[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5382":3,"related-5382":116},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":81,"slug":84,"view_count":70,"doi":85,"paper":86,"created_at":115},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。",null,"Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z","论文",10,true,93,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},26,24,19,15,9,1,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","精准农业","遥感","全球评估",{"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",[],{"status":40,"locked":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46,53,60,68,74],{"code":47,"score":48,"supported":14,"title":49,"abstract":50,"tags":52},"I1",11,[],[27,29,51],"precision agriculture",[27,29],{"code":33,"score":54,"supported":14,"title":55,"abstract":56,"tags":59},7,[],[57,58],"机器学习","machine learning",[28],{"code":61,"score":62,"supported":14,"title":63,"abstract":64,"tags":67},"H3",6,[],[65,66],"灌溉","irrigation",[],{"code":69,"score":70,"supported":14,"title":71,"abstract":72,"tags":73},"I3",4,[],[30],[30],{"code":75,"score":76,"supported":14,"title":77,"abstract":78,"tags":80},"F4",3,[],[79],"除草",[],[82,83],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":107,"oa_url":6,"card":108,"direction":112,"ingested_from":114},"W7222974188",[89,92,95,98,101,104],{"name":90,"orcid":91},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":93,"orcid":94},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":96,"orcid":97},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":99,"orcid":100},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":102,"orcid":103},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":105,"orcid":106},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",0,{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","智慧农业 \u002F 农业物联网","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","openalex","2026-10-20T23:30:08.754594Z",{"total":62,"page":22,"page_size":62,"items":117},[118,227,325,409,470,519],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":126,"score_detail":127,"sources":134,"tags":136,"primary_topic":138,"secondary_topics":144,"classification":152,"search_phrases":197,"slug":200,"view_count":70,"doi":201,"paper":202,"created_at":226},3918,"UAV-based RGB imagery and supervised–unsupervised machine learning for estimating sorghum biomass and grain yield under water and nitrogen stress","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1928610","Precision agriculture emerged as a key strategy for rational management of water and nutrients amid growing food demand driven by global population expansion. Therefore, timely monitoring of field crops is crucial in maintaining adequate nutrient and water levels in the crop fields. Vegetation indices are increasingly being studied for their use in plant nutrient management and in guiding fertilizer recommendations. However, these indices were not adequately evaluated for nutrient management of crops grown with different water and nitrogen application levels. This study investigated the potential application of RGB imagery from low-cost Unmanned Aerial Vehicles (UAVs), combined with machine learning techniques, for quantifying water and nutrient stress in sorghum [ Sorghum bicolor (L.) Moench] crop performance. The field experimental setup at Prairie View A&M University, located in Southeast Texas, applied four irrigation levels (0, 75, 100, and 125% of irrigation; the 100% irrigation level is a fully irrigated treatment with no water stress) and four nitrogen application rates (0, 90, 180, and 360 kg N ha −1 ). The RGB images were captured before harvesting the grain sorghum. Image segmentation for filtering sorghum crops from soil and weeds was performed using K-means clustering and a Convolutional Neural Network (CNN). The vegetation indices, including the Green-Red Vegetation Index (GRVI), Green Leaf Index (GLI), and Excess Green Index (ExG), derived from the segmented images were used to predict sorghum grain and biomass yields using multiple linear regression, Partial least squares regression, Ridge regression, and XGBoost. The results indicated that no single model consistently outperformed the others across both segmentation approaches and response variables, suggesting that the predictive information in the RGB indices is meaningful with selected ML approaches. For example, biomass prediction using ridge regression yielded a Pearson correlation coefficient (r) of 0.65 and an RMSE of 0.72 for K-means clustering-derived indices. The models exhibited moderate, stage-specific predictive performance, with no single regression method consistently outperforming the others across both segmentation approaches and response variables. Because imagery was acquired only once during the late reproductive period, the RGB indices primarily reflected canopy condition, maturity, and senescence at that time. The findings demonstrate the feasibility of a low-cost, single-date UAV RGB workflow for late-season sorghum assessment, while emphasizing the need for multi-temporal imagery and independent validation before application to season-long stress diagnosis or operational yield forecasting.","精准农业已成为在水资源与养分合理管理中的关键策略，以应对全球人口增长所驱动的日益增长的粮食需求。因此，及时监测田间作物对于维持作物田块中适宜的养分和水分水平至关重要。植被指数在植物养分管理和指导施肥推荐中的应用正日益受到研究。然而，这些指数尚未在不同水分和氮素施用水平下对作物养分管理进行充分评估。本研究探讨了来自低成本无人机（UAV）的RGB影像结合机器学习技术在量化高粱[Sorghum bicolor (L.) Moench]作物表现中水分和养分胁迫方面的潜在应用。位于德克萨斯州东南部的Prairie View A&M大学的田间试验设置了四个灌溉水平（灌溉量的0%、75%、100%和125%；100%灌溉水平为充分灌溉处理，无水分胁迫）和四个氮素施用量（0、90、180和360 kg N ha⁻¹）。RGB图像在收获籽粒高粱前采集。使用K-means聚类和卷积神经网络（CNN）进行图像分割，以将高粱作物与土壤和杂草分离。从分割图像中提取的植被指数，包括绿红植被指数（GRVI）、绿叶指数（GLI）和过量绿指数（ExG），被用于通过多元线性回归、偏最小二乘回归、岭回归和XGBoost预测高粱籽粒和生物量产量。结果表明，在两种分割方法和响应变量下，没有单一模型始终优于其他模型，这表明RGB指数中的预测信息在所选机器学习方法下具有意义。例如，使用岭回归进行生物量预测时，基于K-means聚类所得指数的Pearson相关系数（r）为0.65，RMSE为0.72。这些模型表现出中等、特定阶段的预测性能，在两种分割方法和响应变量下，没有单一回归方法始终优于其他方法。由于影像仅在生殖后期采集一次，RGB指数主要反映当时的冠层状况、成熟度和衰老程度。研究结果证明了低成本、","Frontiers in Environmental Science","2026-09-30T00:00:00Z",69,{"impact":128,"substance":129,"depth":130,"authority":131,"freshness":132,"relevant":22,"comment":133},12,20,16,13,8,"低成本无人机RGB影像结合机器学习估算高粱生物量与产量的方法学研究，结论审慎，对精准农业养分水分管理有参考价值，但属单点单期试验，影响范围有限。",[135],{"name":124,"url":121},[27,28,29,30,137],"高粱",{"code":139,"slug":140,"name":141,"path":142,"parent_code":143},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[145,146],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":147,"slug":148,"name":149,"path":150,"parent_code":151},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":40,"locked":41,"version":42,"method":43,"evidence":153},{"candidates":154,"agricultural_evidence":14},[155,160,164,171,176,180,187,193],{"code":139,"score":20,"supported":14,"title":156,"abstract":158,"tags":159},[157],"sorghum",[137,157],[137],{"code":33,"score":20,"supported":14,"title":161,"abstract":162,"tags":163},[58],[57,58],[28],{"code":147,"score":21,"supported":14,"title":165,"abstract":166,"tags":170},[],[167,168,169],"施肥","养分管理","fertilizer",[],{"code":61,"score":21,"supported":14,"title":172,"abstract":173,"tags":175},[],[65,174,66],"水资源",[],{"code":47,"score":132,"supported":14,"title":177,"abstract":178,"tags":179},[],[29,51],[27,29],{"code":181,"score":62,"supported":14,"title":182,"abstract":183,"tags":186},"H2",[],[184,185],"土壤","soil",[],{"code":188,"score":76,"supported":14,"title":189,"abstract":190,"tags":192},"A7",[],[191],"间作",[],{"code":69,"score":22,"supported":41,"title":194,"abstract":195,"tags":196},[],[],[30],[198,199],"Prairie View A&M University 高粱 无人机","高粱 生物量 产量 无人机 RGB","PrairieViewAMUniversity高粱无人机-3918","10.3389\u002Ffenvs.2026.1928610",{"doi":201,"openalex_id":203,"authors":204,"venue":124,"cited_by_count":107,"oa_url":121,"card":219,"direction":225,"ingested_from":114},"W7214994063",[205,207,210,212,215,217],{"name":206,"orcid":9},"Anoop Valiya Veettil",{"name":208,"orcid":209},"Ripendra Awal","https:\u002F\u002Forcid.org\u002F0000-0002-2453-2592",{"name":211,"orcid":9},"Ali Fares",{"name":213,"orcid":214},"Binita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-7093-4915",{"name":216,"orcid":9},"Almoutaz Elhassan",{"name":218,"orcid":9},"Atikur Rahman",{"tldr":220,"method":221,"finding":222,"direction":223,"opportunity":224},"用无人机RGB影像与机器学习估算水分氮胁迫下高粱生物量和产量。","低成本无人机RGB影像，K-means与CNN分割，多种回归模型预测。","单一模型未持续最优，RGB指数在特定时期具中等预测能力。","农业遥感与作物表型","可研究多时相RGB影像与深度学习融合，提升胁迫下作物产量预测精度。","农业人工智能与决策模型","2026-10-01T23:30:54.379150Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":232,"content":9,"source_name":233,"source_url":230,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":235,"score_detail":236,"sources":239,"tags":241,"primary_topic":243,"secondary_topics":247,"classification":253,"search_phrases":288,"slug":291,"view_count":62,"doi":292,"paper":293,"created_at":324},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal","2026-09-18T00:00:00Z",67,{"impact":128,"substance":237,"depth":130,"authority":131,"freshness":132,"relevant":22,"comment":238},18,"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[240],{"name":233,"url":230},[27,28,29,30,242],"土壤碳汇",{"code":181,"slug":185,"name":244,"path":245,"parent_code":246},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[248,249],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":69,"slug":250,"name":251,"path":252,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":41,"version":42,"method":43,"evidence":254},{"candidates":255,"agricultural_evidence":14},[256,261,266,272,276,282],{"code":181,"score":257,"supported":14,"title":258,"abstract":259,"tags":260},14,[185],[184,185],[],{"code":33,"score":13,"supported":14,"title":262,"abstract":263,"tags":265},[],[57,58,264],"artificial intelligence",[28],{"code":69,"score":13,"supported":14,"title":267,"abstract":268,"tags":271},[],[30,269,270],"remote sensing","hyperspectral",[30],{"code":47,"score":132,"supported":14,"title":273,"abstract":274,"tags":275},[],[29,51],[27,29],{"code":277,"score":70,"supported":14,"title":278,"abstract":279,"tags":281},"H4",[],[280],"carbon sequestration",[242],{"code":283,"score":76,"supported":14,"title":284,"abstract":285,"tags":287},"H7",[],[286],"干旱",[],[289,290],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":292,"openalex_id":294,"authors":295,"venue":233,"cited_by_count":107,"oa_url":230,"card":319,"direction":223,"ingested_from":114},"W7213561504",[296,299,302,305,308,311,313,315,317],{"name":297,"orcid":298},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":300,"orcid":301},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":303,"orcid":304},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":306,"orcid":307},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":309,"orcid":310},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":312,"orcid":9},"Almaha Jamal",{"name":314,"orcid":9},"Afra Rashed",{"name":316,"orcid":9},"Hamda Yousif",{"name":318,"orcid":9},"Sarah Sadeq",{"tldr":320,"method":321,"finding":322,"direction":223,"opportunity":323},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":330,"content":9,"source_name":331,"source_url":328,"published_at":332,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":333,"score_detail":334,"sources":338,"tags":340,"primary_topic":342,"secondary_topics":346,"classification":348,"search_phrases":369,"slug":372,"view_count":22,"doi":373,"paper":374,"created_at":408},2512,"Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1698781","Accurate estimation of crop area using classification techniques applied to drone imagery plays an important role in precision agriculture. Traditional machine learning (ML) approaches have been widely used for agricultural image classification; however, advanced deep learning (DL) models generally provide superior feature extraction and classification capability for complex crop patterns. Differentiating various rice establishment methods is essential for precise area estimation. In this study, advanced deep learning models were employed to classify three types of rice cultivation: (i) Broadcasted Direct Seeded Rice (DSR), (ii) Wet Direct Seeded Rice (Wet DSR), and (iii) Transplanted Rice (TR) using drone imagery. The drone imagery was collected from an experimental field at Praanadhaara Organised Agro Forestry Private Limited, Bapatla District, Andhra Pradesh, India. The images were captured in the visible spectrum (Red, Green, and Blue bands) on three dates, viz., 15 October 2023, 27 October 2023, and 01 December 2023, from an altitude of 40 m and were used to train and test classification models. Classification was performed using six ResNet-50 based hybrid models, namely, ResNet-50+K-Nearest Neighbor (ResNet-50+KNN), ResNet-50+Support Vector Machine (ResNet-50+SVM), ResNet-50+Decision Trees (ResNet-50+DT), ResNet-50+Random Forest (ResNet-50+RF), ResNet-50+Naïve Bayes (ResNet-50+NB), and ResNet-50+Neural Network (ResNet-50+NN), along with two additional DL architectures, namely, You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8). Model performance was evaluated using overall accuracy (OA), precision (P), recall (R), kappa coefficient (K), F1-score (F1), and mean Average Precision (mAP). Among the tested classifiers, the ResNet-50+NN model consistently achieved the highest average overall accuracies of 93.16%, 95.81%, and 93.31% at T 1 , T 2 , and T 3 , respectively, outperforming all other models, whose accuracies ranged from 78.05% to 92.07%, 86.11%–95.24%, and 77.47%–92.09% across the respective time intervals. The ResNet-50+NN model also recorded the highest precision (0.93–0.97), recall (0.92–0.97), F1-score (0.92–0.97), and kappa coefficient (0.89–0.96), demonstrating superior and consistent classification performance across all observation dates. The methodology developed in this work enables precise identification of rice establishment methods, improving crop mapping and monitoring. This identification enhances resource efficiency, optimizes input use, and supports site-specific management, contributing to sustainable precision agriculture.","利用分类技术对无人机影像进行作物面积精确估算，在精准农业中发挥着重要作用。传统机器学习（ML）方法已广泛用于农业图像分类；然而，先进的深度学习（DL）模型通常对复杂作物模式具有更优越的特征提取和分类能力。区分不同的水稻种植方式对于精确估算面积至关重要。本研究采用先进的深度学习模型，利用无人机影像对三种水稻种植类型进行分类：（i）撒播直播稻（DSR），（ii）湿润直播稻（Wet DSR），以及（iii）移栽稻（TR）。无人机影像采集自印度安得拉邦巴帕特拉县Praanadhaara Organised Agro Forestry Private Limited的试验田。图像在可见光谱（红、绿、蓝波段）下于三个日期拍摄，即2023年10月15日、2023年10月27日和2023年12月1日，飞行高度为40 m，用于训练和测试分类模型。分类采用六种基于ResNet-50的混合模型，即ResNet-50+K近邻（ResNet-50+KNN）、ResNet-50+支持向量机（ResNet-50+SVM）、ResNet-50+决策树（ResNet-50+DT）、ResNet-50+随机森林（ResNet-50+RF）、ResNet-50+朴素贝叶斯（ResNet-50+NB）和ResNet-50+神经网络（ResNet-50+NN），以及两种额外的深度学习架构，即You Only Look Once第5版（YOLOv5）和You Only Look Once第8版（YOLOv8）。采用总体精度（OA）、精确率（P）、召回率（R）、Kappa系数（K）、F1分数（F1）和平均精度均值（mAP）评估模型性能。在测试的分类器中，ResNet-50+NN模型在T₁、T₂和T₃分别持续取得最高的平均总体精度，为93.16%、95.81%和93.31%，优于所有其他模型，后者的精度在相应时间段分别为78.05%–92.07%、86.11%–95.24%和77.47%–92.09%。ResNet-50+NN模型还记录了最高的精确率（0.93–0.97）、召回率（0.92–0.97）、F1分数（0.92–0.97）和Kappa系数（0.89–0.96），在所有观测日期均表现出优越且稳定的分类性能。本研究开发的方法能够精确识别水稻种植方式，改进作物制图和监测。这种识别增强了资源","Frontiers in Remote Sensing","2026-09-14T00:00:00Z",76,{"impact":130,"substance":335,"depth":336,"authority":131,"freshness":21,"relevant":22,"comment":337},21,17,"基于无人机可见光影像与ResNet-50混合模型区分三类水稻种植方式，方法对比系统、精度数据扎实，对精准农业作物制图有参考价值，但属细分技术进展，影响范围有限。",[339],{"name":331,"url":328},[27,28,341,29,30],"水稻",{"code":343,"slug":344,"name":341,"path":345,"parent_code":143},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[347],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":349},{"candidates":350,"agricultural_evidence":14},[351,357,361,365],{"code":33,"score":237,"supported":14,"title":352,"abstract":354,"tags":356},[353],"deep learning",[355,57,58,353],"深度学习",[28],{"code":47,"score":130,"supported":14,"title":358,"abstract":359,"tags":360},[51],[29,51],[27,29],{"code":343,"score":20,"supported":14,"title":362,"abstract":363,"tags":364},[344],[341,344],[341],{"code":69,"score":22,"supported":41,"title":366,"abstract":367,"tags":368},[],[],[30],[370,371],"农业人工智能 智慧农业 精准农业 水稻","农业人工智能 智慧农业","农业人工智能智慧农业精准农业水稻-2512","10.3389\u002Ffrsen.2026.1698781",{"doi":373,"openalex_id":375,"authors":376,"venue":331,"cited_by_count":107,"oa_url":402,"card":403,"direction":112,"ingested_from":114},"W7212834743",[377,379,381,383,385,388,390,392,394,397,399],{"name":378,"orcid":9},"Amrutha Lakshmi Gubbala",{"name":380,"orcid":9},"Santosha Rathod",{"name":382,"orcid":9},"Ramesh Dasyam",{"name":384,"orcid":9},"Mahender Kumar Rapolu",{"name":386,"orcid":387},"Arun Kumar Dasari","https:\u002F\u002Forcid.org\u002F0000-0001-7398-8601",{"name":389,"orcid":9},"Pundarikakshudu Kurra",{"name":391,"orcid":9},"Hanuma Raviteja Madireddy",{"name":393,"orcid":9},"Prajwal R. Shashishekhar",{"name":395,"orcid":396},"Ravi V. Mural","https:\u002F\u002Forcid.org\u002F0000-0002-5489-9918",{"name":398,"orcid":9},"Anil Kumar",{"name":400,"orcid":401},"R. M. Sundaram","https:\u002F\u002Forcid.org\u002F0000-0002-9857-8251","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1698781\u002Fpdf",{"tldr":404,"method":405,"finding":406,"direction":223,"opportunity":407},"用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。","无人机RGB影像三个时相，比较ResNet-50混合模型与YOLOv5\u002Fv8分类","ResNet-50+NN表现最优，三期总体精度达93.16%、95.81%、93.31%，优于其他模","可扩展到多光谱\u002F多时相与更大区域验证，并用于种植方式制图与面积精准估算。","2026-09-15T23:30:08.600535Z",{"id":410,"title":411,"url":412,"summary":413,"summary_zh":414,"content":9,"source_name":415,"source_url":412,"published_at":416,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":417,"score_detail":418,"sources":421,"tags":423,"primary_topic":426,"secondary_topics":427,"classification":428,"search_phrases":454,"slug":457,"view_count":107,"doi":458,"paper":459,"created_at":469},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":128,"substance":419,"depth":237,"authority":128,"freshness":13,"relevant":22,"comment":420},22,"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。",[422],{"name":415,"url":412},[27,28,424,30,425],"粮食安全","气候分析",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":429},{"candidates":430,"agricultural_evidence":14},[431,435,441,446,450],{"code":33,"score":21,"supported":14,"title":432,"abstract":433,"tags":434},[264],[],[28],{"code":436,"score":76,"supported":14,"title":437,"abstract":438,"tags":440},"I5",[],[439],"农业传感",[],{"code":442,"score":22,"supported":41,"title":443,"abstract":444,"tags":445},"A8",[],[],[424],{"code":47,"score":22,"supported":41,"title":447,"abstract":448,"tags":449},[],[],[27],{"code":69,"score":22,"supported":41,"title":451,"abstract":452,"tags":453},[],[],[30],[455,456],"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":458,"openalex_id":460,"authors":461,"venue":415,"cited_by_count":107,"oa_url":412,"card":464,"direction":225,"ingested_from":114},"W7223111766",[462],{"name":463,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":465,"method":466,"finding":467,"direction":223,"opportunity":468},"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":471,"title":472,"url":473,"summary":474,"summary_zh":475,"content":9,"source_name":415,"source_url":473,"published_at":416,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":235,"score_detail":476,"sources":478,"tags":480,"primary_topic":483,"secondary_topics":484,"classification":486,"search_phrases":503,"slug":506,"view_count":107,"doi":507,"paper":508,"created_at":518},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":132,"substance":129,"depth":336,"authority":128,"freshness":13,"relevant":22,"comment":477},"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。",[479],{"name":415,"url":473},[27,28,481,30,482],"产量预测","可解释性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[485],{"code":69,"slug":250,"name":251,"path":252,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":487},{"candidates":488,"agricultural_evidence":14},[489,495,499],{"code":33,"score":128,"supported":14,"title":490,"abstract":492,"tags":494},[491],"computer vision",[493],"计算机视觉",[28],{"code":69,"score":132,"supported":14,"title":496,"abstract":497,"tags":498},[],[30,269],[481,30],{"code":47,"score":22,"supported":41,"title":500,"abstract":501,"tags":502},[],[],[27],[504,505],"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":507,"openalex_id":509,"authors":510,"venue":415,"cited_by_count":107,"oa_url":473,"card":513,"direction":223,"ingested_from":114},"W7223114002",[511],{"name":512,"orcid":9},"Kadiatou Barry",{"tldr":514,"method":515,"finding":516,"direction":223,"opportunity":517},"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":520,"title":521,"url":522,"summary":523,"summary_zh":524,"content":9,"source_name":525,"source_url":522,"published_at":416,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":526,"score_detail":527,"sources":529,"tags":531,"primary_topic":534,"secondary_topics":537,"classification":543,"search_phrases":597,"slug":600,"view_count":107,"doi":601,"paper":602,"created_at":625},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":237,"substance":419,"depth":237,"authority":128,"freshness":21,"relevant":22,"comment":528},"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。",[530],{"name":525,"url":522},[27,28,30,532,533],"作物水分胁迫","精准灌溉",{"code":61,"slug":66,"name":535,"path":536,"parent_code":246},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[538,539],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":436,"slug":540,"name":541,"path":542,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":41,"version":42,"method":43,"evidence":544},{"candidates":545,"agricultural_evidence":14},[546,550,554,561,567,571,578,585,591],{"code":61,"score":20,"supported":14,"title":547,"abstract":548,"tags":549},[66],[65,66],[533],{"code":33,"score":13,"supported":14,"title":551,"abstract":552,"tags":553},[],[57,58,264],[28],{"code":436,"score":21,"supported":14,"title":555,"abstract":556,"tags":560},[],[557,558,559],"物联网","internet of things","IoT",[],{"code":47,"score":54,"supported":14,"title":562,"abstract":563,"tags":566},[],[564,565],"数字孪生","digital twin",[27],{"code":69,"score":54,"supported":14,"title":568,"abstract":569,"tags":570},[],[30,269],[30],{"code":572,"score":62,"supported":14,"title":573,"abstract":574,"tags":577},"A2",[],[575,576],"小麦","wheat",[],{"code":579,"score":62,"supported":14,"title":580,"abstract":581,"tags":584},"A3",[],[582,583],"玉米","maize",[],{"code":139,"score":62,"supported":14,"title":586,"abstract":587,"tags":590},[],[588,589],"马铃薯","potato",[],{"code":592,"score":76,"supported":14,"title":593,"abstract":594,"tags":596},"B2",[],[595],"葡萄",[],[598,599],"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":601,"openalex_id":603,"authors":604,"venue":525,"cited_by_count":107,"oa_url":522,"card":620,"direction":112,"ingested_from":114},"W7223088921",[605,608,611,614,617],{"name":606,"orcid":607},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":609,"orcid":610},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":612,"orcid":613},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":615,"orcid":616},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":618,"orcid":619},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":621,"method":622,"finding":623,"direction":223,"opportunity":624},"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。","2026-10-22T11:20:24.526683Z"]