[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2541":3,"related-2541":98},{"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":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":97},2541,"Evidence integrity and review utility in crop-image decision support: an audit-to-review framework for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs42269-026-01492-x","Abstract Background Agricultural artificial intelligence benchmarks can overstate decision reliability when multiple files represent the same biological evidence or when split provenance is unclear. We evaluated a classifier-agnostic audit-to-review framework that fixes evidence identity and role provenance before model comparison, declares aggregation estimands, and links predictive uncertainty to review workload. A public wheat-leaf corpus of 7,595 files was hashed to 5,118 unique contents; eligibility criteria defined a 915-content five-class task. Archived file-level benchmarks were separated from three frozen near-duplicate-grouped P2 partition configurations, and semantic, texture and fusion representations were evaluated under partition-weighted and one-content-one-weight estimands. Split conformal prediction was assessed by empirical coverage, review rate, error capture, review yield and dimensionless review utility. Results Macro-F1 was 0.962 under the legacy P0 benchmark, 0.805 ± 0.030 for semantic-only P2 and 0.819 ± 0.029 for direct fusion. The descriptive P0-P2 workflow-sensitivity gap was approximately 15.7% points, versus a 1.4-point partition-weighted fusion increment (95% bootstrap interval − 0.006 to 0.036). The leading fusion-related variants were numerically close, so no inferential ranking is claimed. At α = 0.10, global split conformal prediction achieved 0.896 empirical coverage while reviewing 20.8% of images and capturing 51.4% of top-1 errors; class-conditional Mondrian calibration achieved 0.923 coverage while reviewing 29.7% and capturing 64.8% of errors. Conclusions Defining the evidence unit is part of the statistical specification of a crop-image benchmark, not merely a data-cleaning step. In this corpus, benchmark interpretation was much more sensitive to evidence-workflow specification than to the evaluated representation refinement. The framework supports auditable internal evaluation and review triage but does not establish field, farm-level or intervention performance.","摘要 背景 当多个文件代表同一生物学证据或划分来源不明时，农业人工智能基准可能高估决策可靠性。我们评估了一个与分类器无关的“审计到复核”框架，该框架在模型比较前固定证据身份和角色来源，声明聚合估计目标，并将预测不确定性关联到复核工作量。一个包含7,595个文件的公开小麦叶片语料库经哈希处理得到5,118个唯一内容；资格标准定义了一个915个内容的五分类任务。将归档的文件级基准与三种冻结的近重复分组P2划分配置分离，并在划分加权和“一内容一权重”估计目标下评估了语义、纹理和融合表示。通过经验覆盖率、复核率、错误捕获率、复核产出率和无量纲复核效用评估分裂保形预测。结果 在遗留P0基准下，宏F1为0.962；仅语义P2为0.805 ± 0.030；直接融合为0.819 ± 0.029。描述性的P0-P2工作流敏感性差距约为15.7个百分点，而划分加权融合增量为1.4个百分点（95%自助法区间−0.006至0.036）。领先的融合相关变体在数值上接近，因此不声称推断性排序。在α = 0.10时，全局分裂保形预测实现了0.896的经验覆盖率，同时复核了20.8%的图像并捕获了51.4%的top-1错误；类条件Mondrian校准实现了0.923的覆盖率，同时复核了29.7%并捕获了64.8%的错误。结论 定义证据单元是作物图像基准统计规范的一部分，而不仅仅是数据清理步骤。在该语料库中，基准解释对证据-工作流规范的敏感性远高于对所评估表示改进的敏感性。该框架支持可审计的内部评估和复核分诊，但不能确立田间、农场级或干预性能。",null,"Bulletin of the National Research Centre\u002FBulletin of the National Research Center","2026-09-14T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,13,9,1,"该论文提出面向作物图像决策支持的证据完整性审计框架，揭示基准测试中证据单元定义对性能评估的显著影响，方法新颖、数据规模较大，对农业AI模型评估具有参考价值，但属细分领域方法学研究，产业影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","遥感监测","作物病害识别","模型评估",{"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":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,59,67],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},16,true,[49],"decision support",[28,51],"artificial intelligence",[30,28],{"code":54,"score":21,"supported":47,"title":55,"abstract":57,"tags":58},"I1",[56],"precision agriculture",[],[27],{"code":60,"score":61,"supported":47,"title":62,"abstract":63,"tags":66},"A2",6,[],[64,65],"小麦","wheat",[],{"code":68,"score":22,"supported":14,"title":69,"abstract":70,"tags":71},"I3",[],[],[29],[73,74],"作物病害识别 农业人工智能 智慧农业 模型评估","作物病害识别 农业人工智能","作物病害识别农业人工智能智慧农业模型评估-2541",2,"10.1186\u002Fs42269-026-01492-x",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":89,"oa_url":6,"card":90,"direction":94,"ingested_from":96},"W7212800882",[81,84,86],{"name":82,"orcid":83},"Xin Li","https:\u002F\u002Forcid.org\u002F0009-0005-0670-5006",{"name":85,"orcid":9},"Bojian Guo",{"name":87,"orcid":88},"A.Dzh. Kartanova","https:\u002F\u002Forcid.org\u002F0000-0003-1479-0747",0,{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"提出审计到评审框架，先固定证据身份再比较作物图像分类器，并关联预测不确定性与评审工作量。","对7595个小麦叶片文件哈希去重，构建915内容五类任务，用分裂保形预测评估。","基准解释对证据工作流规范远比对表示优化敏感，P0与P2宏F1差约15.7个百分点。","农业人工智能与决策模型","可将证据单元定义与评审效用纳入农业AI基准标准，并探索田间级验证与主动学习结合。","openalex","2026-09-15T23:30:34.690905Z",{"total":61,"page":22,"page_size":61,"items":99},[100,163,252,316,394,457],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":118,"secondary_topics":124,"classification":126,"search_phrases":143,"slug":146,"view_count":89,"doi":147,"paper":148,"created_at":162},5590,"Evaluation of Concept-Probe Temporal-Stability Audits for Computer Vision Agriculture Disease Classifiers Under Seasonal Phenology Drift: An Interpretability Decay Bound for Smallholder Fungicide Timing","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545234","Concept-probe audits are increasingly used to certify that a crop-disease classifier's internal representations track the pathological concepts a smallholder would act on, but a probe certified at one point in the season may silently decouple from those concepts as phenology shifts the visual statistics of the field. the present analysis treats that decoupling as a formal object. It defines a temporal-stability audit as a sequence of concept-probe accuracies measured across defined phenological windows, and it derives an interpretability decay bound: the rate at which probe accuracy can fall before the fungicide-timing decision it is meant to support becomes unreliable. The bound is expressed as a function of the probe's initial margin, the drift in concept prevalence between windows, and the classifier's sensitivity to the drifted features, and it yields a certified audit interval rather than a single-point certificate. The paper then characterises the operating characteristics of the audit design under a declared variance structure, using a replication-based evaluation of the before-after-control-impact estimator with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64. Power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 54 clusters were required for 5 percentage points at 80% power. The contribution is a decision-theoretic account of when a concept-probe certificate expires, and a design rule for scheduling re-audits across the season.","概念探针审计（concept-probe audit）日益被用于验证作物病害分类器的内部表征是否追踪小农户会据以采取行动的病理学概念，但在生长季某一时点获得认证的探针，可能随着物候变化改变田间的视觉统计特征而悄然与这些概念脱钩。本分析将这种脱钩视为一个形式化对象。它将时间稳定性审计（temporal-stability audit）定义为在既定物候窗口内测量的一系列概念探针准确率，并推导出一个可解释性衰减界限（interpretability decay bound）：即探针准确率在其所支持的杀菌剂施用时机决策变得不可靠之前所能下降的速率。该界限表示为探针初始裕度、窗口间概念流行率漂移以及分类器对漂移特征敏感度的函数，并给出一个经认证的审计区间而非单点认证。随后，本文在声明的方差结构下刻画该审计设计的运行特征，采用基于重复的前后对照影响估计量评估，其中组间标准差 = 6.0、残差标准差 = 4.5、组内相关系数 = 0.64。功效在 2000 个设计单元上估计，聚类数取 8、16、24 和 32，效应量取 3、5 和 8 个百分点；在 80% 功效下达到 5 个百分点需要 54 个聚类。本文的贡献在于从决策论角度阐明概念探针认证何时失效，并给出在整个生长季内安排重新审计的设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",70,{"impact":110,"substance":18,"depth":19,"authority":111,"freshness":13,"relevant":22,"comment":112},8,12,"该论文提出概念探针审计的时序稳定性框架与可解释性衰减界，方法新颖、统计设计扎实，但属小众方法论研究，产业影响有限，适合作为专业精选而非头条。",[114],{"name":106,"url":103},[27,28,116,30,117],"小农户","模型审计",{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[125],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":127},{"candidates":128,"agricultural_evidence":47},[129,134,139],{"code":119,"score":17,"supported":47,"title":130,"abstract":132,"tags":133},[131],"smallholder",[116,131],[116],{"code":33,"score":13,"supported":47,"title":135,"abstract":137,"tags":138},[136],"computer vision",[],[30,28],{"code":54,"score":22,"supported":14,"title":140,"abstract":141,"tags":142},[],[],[27],[144,145],"概念探针 时序稳定性 作物病害分类器","杀菌剂 施药时机 可解释性衰减","概念探针时序稳定性作物病害分类器-5590","10.5281\u002Fzenodo.23545234",{"doi":147,"openalex_id":149,"authors":150,"venue":106,"cited_by_count":89,"oa_url":103,"card":157,"direction":94,"ingested_from":96},"W7223088030",[151,153,155],{"name":152,"orcid":9},"Abdi-Basid Idriss Farah",{"name":154,"orcid":9},"Amina Abdillahi Guedi",{"name":156,"orcid":9},"Moussa Omar Youssouf",{"tldr":158,"method":159,"finding":160,"direction":94,"opportunity":161},"提出概念探针时间稳定性审计与可解释性衰减界，判断何时需重新审计。","定义跨物候窗口探针精度序列，推导衰减界，用BACI复制评估统计功效。","5个百分点效应需54个聚类达80%功效，审计证书会随物候漂移过期。","可将衰减界与再审计调度规则嵌入小农户杀菌剂时机决策支持系统，并验证跨作物泛化。","2026-10-22T11:21:08.978348Z",{"id":164,"title":165,"url":166,"summary":167,"summary_zh":168,"content":9,"source_name":106,"source_url":166,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":170,"sources":172,"tags":174,"primary_topic":177,"secondary_topics":178,"classification":185,"search_phrases":234,"slug":237,"view_count":22,"doi":238,"paper":239,"created_at":251},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。","2026-10-15T00:00:00Z",{"impact":19,"substance":46,"depth":17,"authority":111,"freshness":21,"relevant":22,"comment":171},"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。",[173],{"name":106,"url":166},[27,28,175,176,29],"可持续发展","气候变化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[179],{"code":180,"slug":181,"name":182,"path":183,"parent_code":184},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":186},{"candidates":187,"agricultural_evidence":47},[188,196,201,207,213,220,227],{"code":33,"score":19,"supported":47,"title":189,"abstract":190,"tags":195},[51],[191,192,193,194,51],"深度学习","机器学习","machine learning","deep learning",[28],{"code":180,"score":17,"supported":47,"title":197,"abstract":199,"tags":200},[198],"climate change",[176,198],[176],{"code":54,"score":202,"supported":47,"title":203,"abstract":204,"tags":206},7,[],[205,56],"精准农业",[27],{"code":68,"score":202,"supported":47,"title":208,"abstract":209,"tags":212},[],[210,211],"遥感","remote sensing",[29],{"code":214,"score":61,"supported":47,"title":215,"abstract":216,"tags":219},"H4",[],[217,218],"碳排放","carbon emission",[],{"code":221,"score":61,"supported":47,"title":222,"abstract":223,"tags":226},"H6",[],[224,225],"生物多样性","biodiversity",[],{"code":228,"score":61,"supported":47,"title":229,"abstract":230,"tags":233},"I5",[],[231,232],"物联网","internet of things",[],[235,236],"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":238,"openalex_id":240,"authors":241,"venue":106,"cited_by_count":89,"oa_url":166,"card":244,"direction":250,"ingested_from":96},"W7222092855",[242],{"name":243,"orcid":9},"Deepmala Suryavanshi",{"tldr":245,"method":246,"finding":247,"direction":248,"opportunity":249},"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。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:15.059867Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":263,"tags":265,"primary_topic":268,"secondary_topics":272,"classification":274,"search_phrases":297,"slug":300,"view_count":301,"doi":302,"paper":303,"created_at":315},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":111,"substance":19,"depth":46,"authority":20,"freshness":21,"relevant":22,"comment":262},"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。",[264],{"name":258,"url":255},[27,28,266,29,267],"农业物联网","作物估产",{"code":228,"slug":269,"name":270,"path":271,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[273],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":275},{"candidates":276,"agricultural_evidence":47},[277,284,289,293],{"code":228,"score":19,"supported":47,"title":278,"abstract":280,"tags":283},[279],"IoT",[231,281,232,279,282],"边缘计算","edge computing",[266],{"code":54,"score":17,"supported":47,"title":285,"abstract":287,"tags":288},[286],"smart agriculture",[205,56],[27],{"code":33,"score":13,"supported":47,"title":290,"abstract":291,"tags":292},[],[191,192,193,194,51],[28],{"code":68,"score":76,"supported":14,"title":294,"abstract":295,"tags":296},[],[],[267,29],[298,299],"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",3,"10.1007\u002Fs12083-026-02299-3",{"doi":302,"openalex_id":304,"authors":305,"venue":258,"cited_by_count":89,"oa_url":255,"card":310,"direction":250,"ingested_from":96},"W7221560555",[306,308],{"name":307,"orcid":9},"Keerthana T",{"name":309,"orcid":9},"Janarthanan R",{"tldr":311,"method":312,"finding":313,"direction":250,"opportunity":314},"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":317,"title":318,"url":319,"summary":320,"summary_zh":321,"content":9,"source_name":322,"source_url":319,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":323,"sources":326,"tags":328,"primary_topic":331,"secondary_topics":337,"classification":343,"search_phrases":367,"slug":370,"view_count":22,"doi":371,"paper":372,"created_at":393},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",{"impact":46,"substance":324,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":325},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。",[327],{"name":322,"url":319},[27,329,28,29,330],"无人机","病害预警",{"code":332,"slug":333,"name":334,"path":335,"parent_code":336},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[338,339],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":68,"slug":340,"name":341,"path":342,"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":344},{"candidates":345,"agricultural_evidence":47},[346,354,358,363],{"code":332,"score":347,"supported":47,"title":348,"abstract":350,"tags":353},17,[349],"apple",[351,352,349],"苹果","葡萄",[],{"code":33,"score":17,"supported":47,"title":355,"abstract":356,"tags":357},[194],[191,194],[28],{"code":68,"score":111,"supported":47,"title":359,"abstract":361,"tags":362},[360],"multispectral",[360],[29],{"code":54,"score":202,"supported":47,"title":364,"abstract":365,"tags":366},[],[205,56],[27],[368,369],"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":371,"openalex_id":373,"authors":374,"venue":322,"cited_by_count":89,"oa_url":386,"card":387,"direction":250,"ingested_from":96},"W7221558726",[375,378,381,383],{"name":376,"orcid":377},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":379,"orcid":380},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":382,"orcid":9},"Fatih Sinan Bir",{"name":384,"orcid":385},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":388,"method":389,"finding":390,"direction":391,"opportunity":392},"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。","农业遥感与作物表型","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":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":406,"tags":408,"primary_topic":411,"secondary_topics":416,"classification":418,"search_phrases":438,"slug":441,"view_count":76,"doi":442,"paper":443,"created_at":456},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":19,"substance":18,"depth":19,"authority":404,"freshness":21,"relevant":22,"comment":405},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。",[407],{"name":400,"url":397},[27,28,409,29,410],"水稻","作物长势",{"code":412,"slug":413,"name":409,"path":414,"parent_code":415},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[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},[421,426,430,434],{"code":412,"score":19,"supported":47,"title":422,"abstract":423,"tags":425},[413],[409,424,413],"稻田",[409],{"code":33,"score":17,"supported":47,"title":427,"abstract":428,"tags":429},[194],[191,194],[28],{"code":68,"score":202,"supported":47,"title":431,"abstract":432,"tags":433},[],[210,211],[29],{"code":54,"score":22,"supported":14,"title":435,"abstract":436,"tags":437},[],[],[27],[439,440],"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":442,"openalex_id":444,"authors":445,"venue":400,"cited_by_count":89,"oa_url":397,"card":451,"direction":391,"ingested_from":96},"W7220838898",[446,449],{"name":447,"orcid":448},"Shashank Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1291-7951",{"name":450,"orcid":9},"Shefali Arora Chouhan",{"tldr":452,"method":453,"finding":454,"direction":391,"opportunity":455},"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":458,"title":459,"url":460,"summary":461,"summary_zh":462,"content":9,"source_name":463,"source_url":460,"published_at":464,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":465,"sources":467,"tags":469,"primary_topic":472,"secondary_topics":478,"classification":480,"search_phrases":527,"slug":530,"view_count":531,"doi":532,"paper":533,"created_at":552},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":111,"substance":324,"depth":347,"authority":111,"freshness":110,"relevant":22,"comment":466},"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。",[468],{"name":463,"url":460},[470,27,28,471,29],"数字乡村","农业保险",{"code":473,"slug":474,"name":475,"path":476,"parent_code":477},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[479],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":481},{"candidates":482,"agricultural_evidence":47},[483,489,493,498,503,509,514,518,522],{"code":473,"score":484,"supported":47,"title":485,"abstract":487,"tags":488},11,[486],"cattle",[486],[],{"code":33,"score":21,"supported":47,"title":490,"abstract":491,"tags":492},[51],[],[28],{"code":477,"score":301,"supported":47,"title":494,"abstract":495,"tags":497},[],[496],"livestock",[],{"code":180,"score":301,"supported":47,"title":499,"abstract":500,"tags":502},[],[501],"干旱",[],{"code":504,"score":301,"supported":47,"title":505,"abstract":506,"tags":508},"I7",[],[507],"研究方法",[],{"code":510,"score":22,"supported":14,"title":511,"abstract":512,"tags":513},"G3",[],[],[471],{"code":54,"score":22,"supported":14,"title":515,"abstract":516,"tags":517},[],[],[27],{"code":68,"score":22,"supported":14,"title":519,"abstract":520,"tags":521},[],[],[29],{"code":523,"score":22,"supported":14,"title":524,"abstract":525,"tags":526},"I6",[],[],[470],[528,529],"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":532,"openalex_id":534,"authors":535,"venue":463,"cited_by_count":89,"oa_url":545,"card":546,"direction":250,"ingested_from":96},"W7220655164",[536,539,542],{"name":537,"orcid":538},"Nyasha Nyakuchena","https:\u002F\u002Forcid.org\u002F0009-0005-3219-2275",{"name":540,"orcid":541},"Joseph P. Musara","https:\u002F\u002Forcid.org\u002F0000-0002-2226-3616",{"name":543,"orcid":544},"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":547,"method":548,"finding":549,"direction":550,"opportunity":551},"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"]