{"candidates":329,"agricultural_evidence":56},[330,335,339,344],{"code":77,"score":19,"supported":56,"title":331,"abstract":332,"tags":334},[81],[29,333,81],"稻田",[29],{"code":33,"score":255,"supported":56,"title":336,"abstract":337,"tags":338},[58],[60,58],[28],{"code":40,"score":154,"supported":56,"title":340,"abstract":341,"tags":343},[],[322,342],"crop phenotyping",[322,30],{"code":84,"score":85,"supported":56,"title":345,"abstract":346,"tags":347},[],[88,89],[27],[349,350],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164","10.3390\u002Fagriculture16182034",{"doi":352,"openalex_id":354,"authors":355,"venue":313,"cited_by_count":132,"oa_url":310,"card":364,"direction":139,"ingested_from":140},"W7213887432",[356,359,361],{"name":357,"orcid":358},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":360,"orcid":9},"Noboru Noguchi",{"name":362,"orcid":363},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":365,"method":366,"finding":367,"direction":139,"opportunity":368},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z",{"id":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":9,"source_name":229,"source_url":373,"published_at":376,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":378,"sources":380,"tags":382,"primary_topic":384,"secondary_topics":385,"classification":387,"search_phrases":404,"slug":407,"view_count":132,"doi":408,"paper":409,"created_at":419},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":154,"substance":153,"depth":234,"authority":20,"freshness":13,"relevant":22,"comment":379},"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。",[381],{"name":229,"url":373},[27,28,242,30,383],"可解释性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[386],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":388},{"candidates":389,"agricultural_evidence":56},[390,396,400],{"code":33,"score":20,"supported":56,"title":391,"abstract":393,"tags":395},[392],"computer vision",[394],"计算机视觉",[28],{"code":40,"score":154,"supported":56,"title":397,"abstract":398,"tags":399},[],[30,68],[242,30],{"code":84,"score":22,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[27],[405,406],"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":408,"openalex_id":410,"authors":411,"venue":229,"cited_by_count":132,"oa_url":373,"card":414,"direction":139,"ingested_from":140},"W7223114002",[412],{"name":413,"orcid":9},"Kadiatou Barry",{"tldr":415,"method":416,"finding":417,"direction":139,"opportunity":418},"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":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":423,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":232,"score_detail":428,"sources":430,"tags":432,"primary_topic":435,"secondary_topics":436,"classification":442,"search_phrases":467,"slug":470,"view_count":132,"doi":471,"paper":472,"created_at":492},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",{"impact":20,"substance":18,"depth":234,"authority":235,"freshness":21,"relevant":22,"comment":429},"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。",[431],{"name":426,"url":423},[27,28,30,433,434],"土壤盐渍化","新疆农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[437,441],{"code":100,"slug":104,"name":438,"path":439,"parent_code":440},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":443},{"candidates":444,"agricultural_evidence":56},[445,449,454,458,463],{"code":40,"score":255,"supported":56,"title":446,"abstract":447,"tags":448},[68],[30,68],[30],{"code":100,"score":450,"supported":56,"title":451,"abstract":452,"tags":453},14,[104],[103,104],[],{"code":33,"score":21,"supported":56,"title":455,"abstract":456,"tags":457},[58],[],[28],{"code":275,"score":108,"supported":56,"title":459,"abstract":460,"tags":462},[],[461],"干旱",[],{"code":84,"score":22,"supported":14,"title":464,"abstract":465,"tags":466},[],[],[27],[468,469],"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":471,"openalex_id":473,"authors":474,"venue":426,"cited_by_count":132,"oa_url":423,"card":487,"direction":139,"ingested_from":140},"W7221816183",[475,477,480,482,484],{"name":476,"orcid":9},"Mingjie Qian",{"name":478,"orcid":479},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":481,"orcid":9},"Peng Wang",{"name":483,"orcid":9},"Hangyuan Liu",{"name":485,"orcid":486},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":488,"method":489,"finding":490,"direction":139,"opportunity":491},"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":494,"title":495,"url":496,"summary":497,"summary_zh":498,"content":9,"source_name":499,"source_url":496,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":500,"score_detail":501,"sources":503,"tags":505,"primary_topic":507,"secondary_topics":508,"classification":516,"search_phrases":562,"slug":565,"view_count":22,"doi":566,"paper":567,"created_at":589},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":19,"substance":317,"depth":19,"authority":450,"freshness":21,"relevant":22,"comment":502},"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。",[504],{"name":499,"url":496},[27,28,242,506,30],"多模态融合",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[509,515],{"code":510,"slug":511,"name":512,"path":513,"parent_code":514},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":517},{"candidates":518,"agricultural_evidence":56},[519,523,531,536,540,544,551,557],{"code":33,"score":65,"supported":56,"title":520,"abstract":521,"tags":522},[],[60,61,62,58],[28,506],{"code":510,"score":21,"supported":56,"title":524,"abstract":525,"tags":530},[],[526,527,528,529],"苹果","芒果","orchard","apple",[],{"code":40,"score":154,"supported":56,"title":532,"abstract":533,"tags":535},[],[534,69],"无人机多光谱",[242,30],{"code":84,"score":85,"supported":56,"title":537,"abstract":538,"tags":539},[],[88,89],[27],{"code":100,"score":93,"supported":56,"title":541,"abstract":542,"tags":543},[],[103,104],[],{"code":545,"score":93,"supported":56,"title":546,"abstract":547,"tags":550},"I5",[],[548,549],"物联网","IoT",[],{"code":552,"score":108,"supported":56,"title":553,"abstract":554,"tags":556},"H6",[],[555],"农业生态",[],{"code":275,"score":108,"supported":56,"title":558,"abstract":559,"tags":561},[],[560],"气候适应",[],[563,564],"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":566,"openalex_id":568,"authors":569,"venue":499,"cited_by_count":132,"oa_url":496,"card":583,"direction":588,"ingested_from":140},"W7221795850",[570,573,575,577,579,581],{"name":571,"orcid":572},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":574,"orcid":9},"Umarfarooque Momin",{"name":576,"orcid":9},"Sudeep Kumar E",{"name":578,"orcid":9},"Itfaq Ahmad Mir",{"name":580,"orcid":9},"Ab Rouf Khan",{"name":582,"orcid":9},"Ghulam Mohammad Mir",{"tldr":584,"method":585,"finding":586,"direction":137,"opportunity":587},"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。","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"]