[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3549":3,"related-3549":103},{"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":82,"slug":85,"view_count":56,"doi":86,"paper":87,"created_at":102},3549,"Artificial Intelligence-Based Innovations for Environmental Management in Uganda: A Scoping Review of Current Innovations","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fjatir.141025","Uganda's extraordinary biodiversity, encompassing the Albertine Rift's endangered primate populations, East Africa's most significant freshwater wetland systems, and montane forest corridors of global conservation significance, is under compound and steadily accelerating environmental stress from widespread deforestation, wetland encroachment, agricultural expansion, climate variability, and persistently inadequate environmental governance capacity.Simultaneously, artificial intelligence technologies are undergoing rapid global deployment across environmental monitoring, predictive modelling, natural resource management, and climate adaptation domains, raising the question of whether these innovations are reaching contexts such as Uganda.This scoping review systematically maps the emerging landscape of artificial intelligence applications in environmental management in Uganda, examining which specific technologies have been deployed or piloted, across which environmental domains, by which actor types, and with what documented outcomes or limitations.A systematic search of peer-reviewed literature, grey literature, policy documents, and institutional reports published between 2020 and 2026 was conducted across five databases, ultimately yielding just 18 studies and reports meeting inclusion criteria after careful screening of 247 initial records identified.Findings reveal that artificial intelligence applications in Ugandan environmental management remain genuinely promising yet geographically and thematically concentrated, with remote sensing and machine learning applications for land cover change detection and forest monitoring constituting the single dominant application cluster identified.Applications in wetland management, water quality monitoring, wildlife protection, and climate adaptation do exist but remain isolated, under-resourced, and poorly integrated into government environmental governance frameworks.Critical cross-cutting barriers, including persistent data infrastructure deficits, digital skills shortages, electricity access constraints, and an underdeveloped AI policy ecosystem, systematically limit the effective translation of global AI innovations into practical, Uganda-specific environmental management solutions.The review concludes by proposing an AI-Environmental Governance Integration Framework","乌干达拥有非凡的生物多样性，涵盖艾伯丁裂谷（Albertine Rift）的濒危灵长类动物种群、东非最重要的淡水湿地系统以及具有全球保护意义的山地森林廊道，然而这些生态系统正面临来自大面积森林砍伐、湿地侵占、农业扩张、气候变率以及长期不足的环境治理能力的复合且持续加速的环境压力。与此同时，人工智能技术正在全球范围内迅速部署于环境监测、预测建模、自然资源管理和气候适应等领域，这引发了一个问题：这些创新是否正在惠及乌干达等地区。本范围综述（scoping review）系统梳理了乌干达环境管理中人工智能应用的新兴图景，考察了哪些具体技术已被部署或试点、涉及哪些环境领域、由哪些类型的行动者实施，以及有哪些已记录的结果或局限。研究对2020年至2026年间发表的同行评审文献、灰色文献、政策文件和机构报告在五个数据库中进行了系统检索，在仔细筛选初步识别的247条记录后，最终仅有18项研究和报告符合纳入标准。研究结果表明，人工智能在乌干达环境管理中的应用确实具有前景，但在地理和主题上高度集中，其中用于土地覆盖变化检测和森林监测的遥感与机器学习应用构成了唯一占主导地位的应用集群。湿地管理、水质监测、野生动物保护和气候适应方面的应用确实存在，但仍然孤立、资源不足，且未能有效纳入政府环境治理框架。关键的跨领域障碍，包括持续存在的数据基础设施赤字、数字技能短缺、电力获取限制以及欠发达的人工智能政策生态系统，系统性地限制了全球人工智能创新向乌干达本土化环境管理解决方案的有效转化。综述最后提出了一个人工智能-环境治理整合框架（AI-Environmental Governance Integration Framework）。",null,"OpenAlex","2026-09-24T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,12,9,1,"系统梳理乌干达AI环境管理应用的综述论文，方法规范、结论可靠，但属区域性研究且与三农信息化关联偏间接，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","环境治理","遥感监测","乌干达",{"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":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,54,62,70,77],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":53},18,true,[49],"artificial intelligence",[51,52,49],"机器学习","machine learning",[28],{"code":55,"score":56,"supported":47,"title":57,"abstract":58,"tags":61},"I3",7,[],[59,60],"遥感","remote sensing",[30],{"code":63,"score":64,"supported":47,"title":65,"abstract":66,"tags":69},"H6",6,[],[67,68],"生物多样性","biodiversity",[],{"code":71,"score":72,"supported":47,"title":73,"abstract":74,"tags":76},"H7",3,[],[75],"气候适应",[],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"I1",[],[],[27],[83,84],"乌干达 人工智能 环境管理","AI 遥感 森林监测","乌干达人工智能环境管理-3549","10.64643\u002Fjatir.141025",{"doi":86,"openalex_id":88,"authors":89,"venue":9,"cited_by_count":92,"oa_url":93,"card":94,"direction":100,"ingested_from":101},"W7214195868",[90],{"name":91,"orcid":9},"Henry Omara",0,"https:\u002F\u002Fjatir.org\u002Fpublishedpapers\u002F141025_PAPER.pdf",{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"综述乌干达环境管理中AI应用现状，发现应用集中于遥感与森林监测，整体零散且受基础设施制约。","系统检索2020-2026年五个数据库的文献与政策报告，筛选出18项研究做范围综","AI应用有前景但地理与主题集中，湿地、水质、野生动物等领域应用孤立，数据与政策短板明显。","农业遥感与作物表型","可研究低成本遥感与机器学习在乌干达湿地、水质及小农农业环境监测中的落地路径与治理整合。","智慧农业 \u002F 农业物联网","openalex","2026-09-26T23:30:15.512560Z",{"total":64,"page":22,"page_size":64,"items":104},[105,189,254,331,395,491],{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":111,"source_url":108,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":118,"tags":120,"primary_topic":123,"secondary_topics":124,"classification":130,"search_phrases":172,"slug":175,"view_count":22,"doi":176,"paper":177,"created_at":188},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",70,{"impact":46,"substance":115,"depth":116,"authority":20,"freshness":21,"relevant":22,"comment":117},16,15,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[119],{"name":111,"url":108},[27,28,121,122,30],"可持续发展","气候变化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[125],{"code":71,"slug":126,"name":127,"path":128,"parent_code":129},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":131},{"candidates":132,"agricultural_evidence":47},[133,139,144,150,154,161,165],{"code":33,"score":46,"supported":47,"title":134,"abstract":135,"tags":138},[49],[136,51,52,137,49],"深度学习","deep learning",[28],{"code":71,"score":116,"supported":47,"title":140,"abstract":142,"tags":143},[141],"climate change",[122,141],[122],{"code":78,"score":56,"supported":47,"title":145,"abstract":146,"tags":149},[],[147,148],"精准农业","precision agriculture",[27],{"code":55,"score":56,"supported":47,"title":151,"abstract":152,"tags":153},[],[59,60],[30],{"code":155,"score":64,"supported":47,"title":156,"abstract":157,"tags":160},"H4",[],[158,159],"碳排放","carbon emission",[],{"code":63,"score":64,"supported":47,"title":162,"abstract":163,"tags":164},[],[67,68],[],{"code":166,"score":64,"supported":47,"title":167,"abstract":168,"tags":171},"I5",[],[169,170],"物联网","internet of things",[],[173,174],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":176,"openalex_id":178,"authors":179,"venue":111,"cited_by_count":92,"oa_url":108,"card":182,"direction":100,"ingested_from":101},"W7222092855",[180],{"name":181,"orcid":9},"Deepmala Suryavanshi",{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":197,"score_detail":198,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":210,"classification":212,"search_phrases":236,"slug":239,"view_count":72,"doi":240,"paper":241,"created_at":253},4809,"Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02299-3","Abstract Precision agriculture is one of the major breakthroughs which has been adopted to counteract some of the major challenges facing contemporary agriculture, such as maximizing crop yield, maximizing resource efficiency, and sustainability. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies can go a long way in enhancing crop yield prediction through data obtained from multiple sources, including satellite images, ground sensors, and weather stations. This research paper proposes a next-generation crop yield prediction system called Geo-Optimized Deep Learning (GODL) algorithm. The GODL algorithm integrates geospatial information with deep learning algorithms for crop yield analysis and prediction. This combination of deep learning and geospatial data allows accurate and scalable crop yield predictions using geospatial and sensor information. In order to ensure model optimization in terms of accuracy and efficiency, the GODL Algorithm makes use of advanced hyperparameter optimization methods that include Adam and Stochastic Gradient Descent (SGD). The research paper highlights the efficiency of the GODL Algorithm by conducting extensive experiments that compare the algorithm’s performance with other crop yield prediction models like Recurrent Neural Network (RNN) and other machine learning techniques. The evaluation takes into consideration important metrics such as accuracy, Jaccard Index, Fowlkes-Mallows Index (FMI), and G-mean, as well as IoT data delay and its effects on the prediction accuracy. In future research, the use of blockchain technology to facilitate data sharing and edge computing to process the data will be considered.","摘要 精准农业是为应对当代农业面临的若干重大挑战而采用的主要突破之一，这些挑战包括最大化作物产量、最大化资源效率以及可持续性。人工智能（AI）与物联网（IoT）技术的应用，能够通过从卫星图像、地面传感器和气象站等多源获取的数据，大幅提升作物产量预测能力。本文提出了一种下一代作物产量预测系统，称为地理优化深度学习（GODL）算法。GODL算法将地理空间信息与深度学习算法相结合，用于作物产量分析与预测。深度学习与地理空间数据的这种结合，使得利用地理空间和传感器信息进行准确且可扩展的作物产量预测成为可能。为确保模型在准确性和效率方面实现优化，GODL算法采用了先进的超参数优化方法，包括Adam和随机梯度下降（SGD）。本文通过开展大量实验，将该算法的性能与其他作物产量预测模型（如循环神经网络（RNN）及其他机器学习技术）进行比较，从而突出GODL算法的效率。评估考虑了准确率、Jaccard指数、Fowlkes-Mallows指数（FMI）和G均值等重要指标，以及物联网数据延迟及其对预测精度的影响。在未来的研究中，将考虑使用区块链技术促进数据共享，并利用边缘计算处理数据。","Peer-to-Peer Networking and Applications","2026-10-12T00:00:00Z",68,{"impact":20,"substance":46,"depth":115,"authority":199,"freshness":21,"relevant":22,"comment":200},13,"提出GODL地理优化深度学习算法融合AI与IoT进行作物估产，方法有创新且对比实验充分，但属学术论文，产业落地影响有限。",[202],{"name":195,"url":192},[27,28,204,30,205],"农业物联网","作物估产",{"code":166,"slug":207,"name":208,"path":209,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[211],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":213},{"candidates":214,"agricultural_evidence":47},[215,222,227,231],{"code":166,"score":46,"supported":47,"title":216,"abstract":218,"tags":221},[217],"IoT",[169,219,170,217,220],"边缘计算","edge computing",[204],{"code":78,"score":116,"supported":47,"title":223,"abstract":225,"tags":226},[224],"smart agriculture",[147,148],[27],{"code":33,"score":13,"supported":47,"title":228,"abstract":229,"tags":230},[],[136,51,52,137,49],[28],{"code":55,"score":232,"supported":14,"title":233,"abstract":234,"tags":235},2,[],[],[205,30],[237,238],"GODL 作物估产 算法","AI IoT 智慧农业 产量预测","GODL作物估产算法-4809","10.1007\u002Fs12083-026-02299-3",{"doi":240,"openalex_id":242,"authors":243,"venue":195,"cited_by_count":92,"oa_url":192,"card":248,"direction":100,"ingested_from":101},"W7221560555",[244,246],{"name":245,"orcid":9},"Keerthana T",{"name":247,"orcid":9},"Janarthanan R",{"tldr":249,"method":250,"finding":251,"direction":100,"opportunity":252},"提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。","GODL算法，融合地理空间数据与深度学习，采用Adam和SGD超参数优化。","GODL在准确率、Jaccard指数、FMI和G-mean上优于RNN等模型，且IoT数据延迟影响预","可探索区块链保障数据共享安全、边缘计算降低IoT延迟，以及多源异构数据融合的轻量化模型。","2026-10-13T23:30:10.304344Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":270,"secondary_topics":276,"classification":282,"search_phrases":305,"slug":308,"view_count":22,"doi":309,"paper":310,"created_at":330},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi",77,{"impact":115,"substance":263,"depth":46,"authority":199,"freshness":21,"relevant":22,"comment":264},21,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[266],{"name":260,"url":257},[27,268,28,30,269],"无人机","病害预警",{"code":271,"slug":272,"name":273,"path":274,"parent_code":275},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[277,278],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":55,"slug":279,"name":280,"path":281,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":283},{"candidates":284,"agricultural_evidence":47},[285,292,296,301],{"code":271,"score":19,"supported":47,"title":286,"abstract":288,"tags":291},[287],"apple",[289,290,287],"苹果","葡萄",[],{"code":33,"score":116,"supported":47,"title":293,"abstract":294,"tags":295},[137],[136,137],[28],{"code":55,"score":20,"supported":47,"title":297,"abstract":299,"tags":300},[298],"multispectral",[298],[30],{"code":78,"score":56,"supported":47,"title":302,"abstract":303,"tags":304},[],[147,148],[27],[306,307],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":309,"openalex_id":311,"authors":312,"venue":260,"cited_by_count":92,"oa_url":324,"card":325,"direction":100,"ingested_from":101},"W7221558726",[313,316,319,321],{"name":314,"orcid":315},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":317,"orcid":318},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":320,"orcid":9},"Fatih Sinan Bir",{"name":322,"orcid":323},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":326,"method":327,"finding":328,"direction":98,"opportunity":329},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":336,"content":9,"source_name":337,"source_url":334,"published_at":338,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":339,"score_detail":340,"sources":344,"tags":346,"primary_topic":349,"secondary_topics":354,"classification":356,"search_phrases":376,"slug":379,"view_count":232,"doi":380,"paper":381,"created_at":394},4431,"A multimodal deep learning for rice crop health monitoring using vegetation index feature selection","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74895-5","Crop health monitoring is essential for sustaining agricultural productivity and ensuring stable production, particularly in major crop-producing regions that face challenges due to climate variability, resource constraints, and spatial heterogeneity. Traditional methods struggle to effectively integrate spatial heterogeneity and environmental variability from multi-source remote sensing (RS) data. To address this problem, the proposed work explores the role of satellite imagery in monitoring rice crop health with the help of Vegetation Indices (VIs) under the impact of changing weather conditions. Multi-temporal Sentinel-1 and Sentinel-2 satellite data were acquired from a rice cultivation field in Gorakhpur, Uttar Pradesh, India, during three Kharif growing seasons (2023-2025). In this work, we propose a supervised MI-based feature selection framework employing Otsu’s adaptive thresholding for selection of VIs that strongly determine the health of rice crop. To ensure data integrity, cloud-contaminated observations were systematically excluded through a two-stage cleaning process. Further, we propose a multimodal deep learning (DL) framework that fuses a Swin Transformer (Swin-T) backbone for spatial feature extraction from vegetation index imagery with a Multi-Layer Perceptron (MLP) for structured numerical modeling of meteorological variables. The proposed framework achieved 94.05% accuracy on an independent held-out test set and a mean accuracy of \\(92.12 \\pm 5.28\\) % under 10-fold cross-validation, consistently outperforming competing hybrid deep learning baselines. These results highlight that integrating transformer-based spatial representations with environmental information provides an efficient approach for rice crop health classification. The proposed framework can be extended to multi-region crop monitoring by incorporating additional vegetation index modalities and diverse climatic conditions.","作物健康监测对于维持农业生产力、保障稳定生产至关重要，尤其是在面临气候变异性、资源约束和空间异质性挑战的主要作物产区。传统方法难以有效整合多源遥感（RS）数据中的空间异质性和环境变异性。为解决这一问题，本研究探索了卫星影像在天气条件变化影响下借助植被指数（VIs）监测水稻作物健康的作用。研究在印度北方邦戈勒克布尔的稻田中，于三个水稻生长季（2023—2025年）获取了多时相Sentinel-1和Sentinel-2卫星数据。在本研究中，我们提出了一种基于互信息（MI）的监督式特征选择框架，采用Otsu自适应阈值法筛选对水稻作物健康具有强判别能力的植被指数。为确保数据完整性，通过两阶段清洗流程系统性地剔除了受云污染的观测数据。此外，我们提出了一种多模态深度学习（DL）框架，该框架融合了Swin Transformer（Swin-T）主干网络用于从植被指数影像中提取空间特征，以及多层感知机（MLP）用于气象变量的结构化数值建模。所提框架在独立留出测试集上达到了94.05%的准确率，在10折交叉验证下平均准确率为\\(92.12 \\pm 5.28\\)%，持续优于竞争的混合深度学习基线模型。这些结果表明，将基于Transformer的空间表征与环境信息相结合，为水稻作物健康分类提供了一种高效方法。所提框架可通过纳入更多植被指数模态和多样化气候条件，扩展至多区域作物监测。","Scientific Reports","2026-10-07T00:00:00Z",81,{"impact":46,"substance":341,"depth":46,"authority":342,"freshness":21,"relevant":22,"comment":343},22,14,"方法新颖、数据跨三个生长季且精度可靠，属农业遥感与AI交叉的细分领域进展，值得进入每日精选。",[345],{"name":337,"url":334},[27,28,347,30,348],"水稻","作物长势",{"code":350,"slug":351,"name":347,"path":352,"parent_code":353},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[355],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":357},{"candidates":358,"agricultural_evidence":47},[359,364,368,372],{"code":350,"score":46,"supported":47,"title":360,"abstract":361,"tags":363},[351],[347,362,351],"稻田",[347],{"code":33,"score":116,"supported":47,"title":365,"abstract":366,"tags":367},[137],[136,137],[28],{"code":55,"score":56,"supported":47,"title":369,"abstract":370,"tags":371},[],[59,60],[30],{"code":78,"score":22,"supported":14,"title":373,"abstract":374,"tags":375},[],[],[27],[377,378],"Sentinel-2 水稻 遥感监测","Swin Transformer 作物健康","Sentinel-2水稻遥感监测-4431","10.1038\u002Fs41598-026-74895-5",{"doi":380,"openalex_id":382,"authors":383,"venue":337,"cited_by_count":92,"oa_url":334,"card":389,"direction":98,"ingested_from":101},"W7220838898",[384,387],{"name":385,"orcid":386},"Shashank Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1291-7951",{"name":388,"orcid":9},"Shefali Arora Chouhan",{"tldr":390,"method":391,"finding":392,"direction":98,"opportunity":393},"融合Sentinel-1\u002F2植被指数与气象数据，用多模态深度学习监测水稻健康。","Sentinel-1\u002F2多时相数据、MI特征选择、Otsu阈值、Swin-T+M","独立测试准确率94.05%，10折交叉验证92.12%，优于混合基线。","可扩展至多区域多作物，探索更多植被指数模态与极端气候下的泛化能力。","2026-10-08T23:30:29.254071Z",{"id":396,"title":397,"url":398,"summary":399,"summary_zh":400,"content":9,"source_name":401,"source_url":398,"published_at":402,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":403,"sources":405,"tags":407,"primary_topic":410,"secondary_topics":416,"classification":418,"search_phrases":465,"slug":468,"view_count":469,"doi":470,"paper":471,"created_at":490},4422,"Artificial intelligence driven framework for enhancing sustainability and resilience in communal cattle insurance in Lupane District Zimbabwe","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44279-026-00786-y","Communal cattle farming in Sub-Saharan Africa continues to face destabilization from non-linear climate variability, yet insurance penetration remains negligible. In Lupane District, Zimbabwe, uptake is exceptionally low at approximately 0.01%, despite recurrent droughts that threaten household livelihoods. This study aimed to develop an AI-driven framework to enhance sustainability and resilience in communal cattle insurance within the district. A positivist research philosophy guided the study, and a quantitative approach was adopted. The Kothari sample size determination formula was applied to derive a statistically representative sample of 219 households from a population of 49,841 communal cattle farmers distributed across four villages in 23 wards. Data were collected through KoboCollect, transmitted to secure servers, and analyzed using JAMOVI version 2.6.44. Descriptive statistics indicated that the majority of farmers were male (62%), with an average age of 47 years and herd sizes ranging from 3 to 25 cattle. Education levels were generally low, with 54% reporting primary schooling and only 12% having secondary or higher qualifications. Income sources were largely subsistence-based, and 71% of respondents relied on cattle as their primary asset. Binary logistic regression revealed that affordability (β = 1.25, p \u003C 0.01) and awareness (β = 0.99, p \u003C 0.01) were the strongest determinants of adoption, while institutional trust (β = 0.74, p \u003C 0.05) emerged as a significant direct predictor of adoption willingness. The study was theoretically anchored in the Technology Acceptance Model (TAM), which explains how perceived usefulness and perceived ease of use influence uptake. Informed by findings from Objectives 1 and 2, the study proposes the Tri-Modular AI Resilience Engine (TMAIE). TMAIE integrates Convolutional Neural Networks (CNN) for biometric cattle identification, Recurrent Neural Networks (RNN) for satellite-derived NDVI index triggers, and Transformer-based Natural Language Processing (NLP) localized in IsiNdebele and ChiShona for inclusive voice-based access. The study findings suggest that adoption is not a linear process but an adaptive evolution requiring both technological and social mechanisms. The proposed framework reflects these relationships through the integration of technological precision and community-based mediation. Overall, the findings provide empirical evidence on the determinants of cattle insurance adoption and inform the development of a conceptual AI-driven framework that may support future climate-resilient livestock insurance innovations, subject to further validation and testing.","撒哈拉以南非洲的社区养牛业持续面临非线性气候变率的冲击，然而保险渗透率仍然微不足道。在津巴布韦卢潘内区，尽管反复发生的干旱威胁着家庭生计，但参保率仍极低，约为0.01%。本研究旨在开发一个人工智能驱动的框架，以提升该区社区养牛保险的可持续性和韧性。本研究以实证主义研究哲学为指导，采用定量研究方法。应用Kothari样本量确定公式，从分布于23个选区4个村庄的49841名社区养牛户总体中，推导出具有统计代表性的219户样本。数据通过KoboCollect采集，传输至安全服务器，并使用JAMOVI 2.6.44版本进行分析。描述性统计表明，大多数农户为男性（62%），平均年龄47岁，牛群规模为3至25头。教育水平普遍较低，54%报告仅接受过小学教育，仅12%拥有中学或更高学历。收入来源大多以自给自足为基础，71%的受访者将牛作为其主要资产。二元逻辑回归显示，可负担性（β = 1.25，p \u003C 0.01）和认知度（β = 0.99，p \u003C 0.01）是参保的最强决定因素，而制度信任（β = 0.74，p \u003C 0.05）则是参保意愿的显著直接预测因素。本研究在理论上以技术接受模型（TAM）为锚点，该模型解释了感知有用性和感知易用性如何影响采纳行为。基于目标1和目标2的研究结果，本研究提出了三模块人工智能韧性引擎（TMAIE）。TMAIE整合了用于牛只生物特征识别的卷积神经网络（CNN）、用于卫星衍生NDVI指数触发的循环神经网络（RNN），以及基于Transformer的、以恩德贝莱语和绍纳语本地化的自然语言处理（NLP），以实现包容性的语音访问。研究结果表明，采纳并非线性过程，而是一种需要技术和社会机制共同作用的自适应演化。所提出的框架通过整合技术精确性与社区中介来反映这些关系。总体而言，研究结果提供了关于养牛保险采纳决定因素的实证证据，并为","Discover Agriculture","2026-10-06T00:00:00Z",{"impact":20,"substance":263,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":404},"以津巴布韦Lupane地区社区养牛保险为对象，提出融合CNN牛只识别、NDVI遥感触发与本地语言NLP的AI框架，方法具体、数据扎实，但属区域性案例研究，对国内农业信息化有借鉴意义而非直接影响。",[406],{"name":401,"url":398},[408,27,28,409,30],"数字乡村","农业保险",{"code":411,"slug":412,"name":413,"path":414,"parent_code":415},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[417],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":419},{"candidates":420,"agricultural_evidence":47},[421,427,431,436,441,447,452,456,460],{"code":411,"score":422,"supported":47,"title":423,"abstract":425,"tags":426},11,[424],"cattle",[424],[],{"code":33,"score":21,"supported":47,"title":428,"abstract":429,"tags":430},[49],[],[28],{"code":415,"score":72,"supported":47,"title":432,"abstract":433,"tags":435},[],[434],"livestock",[],{"code":71,"score":72,"supported":47,"title":437,"abstract":438,"tags":440},[],[439],"干旱",[],{"code":442,"score":72,"supported":47,"title":443,"abstract":444,"tags":446},"I7",[],[445],"研究方法",[],{"code":448,"score":22,"supported":14,"title":449,"abstract":450,"tags":451},"G3",[],[],[409],{"code":78,"score":22,"supported":14,"title":453,"abstract":454,"tags":455},[],[],[27],{"code":55,"score":22,"supported":14,"title":457,"abstract":458,"tags":459},[],[],[30],{"code":461,"score":22,"supported":14,"title":462,"abstract":463,"tags":464},"I6",[],[],[408],[466,467],"津巴布韦 Lupane 牛保险","AI 牲畜保险 框架","津巴布韦Lupane牛保险-4422",5,"10.1007\u002Fs44279-026-00786-y",{"doi":470,"openalex_id":472,"authors":473,"venue":401,"cited_by_count":92,"oa_url":483,"card":484,"direction":100,"ingested_from":101},"W7220655164",[474,477,480],{"name":475,"orcid":476},"Nyasha Nyakuchena","https:\u002F\u002Forcid.org\u002F0009-0005-3219-2275",{"name":478,"orcid":479},"Joseph P. Musara","https:\u002F\u002Forcid.org\u002F0000-0002-2226-3616",{"name":481,"orcid":482},"Emmanuel Zivenge","https:\u002F\u002Forcid.org\u002F0000-0003-4740-8225","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44279-026-00786-y.pdf",{"tldr":485,"method":486,"finding":487,"direction":488,"opportunity":489},"研究提出AI驱动的三模块框架，以提升津巴布韦Lupane地区社区牛保险的可持续性和韧性。","对219户农户进行问卷调查，用二元逻辑回归分析，并设计CNN、RNN和Trans","可负担性和意识是保险采纳的最强决定因素，机构信任也显著影响采纳意愿。","数字乡村与农业信息化","可验证TMAIE框架在类似地区的适用性，并探索AI语音交互对低教育水平农户保险采纳的长期影响。","2026-10-08T23:30:11.018464Z",{"id":492,"title":493,"url":494,"summary":495,"summary_zh":496,"content":9,"source_name":497,"source_url":494,"published_at":498,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":499,"score_detail":500,"sources":502,"tags":504,"primary_topic":506,"secondary_topics":507,"classification":514,"search_phrases":578,"slug":581,"view_count":92,"doi":582,"paper":583,"created_at":602},4302,"BIG DATA IN AGRONOMY: TECHNOLOGIES, APPLICATIONS, BENEFITS AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.4238\u002Fk6hcgw77","Big Data is becoming an important component of modern agronomy by enabling the integration, processing and analysis of large, diverse and continuously generated datasets for improved agricultural decision-making. Agricultural Big Data is generated from multiple sources, including remote sensing satellites, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors, weather stations, farm machinery, field observations and historical databases. The integration of these data sources with Geographic Information Systems (GIS), cloud computing, Artificial Intelligence (AI) and Machine Learning (ML) enables the identification of spatial and temporal patterns that are difficult to detect using conventional approaches. Big Data applications in agronomy include climate and weather prediction, pest and disease management, soil health and nutrient management, irrigation scheduling, crop yield prediction, precision agriculture, crop insurance and weed management. Examples from India and other regions demonstrate the application of satellite imagery, UAV-based sensing, IoT systems, crop-growth models and machine-learning algorithms for field-scale and regional agricultural decision making. Big Data analytics can improve resource-use efficiency, support predictive and real-time monitoring, strengthen agricultural risk assessment and facilitate site-specific management. However, effective implementation is constrained by challenges such as high infrastructure and operational costs, data quality and interoperability issues, data privacy and ownership concerns, limited rural digital infrastructure, inadequate technical skills and increasing requirements for data storage and computing. Therefore, the effective integration of Big Data with agronomic knowledge and decision-support systems can contribute to more precise, efficient and sustainable agricultural production while addressing emerging challenges related to climate variability, resource scarcity and food security.","大数据正成为现代农学的重要组成部分，它能够整合、处理和分析大规模、多样化且持续生成的数据集，从而改进农业决策。农业大数据来源于多种渠道，包括遥感卫星、无人机（UAV）、物联网（IoT）传感器、气象站、农业机械、田间观测和历史数据库。将这些数据源与地理信息系统（GIS）、云计算、人工智能（AI）和机器学习（ML）相结合，能够识别传统方法难以发现的空间和时间模式。大数据在农学中的应用包括气候与天气预测、病虫害管理、土壤健康与养分管理、灌溉调度、作物产量预测、精准农业、作物保险和杂草管理。来自印度及其他地区的案例展示了卫星影像、基于无人机的遥感、物联网系统、作物生长模型和机器学习算法在田块尺度和区域农业决策中的应用。大数据分析可以提高资源利用效率，支持预测性和实时监测，加强农业风险评估，并促进因地制宜的管理。然而，有效实施受到诸多挑战的制约，如高昂的基础设施和运营成本、数据质量和互操作性问题、数据隐私和所有权担忧、农村数字基础设施有限、技术技能不足以及数据存储和计算需求不断增加。因此，将大数据与农学知识和决策支持系统有效整合，有助于实现更精准、高效和可持续的农业生产，同时应对气候变异性、资源稀缺和粮食安全等方面的新兴挑战。","Genetics and Molecular Research","2026-10-05T00:00:00Z",76,{"impact":46,"substance":18,"depth":19,"authority":199,"freshness":17,"relevant":22,"comment":501},"系统综述农业大数据技术、应用与挑战，信息密度高，对智慧农业决策有参考价值，但属综述性论文，非突破性成果。",[503],{"name":497,"url":494},[27,28,505,147,30],"农业大数据",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[508,513],{"code":509,"slug":510,"name":511,"path":512,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":166,"slug":207,"name":208,"path":209,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":515},{"candidates":516,"agricultural_evidence":47},[517,521,528,532,536,540,547,554,561,566,572],{"code":33,"score":13,"supported":47,"title":518,"abstract":519,"tags":520},[],[51,52,49],[28],{"code":509,"score":21,"supported":47,"title":522,"abstract":523,"tags":527},[],[524,525,526],"地理信息","GIS","geographic information",[],{"code":166,"score":21,"supported":47,"title":529,"abstract":530,"tags":531},[],[169,170,217],[],{"code":78,"score":17,"supported":47,"title":533,"abstract":534,"tags":535},[],[147,148],[27,147],{"code":55,"score":56,"supported":47,"title":537,"abstract":538,"tags":539},[],[59,60],[30],{"code":541,"score":64,"supported":47,"title":542,"abstract":543,"tags":546},"A8",[],[544,545],"粮食安全","food security",[],{"code":548,"score":64,"supported":47,"title":549,"abstract":550,"tags":553},"H2",[],[551,552],"土壤","soil",[],{"code":555,"score":64,"supported":47,"title":556,"abstract":557,"tags":560},"H3",[],[558,559],"灌溉","irrigation",[],{"code":461,"score":562,"supported":47,"title":563,"abstract":564,"tags":565},4,[],[505],[505],{"code":567,"score":72,"supported":47,"title":568,"abstract":569,"tags":571},"F3",[],[570],"养分管理",[],{"code":573,"score":72,"supported":47,"title":574,"abstract":575,"tags":577},"F5",[],[576],"农业机械",[],[579,580],"农业大数据 遥感 物联网","机器学习 作物产量预测","农业大数据遥感物联网-4302","10.4238\u002Fk6hcgw77",{"doi":582,"openalex_id":584,"authors":585,"venue":497,"cited_by_count":92,"oa_url":494,"card":596,"direction":100,"ingested_from":101},"W7220374836",[586,588,590,592,594],{"name":587,"orcid":9},"Kodavali Aswini",{"name":589,"orcid":9},"Raju Marimuthu",{"name":591,"orcid":9},"Guru Gurusamy",{"name":593,"orcid":9},"Kannan Pandian",{"name":595,"orcid":9},"Anandham Rangasamy",{"tldr":597,"method":598,"finding":599,"direction":600,"opportunity":601},"综述农学大数据的技术、应用、效益与挑战，强调多源数据融合支撑精准农业决策。","整合遥感、无人机、物联网、GIS、云计算与AI\u002FML的多源农业数据分析。","大数据可提升资源效率与实时监测，但受成本、数据质量、隐私和农村数字基建制约。","农业人工智能与决策模型","可研究低成本边缘计算与数据互操作标准，破解小农户场景下大数据落地瓶颈。","2026-10-06T23:30:11.880721Z"]