[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4237":3,"related-4237":101},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":67,"slug":70,"view_count":21,"doi":71,"paper":72,"created_at":100},4237,"A Feature Engineering Framework for High-Precision Land Use and Land Cover Classification Using Remote Sensing Images","https:\u002F\u002Fdoi.org\u002F10.48084\u002Fetasr.20795","Accurate Land Use\u002FLand Cover (LULC) classification is important for efficient natural resource management and for understanding how weather conditions modify the environment. For precise and effective LULC classification, researchers need higher-quality databases and robust classification models. As classification models, geospatial analytical tools, and satellite data become more accessible, it is vital to systematically evaluate different combinations of satellite data and classification methods to help select the best approach for LULC classification. Currently, Deep Learning (DL) algorithms have gained popularity and have become the new standard for data handling in remote sensing research. DL architectures have drawn significant attention owing to their superior performance in classification tasks and other machine vision applications. DL-based classification of very high-resolution satellite images is an important task that provides valuable information for various geospatial applications, particularly LULC mapping. In this paper, we present a novel Enhanced Object Detection and Feature Engineering for High-Precision Land Use and Land Cover (EODFE-HPLULC) algorithm for satellite remote sensing. The main purpose of the EODFE-HPLULC methodology is to demonstrate its efficacy in achieving high-precision classification for real-world remote sensing applications. For object detection, YOLOv12 can be deployed to provide fast, accurate identification of land features. Next, the EfficientNet-B5 model serves as the backbone for feature engineering, extracting high-level, discriminative features from the detected objects. Finally, the deep belief network model performs LULC classification. To evaluate the enriched classification results of the EODFE-HPLULC model, we conduct a comprehensive set of experiments on the benchmark dataset. The comprehensive comparison results demonstrated that the EODFE-HPLULC model outperformed recent techniques, achieving a classification accuracy of 99.14% on the Remote Sensing Satellite Image dataset.","准确的土地利用\u002F土地覆盖（LULC）分类对于高效管理自然资源以及理解天气条件如何改变环境具有重要意义。为实现精确有效的LULC分类，研究人员需要更高质量的数据库和稳健的分类模型。随着分类模型、地理空间分析工具和卫星数据日益普及，系统评估卫星数据与分类方法的不同组合，以帮助选择LULC分类的最佳方案，显得至关重要。当前，深度学习（DL）算法已广受欢迎，并成为遥感研究中数据处理的新标准。DL架构因其在分类任务和其他机器视觉应用中的卓越性能而备受关注。基于DL的超高分辨率卫星图像分类是一项重要任务，可为各类地理空间应用，尤其是LULC制图，提供有价值的信息。本文提出了一种用于卫星遥感的新型增强目标检测与特征工程高精度土地利用与土地覆盖（EODFE-HPLULC）算法。EODFE-HPLULC方法的主要目的是展示其在实际遥感应用中实现高精度分类的有效性。在目标检测方面，可部署YOLOv12以快速、准确地识别地物特征。接下来，EfficientNet-B5模型作为特征工程的主干网络，从检测到的目标中提取高层次的判别性特征。最后，深度信念网络模型执行LULC分类。为评估EODFE-HPLULC模型丰富的分类结果，我们在基准数据集上开展了一系列全面的实验。综合对比结果表明，EODFE-HPLULC模型优于近期技术，在遥感卫星图像数据集上达到了99.14%的分类准确率。",null,"Engineering Technology & Applied Science Research","2026-10-02T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,16,8,1,"提出融合YOLOv12与EfficientNet-B5的遥感LULC分类框架，方法新颖、精度数据明确，对农业遥感监测有参考价值，但属学术论文、影响面偏细分领域。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","深度学习","遥感","土地利用分类",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":66},[38,46,54,61],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"I3",15,true,[43],"remote sensing",[29,43],[29],{"code":47,"score":48,"supported":41,"title":49,"abstract":50,"tags":53},"I2",11,[],[51,28,52],"目标检测","deep learning",[27,28],{"code":55,"score":56,"supported":41,"title":57,"abstract":58,"tags":60},"I4",3,[],[59],"空间分析",[],{"code":62,"score":21,"supported":14,"title":63,"abstract":64,"tags":65},"I1",[],[],[26],"农业相关性需要复核",[68,69],"YOLOv12 EfficientNet-B5 遥感分类","EODFE-HPLULC 土地覆盖分类","YOLOv12EfficientNet-B5遥感分类-4237","10.48084\u002Fetasr.20795",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":91,"oa_url":92,"card":93,"direction":97,"ingested_from":99},"W7218379419",[75,78,80,83,86,88],{"name":76,"orcid":77},"U. M. Prakash","https:\u002F\u002Forcid.org\u002F0000-0003-0776-9616",{"name":79,"orcid":9},"M. Eliazer",{"name":81,"orcid":82},"Rajamohan Parthasarathy","https:\u002F\u002Forcid.org\u002F0000-0002-1571-6250",{"name":84,"orcid":85},"Mohammed Altaf Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-0355-7835",{"name":87,"orcid":9},"Sultan Alqahtani",{"name":89,"orcid":90},"Qutubuddin Mohammed","https:\u002F\u002Forcid.org\u002F0009-0009-7585-1137",0,"https:\u002F\u002Fwww.etasr.com\u002Findex.php\u002FETASR\u002Farticle\u002Fdownload\u002F20795\u002F7235",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"提出EODFE-HPLULC框架，结合YOLOv12、EfficientNet-B5和深度信念网络实","YOLOv12目标检测、EfficientNet-B5特征提取、深度信念网络分类","在遥感卫星图像数据集上分类准确率达99.14%，优于近期方法。","农业遥感与作物表型","可探索轻量化模型在边缘设备部署，或迁移至多时相作物分类与变化检测任务。","openalex","2026-10-05T23:30:27.886624Z",{"total":102,"page":21,"page_size":102,"items":103},6,[104,185,244,308,368,419],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":119,"tags":121,"primary_topic":123,"secondary_topics":129,"classification":130,"search_phrases":159,"slug":162,"view_count":102,"doi":163,"paper":164,"created_at":184},3277,"Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193272","Soil moisture (SM) is critical for climate, water, and agriculture, but Soil Moisture Active Passive (SMAP) passive microwave products have coarse resolution, limiting regional applications. This study develops an SM downscaling framework based on Transformer and its variants (PatchTST and iTransformer), integrating multi-source satellite and groundwater data to generate 1 km daily SM products (2015–2022). Compared with Random Forest (RF), Long Short-Term Memory (LSTM), and Convolutional Neural Network–LSTM (CNN-LSTM), Transformer and its variants achieve superior accuracy and generalization. Validated against in situ measurements and SMCI1.0, the Transformer-downscaled SM product achieved the best accuracy with ubRMSE = 0.0372 m3\u002Fm3 and RMSE = 0.0591 m3\u002Fm3. The downscaled SM dataset not only captured finer spatial details but also preserved the spatial patterns and seasonal dynamics of the original SMAP product and showed good responsiveness to precipitation events. Feature importance analysis revealed that, aside from precipitation, the diurnal land surface temperature difference had a greater impact on SM than individual daytime or nighttime land surface temperature, ranking just below vegetation indices and soil texture factors, while groundwater level showed higher importance than elevation and surface temperature. This study confirms the effectiveness of Transformer-based models for SM spatial downscaling, providing a novel framework integrating remote sensing and deep hydrological information to generate accurate 1 km SM products.","土壤水分（SM）对气候、水资源和农业至关重要，但土壤水分主动被动（SMAP）被动微波产品分辨率较粗，限制了区域应用。本研究构建了一个基于Transformer及其变体（PatchTST和iTransformer）的土壤水分降尺度框架，融合多源卫星和地下水数据，生成1 km日尺度土壤水分产品（2015—2022年）。与随机森林（RF）、长短期记忆网络（LSTM）和卷积神经网络—长短期记忆网络（CNN-LSTM）相比，Transformer及其变体取得了更高的精度和泛化能力。利用站点实测数据和SMCI1.0进行验证，Transformer降尺度土壤水分产品精度最优，ubRMSE = 0.0372 m³\u002Fm³，RMSE = 0.0591 m³\u002Fm³。降尺度土壤水分数据集不仅捕捉到了更精细的空间细节，还保留了原始SMAP产品的空间格局和季节动态，并对降水事件表现出良好的响应。特征重要性分析表明，除降水外，昼夜地表温差对土壤水分的影响大于单独的白天或夜间地表温度，其重要性仅次于植被指数和土壤质地因子，而地下水埋深的重要性高于高程和地表温度。本研究证实了基于Transformer的模型在土壤水分空间降尺度中的有效性，为融合遥感和深层水文信息生成准确的1 km土壤水分产品提供了一种新框架。","Remote Sensing","2026-09-22T00:00:00Z",81,{"impact":114,"substance":115,"depth":114,"authority":116,"freshness":117,"relevant":21,"comment":118},18,22,14,9,"基于Transformer的SMAP土壤水分1km降尺度研究，方法新颖、验证充分，对区域农业旱情监测有实用价值。",[120],{"name":110,"url":107},[26,27,28,29,122],"土壤墒情",{"code":124,"slug":125,"name":126,"path":127,"parent_code":128},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":131,"locked":14,"version":34,"method":35,"evidence":132},"classified",{"candidates":133,"agricultural_evidence":41},[134,139,144,150,155],{"code":124,"score":40,"supported":41,"title":135,"abstract":136,"tags":138},[125],[137,125],"土壤",[122],{"code":39,"score":140,"supported":41,"title":141,"abstract":142,"tags":143},7,[],[29,43],[29],{"code":145,"score":56,"supported":41,"title":146,"abstract":147,"tags":149},"H3",[],[148],"水资源",[],{"code":47,"score":151,"supported":14,"title":152,"abstract":153,"tags":154},2,[],[],[27,28],{"code":62,"score":21,"supported":14,"title":156,"abstract":157,"tags":158},[],[],[26],[160,161],"SMAP 土壤水分 降尺度","Transformer 土壤水分 安徽","SMAP土壤水分降尺度-3277","10.3390\u002Frs18193272",{"doi":163,"openalex_id":165,"authors":166,"venue":110,"cited_by_count":91,"oa_url":107,"card":179,"direction":97,"ingested_from":99},"W7208807695",[167,169,171,173,175,177],{"name":168,"orcid":9},"Yuyang Fan",{"name":170,"orcid":9},"Jianwei Ma",{"name":172,"orcid":9},"Mengmeng Li",{"name":174,"orcid":9},"Changqing Ke",{"name":176,"orcid":9},"Bin Cheng",{"name":178,"orcid":9},"Zheng Duan",{"tldr":180,"method":181,"finding":182,"direction":97,"opportunity":183},"基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。","Transformer、PatchTST、iTransformer，融合多源卫星","Transformer降尺度产品精度最优（ubRMSE=0.0372），保留原产品时空格局并响应降水","可探索Transformer降尺度产品在区域干旱监测、灌溉决策及作物估产中的耦合应用。","2026-09-23T23:30:19.132307Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":193,"is_selected":14,"score":194,"score_detail":195,"sources":197,"tags":201,"primary_topic":203,"secondary_topics":208,"classification":209,"search_phrases":224,"slug":227,"view_count":56,"doi":228,"paper":229,"created_at":243},1534,"Deep Learning Based Semantic Segmentation of Satellite Imagery for Land Cover Classification","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22251467","Land cover classification from remote sensing imagery underpins agricultural monitoring, urban planning, disaster response, and environmental assessment, and deep learning has become the dominant paradigm for extracting land cover information from satellite scenes at pixel-level granularity. This survey reviews recent advances in deep learning-based land cover classification and semantic segmentation, organizing the literature into convolutional neural network (CNN)-based encoder–decoder architectures, attention and self-attention mechanisms, transformer and hybrid CNN–Transformer architectures, and optimization-augmented and foundation-model-adapted approaches. We summarize the datasets, evaluation metrics, and comparative performance reported across the reviewed studies, and use a representative hybrid EfficientNet–Vision Transformer framework as a case study to illustrate current design trends. Persistent challenges are identified, including class imbalance for narrow structures such as roads, the computational cost of hybrid transformer-based models, limited cross-dataset generalization, and the scarcity of densely annotated data. The survey concludes by outlining promising future directions, including lightweight hybrid architectures, self-supervised and weakly supervised learning, adaptation of large vision foundation models, multi-sensor data fusion, and explainable land cover mapping.","基于遥感影像的土地覆盖分类支撑着农业监测、城市规划、灾害响应和环境评估，而深度学习已成为从卫星场景中提取像素级土地覆盖信息的主导范式。本综述回顾了基于深度学习的土地覆盖分类与语义分割的最新进展，将相关文献组织为基于卷积神经网络（CNN）的编码器-解码器架构、注意力与自注意力机制、Transformer及CNN-Transformer混合架构，以及优化增强和基础模型适配方法等类别。我们总结了所评述研究中报告的数据集、评估指标和对比性能，并以一个具有代表性的混合EfficientNet-Vision Transformer框架作为案例研究，以说明当前的设计趋势。本文指出了持续存在的挑战，包括道路等狭窄结构的类别不平衡问题、基于混合Transformer模型的计算成本、跨数据集泛化能力有限，以及密集标注数据的稀缺性。综述最后概述了有前景的未来方向，包括轻量级混合架构、自监督和弱监督学习、大型视觉基础模型的适配、多传感器数据融合以及可解释的土地覆盖制图。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-02T00:00:00Z",25,67,{"impact":17,"substance":18,"depth":114,"authority":13,"freshness":140,"relevant":21,"comment":196},"综述深度学习用于卫星影像土地覆盖分类，对农业监测有参考价值，但偏学术，影响有限。",[198,199],{"name":191,"url":188},{"name":191,"url":200},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22251468",[26,27,28,29,202],"土地覆盖分类",{"code":47,"slug":204,"name":205,"path":206,"parent_code":207},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":131,"locked":14,"version":34,"method":35,"evidence":210},{"candidates":211,"agricultural_evidence":41},[212,216,220],{"code":47,"score":19,"supported":41,"title":213,"abstract":214,"tags":215},[52],[28,52],[27,28],{"code":39,"score":140,"supported":41,"title":217,"abstract":218,"tags":219},[],[29,43],[29],{"code":62,"score":21,"supported":14,"title":221,"abstract":222,"tags":223},[],[],[26],[225,226],"农业人工智能 土地覆盖分类 智慧农业 深度学习","农业人工智能 土地覆盖分类","农业人工智能土地覆盖分类智慧农业深度学习-1534","10.5281\u002Fzenodo.22251467",{"doi":228,"openalex_id":230,"authors":231,"venue":191,"cited_by_count":91,"oa_url":188,"card":238,"direction":97,"ingested_from":99},"W7206172334",[232,234,236],{"name":233,"orcid":9},"Antony Daniel Rex. J",{"name":235,"orcid":9},"R. Vidya",{"name":237,"orcid":9},"A. Martin",{"tldr":239,"method":240,"finding":241,"direction":97,"opportunity":242},"综述深度学习用于卫星影像土地覆盖分类的语义分割方法，并分析挑战与未来方向。","综述CNN、Transformer及混合架构，案例为EfficientNet-V","混合架构性能优，但存在类别不平衡、计算成本高、泛化差等问题。","可探索轻量级混合架构或自监督学习，以提升农业土地覆盖分类的精度与泛化能力。","2026-09-03T23:30:30.230250Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":9,"content":9,"source_name":249,"source_url":9,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":254,"tags":256,"primary_topic":258,"secondary_topics":263,"classification":264,"search_phrases":293,"slug":296,"view_count":297,"doi":9,"paper":298,"created_at":307},1371,"Diag-STFN：用于全球收获前作物产量预测的诊断时空多模态融合网络","https:\u002F\u002Flinkinghub.elsevier.com\u002Fretrieve\u002Fpii\u002FS1574954126002669","针对全球 38 国玉米和 29 国小麦在早、中、晚三个收获前时段的产量预测，提出 Diag-STFN 诊断时空多模态融合网络；根据数据集特征自动选择时空趋势耦合与空间模块激活；在 CY-Bench 基准下，玉米和小麦所有时段均取得最低池化 NRMSE，在 MAPE 和 KGE 上亦保持领先；消融研究表明诊断模块选择是性能提升的首要因素，融合策略是次要因素；方差分解显示国别间差异大于模型间差异。","Ecological Informatics · Elsevier · 2026-08-31","2026-08-30T16:00:00Z",75,{"impact":114,"substance":115,"depth":114,"authority":116,"freshness":56,"relevant":21,"comment":253},"全球尺度的作物产量预测研究，方法新颖且数据规模大，对农业信息化有参考价值。",[255],{"name":249,"url":247},[26,27,257,28,29],"产量预测",{"code":259,"slug":260,"name":261,"path":262,"parent_code":9},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":265,"locked":14,"version":34,"method":35,"evidence":266},"partial",{"candidates":267,"agricultural_evidence":41,"reason":292},[268,274,280,284,288],{"code":269,"score":56,"supported":41,"title":270,"abstract":271,"tags":273},"A2",[],[272],"小麦",[],{"code":275,"score":56,"supported":41,"title":276,"abstract":277,"tags":279},"A3",[],[278],"玉米",[],{"code":47,"score":151,"supported":14,"title":281,"abstract":282,"tags":283},[],[],[27,28],{"code":39,"score":151,"supported":14,"title":285,"abstract":286,"tags":287},[],[],[257,29],{"code":62,"score":21,"supported":14,"title":289,"abstract":290,"tags":291},[],[],[26],"一级领域明确，二级方向需要细分",[294,295],"农业人工智能 产量预测 智慧农业 深度学习","农业人工智能 产量预测","农业人工智能产量预测智慧农业深度学习-1371",5,{"doi":9,"openalex_id":9,"authors":299,"venue":9,"cited_by_count":91,"oa_url":9,"card":300,"direction":304,"ingested_from":306},[],{"tldr":301,"method":302,"finding":303,"direction":304,"opportunity":305},"提出Diag-STFN网络，用于全球收获前玉米和小麦产量预测，在CY-Bench基准上取得最优性能。","诊断时空多模态融合网络，自动选择时空趋势耦合与空间模块激活，基于CY-Bench","Diag-STFN在玉米和小麦所有时段均取得最低NRMSE，诊断模块选择是性能提升首要因素。","农业人工智能与决策模型","国别间差异大于模型间差异，可探索区域自适应或跨域迁移的产量预测模型，以提升全球适用性。","agent","2026-09-02T00:05:07.231943Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":313,"content":9,"source_name":191,"source_url":311,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":318,"tags":320,"primary_topic":323,"secondary_topics":324,"classification":325,"search_phrases":352,"slug":355,"view_count":91,"doi":356,"paper":357,"created_at":367},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","2026-10-21T00:00:00Z",74,{"impact":17,"substance":115,"depth":114,"authority":17,"freshness":13,"relevant":21,"comment":317},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[319],{"name":191,"url":311},[26,27,321,29,322],"粮食安全","气候分析",{"code":47,"slug":204,"name":205,"path":206,"parent_code":207},[],{"status":131,"locked":14,"version":34,"method":35,"evidence":326},{"candidates":327,"agricultural_evidence":41},[328,333,339,344,348],{"code":47,"score":117,"supported":41,"title":329,"abstract":331,"tags":332},[330],"artificial intelligence",[],[27],{"code":334,"score":56,"supported":41,"title":335,"abstract":336,"tags":338},"I5",[],[337],"农业传感",[],{"code":340,"score":21,"supported":14,"title":341,"abstract":342,"tags":343},"A8",[],[],[321],{"code":62,"score":21,"supported":14,"title":345,"abstract":346,"tags":347},[],[],[26],{"code":39,"score":21,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[29],[353,354],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":356,"openalex_id":358,"authors":359,"venue":191,"cited_by_count":91,"oa_url":311,"card":362,"direction":304,"ingested_from":99},"W7223111766",[360],{"name":361,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":363,"method":364,"finding":365,"direction":97,"opportunity":366},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z",{"id":369,"title":370,"url":371,"summary":372,"summary_zh":373,"content":9,"source_name":191,"source_url":371,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":194,"score_detail":374,"sources":377,"tags":379,"primary_topic":381,"secondary_topics":382,"classification":387,"search_phrases":404,"slug":406,"view_count":91,"doi":407,"paper":408,"created_at":418},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。",{"impact":20,"substance":18,"depth":375,"authority":17,"freshness":13,"relevant":21,"comment":376},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。",[378],{"name":191,"url":371},[26,27,257,29,380],"可解释性",{"code":47,"slug":204,"name":205,"path":206,"parent_code":207},[383],{"code":39,"slug":384,"name":385,"path":386,"parent_code":207},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":131,"locked":14,"version":34,"method":35,"evidence":388},{"candidates":389,"agricultural_evidence":41},[390,396,400],{"code":47,"score":17,"supported":41,"title":391,"abstract":393,"tags":395},[392],"computer vision",[394],"计算机视觉",[27],{"code":39,"score":20,"supported":41,"title":397,"abstract":398,"tags":399},[],[29,43],[257,29],{"code":62,"score":21,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[26],[405,295],"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可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":407,"openalex_id":409,"authors":410,"venue":191,"cited_by_count":91,"oa_url":371,"card":413,"direction":97,"ingested_from":99},"W7223114002",[411],{"name":412,"orcid":9},"Kadiatou Barry",{"tldr":414,"method":415,"finding":416,"direction":97,"opportunity":417},"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":420,"title":421,"url":422,"summary":423,"summary_zh":424,"content":9,"source_name":425,"source_url":422,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":426,"score_detail":427,"sources":429,"tags":431,"primary_topic":434,"secondary_topics":438,"classification":444,"search_phrases":498,"slug":501,"view_count":91,"doi":502,"paper":503,"created_at":527},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",79,{"impact":114,"substance":115,"depth":114,"authority":17,"freshness":117,"relevant":21,"comment":428},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[430],{"name":425,"url":422},[26,27,29,432,433],"作物水分胁迫","精准灌溉",{"code":145,"slug":435,"name":436,"path":437,"parent_code":128},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[439,440],{"code":47,"slug":204,"name":205,"path":206,"parent_code":207},{"code":334,"slug":441,"name":442,"path":443,"parent_code":207},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":131,"locked":14,"version":34,"method":35,"evidence":445},{"candidates":446,"agricultural_evidence":41},[447,452,458,465,471,475,480,485,492],{"code":145,"score":40,"supported":41,"title":448,"abstract":449,"tags":451},[435],[450,435],"灌溉",[433],{"code":47,"score":13,"supported":41,"title":453,"abstract":454,"tags":457},[],[455,456,330],"机器学习","machine learning",[27],{"code":334,"score":117,"supported":41,"title":459,"abstract":460,"tags":464},[],[461,462,463],"物联网","internet of things","IoT",[],{"code":62,"score":140,"supported":41,"title":466,"abstract":467,"tags":470},[],[468,469],"数字孪生","digital twin",[26],{"code":39,"score":140,"supported":41,"title":472,"abstract":473,"tags":474},[],[29,43],[29],{"code":269,"score":102,"supported":41,"title":476,"abstract":477,"tags":479},[],[272,478],"wheat",[],{"code":275,"score":102,"supported":41,"title":481,"abstract":482,"tags":484},[],[278,483],"maize",[],{"code":486,"score":102,"supported":41,"title":487,"abstract":488,"tags":491},"A5",[],[489,490],"马铃薯","potato",[],{"code":493,"score":56,"supported":41,"title":494,"abstract":495,"tags":497},"B2",[],[496],"葡萄",[],[499,500],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":502,"openalex_id":504,"authors":505,"venue":425,"cited_by_count":91,"oa_url":422,"card":521,"direction":526,"ingested_from":99},"W7223088921",[506,509,512,515,518],{"name":507,"orcid":508},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":510,"orcid":511},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":513,"orcid":514},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":516,"orcid":517},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":519,"orcid":520},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":522,"method":523,"finding":524,"direction":97,"opportunity":525},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z"]