[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5589":3,"related-5589":81},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":80},5589,"Conformal-Risk Evaluation for Artificial Intelligence Agriculture Spray-Nozzle Fault Detection Under Label-Delay Feedback: A Computer Coverage Audit","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544107","Spray-nozzle fault detection in precision agriculture is a classification problem in which the label that confirms a fault arrives after the spray pass, so the detector must act on unlabelled evidence and its error rate must be certified before the label exists. Conformal prediction offers distribution-free coverage guarantees, but the guarantee is stated for exchangeable calibration and test data, an assumption that label-delayed feedback violates because the calibration set is always older than the deployment set. the present analysis formalises label-delay feedback as a distribution shift in the conformity score and derives a coverage bound for split-conformal fault detectors under that shift. The bound separates the nominal miscoverage rate into an exchangeable component and a delay-induced component that scales with the total variation distance between the calibration and deployment score distributions. A replication-based evaluation of the ['Intercept', 'exposure', 'group', 'severity'] estimator was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), and the study characterises the behaviour of the ordinary least squares estimator under the stated process. The estimator recovered the declared coefficients with estimated bias 0.01122 for the intercept and -0.0002 for exposure, root mean squared error 0.9217 and 0.01614 respectively, and empirical coverage of the nominal 95% intervals of 1.0 for both parameters across 600 replications. The coverage audit shows that the delay-induced term, not the exchangeable term, dominates the risk of a conformal spray-nozzle detector, and that the audit procedure can certify a detector before field labels return.","在精准农业中，喷雾喷嘴故障检测是一个分类问题，其中确认故障的标签在喷雾作业结束后才到达，因此检测器必须基于未标记的证据采取行动，并且其错误率必须在标签存在之前得到认证。保形预测（conformal prediction）提供了无分布覆盖保证，但该保证是针对可交换的校准数据和测试数据而言的，而标签延迟反馈违反了这一假设，因为校准集总是比部署集更旧。本分析将标签延迟反馈形式化为一致性分数中的分布偏移，并推导了在该偏移下分裂保形故障检测器的覆盖界。该界将名义错误覆盖 rate 分解为可交换分量和延迟诱导分量，后者随校准与部署分数分布之间的全变差距离而缩放。对 ['Intercept', 'exposure', 'group', 'severity'] 估计器进行了基于重复的评估：从声明的数据生成过程（截距 12.0，暴露 0.42，分组 -3.1，严重程度 1.35，误差标准差 4.2）中抽取了 600 个 N = 500 的数据集，研究刻画了普通最小二乘估计器在所述过程下的行为。该估计器恢复了声明的系数，截距的估计偏差为 0.01122，暴露为 -0.0002，均方根误差分别为 0.9217 和 0.01614，两个参数在 600 次重复中名义 95% 区间的经验覆盖率为 1.0。覆盖审计表明，延迟诱导项而非可交换项主导了保形喷雾喷嘴检测器的风险，并且该审计程序可以在田间标签返回之前对检测器进行认证。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},8,18,17,9,1,"论文提出标签延迟下共形预测覆盖界，方法有新意但属预印本、应用面窄，可作为专业补充而非每日精选头条。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","农业人工智能","精准施药","病虫害监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,49,57],{"code":31,"score":20,"supported":44,"title":45,"abstract":47,"tags":48},true,[46],"artificial intelligence",[],[27],{"code":50,"score":51,"supported":44,"title":52,"abstract":53,"tags":56},"I1",7,[],[54,55],"精准农业","precision agriculture",[26],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"F4",[],[],[28],[63,64],"农业人工智能 病虫害监测 智慧农业 精准施药","农业人工智能 病虫害监测","农业人工智能病虫害监测智慧农业精准施药-5589",0,"10.5281\u002Fzenodo.23544107",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":66,"oa_url":6,"card":73,"direction":77,"ingested_from":79},"W7223116087",[71],{"name":72,"orcid":9},"Achieng' Atim Okello",{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"将标签延迟反馈建模为共形分数分布偏移，推导覆盖率界并审计喷雾喷嘴故障检测器。","分裂共形预测、总变差距离、600次重复模拟（N=500）评估OLS估计。","延迟诱导项而非可交换项主导检测器风险，审计可在田间标签返回前认证检测器。","农业人工智能与决策模型","可将该覆盖率审计框架扩展到其他标签延迟的农业传感器故障检测，并研究延迟分布估计误差对认证的影响。","openalex","2026-10-22T11:21:02.845718Z",{"total":82,"page":21,"page_size":82,"items":83},6,[84,153,243,316,374,459],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":9,"source_name":90,"source_url":87,"published_at":91,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":92,"score_detail":93,"sources":97,"tags":99,"primary_topic":102,"secondary_topics":103,"classification":104,"search_phrases":126,"slug":129,"view_count":130,"doi":131,"paper":132,"created_at":152},4108,"Visión artificial para la detección de plagas en cultivos de tomate: revisión sistemática de la literatura","https:\u002F\u002Fdoi.org\u002F10.36825\u002Friti.14.34.010","Phytosanitary monitoring is an essential activity for reducing production losses caused by pests in tomato crops grown under protected agriculture conditions. In recent years, computer vision and deep learning methods have gained relevance as tools capable of automating agricultural image detection and analysis processes. Despite these advances, there is still a need to determine which techniques have been used, under what conditions they have been evaluated, and what performance they have reported, particularly in protected agriculture and small-scale production contexts. This study aims to examine research published on these technologies and assess their potential incorporation into participatory agricultural monitoring schemes. To this end, a systematic review of scientific literature published between 2020 and 2026 was conducted following the methodological guidelines proposed by Kitchenham. The review was developed through the stages of planning, study search and selection, and information synthesis, considering previously established inclusion and exclusion criteria. As a result, fifty studies related to image processing, artificial intelligence, and phytosanitary monitoring were selected and analyzed. Among the main findings of the review, several studies reported high-performance levels for deep learning models in tasks involving the automatic identification of symptoms associated with pests and other crop-related conditions. The synthesis identified the main techniques, data, application contexts, and reported performance levels, providing reference elements for assessing their potential incorporation into participatory agricultural monitoring schemes.","植物检疫监测是减少保护地番茄种植中由有害生物造成的产量损失的一项重要活动。近年来，计算机视觉和深度学习方法作为能够自动化农业图像检测与分析流程的工具而日益受到重视。尽管取得了这些进展，但仍需明确已采用了哪些技术、这些技术在何种条件下进行了评估，以及它们报告了怎样的性能，尤其是在保护地农业和小规模生产情境下。本研究旨在考察已发表的关于这些技术的研究，并评估其纳入参与式农业监测方案的潜力。为此，按照Kitchenham提出的方法学指南，对2020年至2026年间发表的科学文献进行了系统综述。该综述通过规划、研究检索与筛选以及信息综合等阶段展开，并依据事先确立的纳入与排除标准。结果共筛选并分析了50项与图像处理、人工智能和植物检疫监测相关的研究。在综述的主要发现中，多项研究报告了深度学习模型在自动识别与有害生物相关的症状及其他作物相关状况的任务中具有高性能水平。该综合识别了主要技术、数据、应用情境及所报告的性能水平，为评估其纳入参与式农业监测方案的潜力提供了参考要素。","Revista de Investigación en Tecnologías de la Información","2026-10-02T00:00:00Z",63,{"impact":94,"substance":18,"depth":95,"authority":13,"freshness":17,"relevant":21,"comment":96},12,15,"系统综述梳理2020-2026年计算机视觉与深度学习在番茄病虫害检测中的应用，方法规范、结论有参考价值，但属细分领域综述，公共影响有限。",[98],{"name":90,"url":87},[26,27,100,101,29],"设施农业","番茄种植",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":105},{"candidates":106,"agricultural_evidence":44},[107,115,122],{"code":31,"score":13,"supported":44,"title":108,"abstract":109,"tags":114},[],[110,111,112,46,113],"深度学习","计算机视觉","deep learning","computer vision",[27],{"code":116,"score":51,"supported":44,"title":117,"abstract":118,"tags":121},"B1",[],[119,120],"番茄","tomato",[101],{"code":50,"score":21,"supported":14,"title":123,"abstract":124,"tags":125},[],[],[26],[127,128],"计算机视觉 番茄 病虫害检测","深度学习 设施农业 植保监测","计算机视觉番茄病虫害检测-4108",3,"10.36825\u002Friti.14.34.010",{"doi":131,"openalex_id":133,"authors":134,"venue":90,"cited_by_count":66,"oa_url":87,"card":147,"direction":77,"ingested_from":79},"W7216085028",[135,137,140,143,145],{"name":136,"orcid":9},"Jesús Alberto Alarcón Luna",{"name":138,"orcid":139},"María Salomé Alejandre Apolinar","https:\u002F\u002Forcid.org\u002F0009-0008-0232-8217",{"name":141,"orcid":142},"Hugo Amores Pérez","https:\u002F\u002Forcid.org\u002F0009-0009-0260-4872",{"name":144,"orcid":9},"Jacel Adame García",{"name":146,"orcid":9},"María Angélica Cerdán",{"tldr":148,"method":149,"finding":150,"direction":77,"opportunity":151},"系统综述2020-2026年计算机视觉与深度学习在番茄设施栽培虫害检测中的研究。","遵循Kitchenham指南系统检索筛选50篇文献，归纳技术、数据与性能。","深度学习模型在虫害症状自动识别任务中普遍报告高性能，但多限于特定条件。","面向设施农业与小规模生产的轻量化模型及参与式监测集成仍属研究空白。","2026-10-03T23:30:53.163959Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":9,"source_name":159,"source_url":156,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":166,"tags":168,"primary_topic":171,"secondary_topics":172,"classification":173,"search_phrases":196,"slug":199,"view_count":200,"doi":201,"paper":202,"created_at":242},3467,"Smart agriculture using digital holography and artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1871510","In viticulture, direct protection against downy and powdery mildews relies on the preventive application of fungicides, requiring growers to anticipate infection events. Decision-making is mainly supported by forecasting models driven by weather predictions. However, these decisions are inherently uncertain, as some treatments ultimately prove unnecessary, although this information only becomes available retrospectively from observed conditions. This study explores the integration of an advanced spore detection device to better target fungicide applications in vineyards, aiming to reduce the use of phytosanitary products while maintaining high grape quality. The stand-alone device uses digital holography combined with artificial intelligence (AI) for detecting and classifying airborne spores of both downy and powdery mildew. It enables the tracking of disease dynamics as well as the assessment of environmental conditions and treatment effects on spore counts. Two case studies with real-time data access in Changins, Switzerland and Château le Puy, France, are presented and revealed promising strategies for substantial reductions in fungicide use while maintaining effective disease control.","在葡萄栽培中，对霜霉病和白粉病的直接防护依赖于杀菌剂的预防性施用，这要求种植者预判侵染事件的发生。决策主要依靠由天气预报驱动的预测模型来支持。然而，这些决策本质上具有不确定性，因为有些处理最终被证明是不必要的，尽管这一信息只能通过观测条件回顾性地获得。本研究探索了集成先进孢子检测装置以更精准地指导葡萄园杀菌剂施用的方法，旨在减少植物检疫产品的使用，同时保持葡萄的高品质。该独立装置利用数字全息术结合人工智能（AI）来检测和分类空气中的霜霉病和白粉病孢子。它能够追踪病害动态，并评估环境条件和处理措施对孢子数量的影响。本文介绍了在瑞士Changins和法国Château le Puy进行的两个可实时获取数据的案例研究，并揭示了在保持有效病害控制的同时大幅减少杀菌剂用量的有前景的策略。","Frontiers in Plant Science","2026-09-24T00:00:00Z",80,{"impact":18,"substance":163,"depth":18,"authority":164,"freshness":17,"relevant":21,"comment":165},22,14,"数字全息结合AI检测葡萄病害孢子，为减少杀菌剂施用提供实证案例，方法新颖且具推广价值。",[167],{"name":159,"url":156},[26,27,28,169,170],"葡萄种植","病害预警",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":174},{"candidates":175,"agricultural_evidence":44},[176,180,185,192],{"code":31,"score":94,"supported":44,"title":177,"abstract":178,"tags":179},[46],[46],[27],{"code":50,"score":20,"supported":44,"title":181,"abstract":183,"tags":184},[182],"smart agriculture",[],[26],{"code":186,"score":51,"supported":44,"title":187,"abstract":188,"tags":191},"B2",[],[189,190],"葡萄","grape",[169],{"code":58,"score":21,"supported":14,"title":193,"abstract":194,"tags":195},[],[],[28],[197,198],"数字全息 孢子检测 葡萄","霜霉病 白粉病 人工智能","数字全息孢子检测葡萄-3467",4,"10.3389\u002Ffpls.2026.1871510",{"doi":201,"openalex_id":203,"authors":204,"venue":159,"cited_by_count":66,"oa_url":156,"card":236,"direction":240,"ingested_from":79},"W7214152870",[205,207,209,212,215,218,221,223,225,228,231,234],{"name":206,"orcid":9},"Tessa Chiara Basso",{"name":208,"orcid":9},"Sara Leoni",{"name":210,"orcid":211},"Denis Ullmann","https:\u002F\u002Forcid.org\u002F0000-0002-7179-005X",{"name":213,"orcid":214},"Adimulya Kartiyasa","https:\u002F\u002Forcid.org\u002F0009-0003-3142-1174",{"name":216,"orcid":217},"Livio Ruzzante","https:\u002F\u002Forcid.org\u002F0000-0002-8693-8678",{"name":219,"orcid":220},"Sylvain Schnée","https:\u002F\u002Forcid.org\u002F0000-0002-1014-1961",{"name":222,"orcid":9},"Anne‐Lise Fabre",{"name":224,"orcid":9},"Steven Hewison",{"name":226,"orcid":227},"Jérôme Kasparian","https:\u002F\u002Forcid.org\u002F0000-0003-2398-3882",{"name":229,"orcid":230},"Nicolas Berti","https:\u002F\u002Forcid.org\u002F0000-0003-3769-3966",{"name":232,"orcid":233},"Pierre‐Henri Dubuis","https:\u002F\u002Forcid.org\u002F0000-0002-9624-3925",{"name":235,"orcid":9},"Jean-Pierre Wolf",{"tldr":237,"method":238,"finding":239,"direction":240,"opportunity":241},"利用数字全息与AI检测葡萄园空气中霜霉和白粉病菌孢子，优化杀菌剂施用。","数字全息成像结合AI分类孢子，在瑞士和法国葡萄园实时监测。","孢子检测可追踪病害动态，在保持防效下大幅减少杀菌剂使用。","智慧农业 \u002F 农业物联网","可探索孢子自动监测与气象预报融合的精准施药决策模型，并验证多作物适用性。","2026-09-25T23:30:09.459179Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":251,"sources":253,"tags":255,"primary_topic":257,"secondary_topics":258,"classification":259,"search_phrases":270,"slug":273,"view_count":130,"doi":274,"paper":275,"created_at":315},3366,"Brazilian Insect Survey: A Platform for Pest Management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13744-026-01426-2","Abstract Modern agriculture faces significant challenges in integrated pest management, where collecting, connecting, and processing monitoring data in real time are essential. This study presents the Brazilian Insect Survey (BIS), a web-based platform designed to centralize and streamline phytosanitary data management, enabling the integration of computer vision, field experimentation, and population modeling within a unified digital ecosystem. The platform is organized into functional modules that support experimental data management (TrapSystem and AgroExperiment), automated insect detection and counting from digital images (InsectCV and AphidCV), and simulation of aphid population dynamics (ABISM). These components operate in synergy, supported by distributed processing infrastructure, to ensure scalable data handling and efficient analytical workflows. Multi-year field applications demonstrate that integrating field data acquisition and automated image analysis with mechanistic population modeling enables timely, model-driven interventions that reduce aphid infestation levels and protect crop yield potential under variable environmental conditions, providing empirical evidence for queryPlease check if the captured keywords are correct.the effectiveness of combining AI-based insect detection with population dynamics modeling in operational integrated pest management. The case studies presented here demonstrate that the BIS platform successfully integrates computer vision, field experimentation, and population modeling within a modular digital ecosystem, highlighting its potential to enhance decision-making and advance data-driven integrated pest management.","摘要 现代农业在有害生物综合治理方面面临重大挑战，其中实时采集、连接和处理监测数据至关重要。本研究提出了巴西昆虫调查平台（Brazilian Insect Survey，BIS），这是一个基于网络的平台，旨在集中化和简化植物检疫数据管理，使计算机视觉、田间试验和种群建模能够整合在一个统一的数字生态系统中。该平台按功能模块组织，支持实验数据管理（TrapSystem和AgroExperiment）、基于数字图像的昆虫自动检测与计数（InsectCV和AphidCV），以及蚜虫种群动态模拟（ABISM）。这些组件在分布式处理基础设施的支持下协同运行，以确保可扩展的数据处理和高效的分析工作流。多年田间应用表明，将田间数据采集和自动图像分析与机制性种群建模相结合，能够实现及时的、模型驱动的干预，从而在多变的环境条件下降低蚜虫侵染水平并保护作物产量潜力，为将基于人工智能的昆虫检测与种群动态建模相结合在实际有害生物综合治理中的有效性提供了经验证据。本文所呈现的案例研究表明，BIS平台成功地将计算机视觉、田间试验和种群建模整合在一个模块化数字生态系统中，凸显了其在增强决策能力和推进数据驱动有害生物综合治理方面的潜力。","Neotropical Entomology","2026-09-23T00:00:00Z",{"impact":18,"substance":163,"depth":18,"authority":164,"freshness":17,"relevant":21,"comment":252},"巴西昆虫调查平台将计算机视觉与种群动态模型整合进IPM实践，方法新颖、多年田间验证，对智慧农业病虫害监测有参考价值。",[254],{"name":249,"url":246},[26,27,111,29,256],"种群模型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":260},{"candidates":261,"agricultural_evidence":44},[262,266],{"code":31,"score":17,"supported":44,"title":263,"abstract":264,"tags":265},[],[111,113],[27,111],{"code":50,"score":21,"supported":14,"title":267,"abstract":268,"tags":269},[],[],[26],[271,272],"Brazil 蚜虫 监测平台","农业人工智能 病虫害监测 计算机视觉 智慧农业","Brazil蚜虫监测平台-3366","10.1007\u002Fs13744-026-01426-2",{"doi":274,"openalex_id":276,"authors":277,"venue":249,"cited_by_count":66,"oa_url":246,"card":309,"direction":314,"ingested_from":79},"W7214074071",[278,281,283,286,289,291,294,297,300,303,306],{"name":279,"orcid":280},"Telmo De Cesaro Júnior","https:\u002F\u002Forcid.org\u002F0000-0002-2886-229X",{"name":282,"orcid":9},"Bárbara Stella Wehrmann",{"name":284,"orcid":285},"Alexandre Tagliari Lazzaretti","https:\u002F\u002Forcid.org\u002F0009-0004-8345-8216",{"name":287,"orcid":288},"Roberto Wiest","https:\u002F\u002Forcid.org\u002F0000-0001-7982-6603",{"name":290,"orcid":9},"Jorge Luis Boeira Bavaresco",{"name":292,"orcid":293},"Brenda Slongo Taca","https:\u002F\u002Forcid.org\u002F0009-0000-6408-5523",{"name":295,"orcid":296},"Nicolas Welfer Kirinus","https:\u002F\u002Forcid.org\u002F0009-0003-5260-3161",{"name":298,"orcid":299},"Crislaine Sartori Suzana Milan","https:\u002F\u002Forcid.org\u002F0000-0003-0562-7286",{"name":301,"orcid":302},"Jayme Garcia Arnal Barbedo","https:\u002F\u002Forcid.org\u002F0000-0002-1156-8270",{"name":304,"orcid":305},"Douglas Lau","https:\u002F\u002Forcid.org\u002F0000-0001-8648-0102",{"name":307,"orcid":308},"Rafael Rieder","https:\u002F\u002Forcid.org\u002F0000-0002-7435-9054",{"tldr":310,"method":311,"finding":312,"direction":240,"opportunity":313},"巴西昆虫调查平台整合计算机视觉、田间试验与种群建模，实现害虫实时监测与治理。","构建BIS网络平台，集成InsectCV\u002FAphidCV图像识别、TrapSys","AI昆虫检测与种群动态建模结合可降低蚜虫侵害并保护作物产量。","可探索多害虫跨区域监测数据标准化与模型迁移，构建开放植保决策生态。","数字乡村与农业信息化","2026-09-24T23:30:25.977846Z",{"id":317,"title":318,"url":319,"summary":320,"summary_zh":9,"content":9,"source_name":321,"source_url":319,"published_at":322,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":327,"tags":329,"primary_topic":332,"secondary_topics":333,"classification":334,"search_phrases":350,"slug":353,"view_count":354,"doi":355,"paper":356,"created_at":373},2620,"A deep learning-enhanced vision system for precision target spraying in chinese cabbage cultivation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10444-4","A deep learning-enhanced vision system for precision target spraying in chinese cabbage cultivation。Precision Agriculture","Precision Agriculture","2026-09-16T00:00:00Z",70,{"impact":94,"substance":18,"depth":325,"authority":164,"freshness":13,"relevant":21,"comment":326},16,"核心期刊论文，方法新颖且时效性强，但属细分作物技术进展，产业影响范围有限。",[328],{"name":321,"url":319},[26,27,28,330,331],"机器视觉","大白菜",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":335},{"candidates":336,"agricultural_evidence":44},[337,342,346],{"code":31,"score":338,"supported":44,"title":339,"abstract":340,"tags":341},13,[112],[112],[27,330],{"code":50,"score":200,"supported":44,"title":343,"abstract":344,"tags":345},[],[55],[26],{"code":58,"score":21,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[28],[351,352],"农业人工智能 智慧农业 机器视觉 精准施药","农业人工智能 智慧农业","农业人工智能智慧农业机器视觉精准施药-2620",2,"10.1007\u002Fs11119-026-10444-4",{"doi":355,"openalex_id":357,"authors":358,"venue":321,"cited_by_count":66,"oa_url":9,"card":9,"direction":9,"ingested_from":79},"W7213275445",[359,361,363,365,368,371],{"name":360,"orcid":9},"Changxi Liu",{"name":362,"orcid":9},"Hang Shi",{"name":364,"orcid":9},"Hao Sun",{"name":366,"orcid":367},"Hui Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8843-7298",{"name":369,"orcid":370},"Qingda Li","https:\u002F\u002Forcid.org\u002F0000-0002-9046-2094",{"name":372,"orcid":9},"Jun Hu","2026-09-16T23:30:03.326664Z",{"id":375,"title":376,"url":377,"summary":378,"summary_zh":379,"content":9,"source_name":380,"source_url":377,"published_at":381,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":382,"score_detail":383,"sources":386,"tags":388,"primary_topic":391,"secondary_topics":396,"classification":398,"search_phrases":442,"slug":444,"view_count":354,"doi":445,"paper":446,"created_at":458},2046,"AI-Powered Crop Protection: Transforming Pest Surveillance and Decision-Making Through Digital Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjsrr\u002F2026\u002Fv32i94503","Crop protection is increasingly challenged by changing pest population dynamics, pesticide resistance and the need for more sustainable agricultural production. Artificial intelligence (AI) and digital agriculture offer complementary tools for improving pest surveillance, forecasting and decision-making through automated detection, continuous monitoring and more precise interventions. This review synthesises the applications of machine learning, deep learning, computer vision, Internet of Things-enabled sensing, smart traps, remote sensing, unmanned aerial vehicles, robotics, predictive analytics and intelligent decision support systems in pest management. These approaches support pest identification, population monitoring, outbreak prediction, risk assessment and site-specific management, thereby strengthening the information base required for integrated pest management. The review also examines emerging technologies, including generative AI, foundation models, digital twins, explainable AI, edge intelligence and autonomous agricultural platforms, in relation to future crop protection systems. Despite rapid technological progress, practical deployment remains constrained by limited high-quality datasets, variable field conditions, model generalisation, interoperability, digital infrastructure, cost and farmer adoption. Effective implementation therefore requires transparent, robust and scalable systems that are validated under diverse agricultural conditions and aligned with ecological pest management principles. Overall, integrating AI-enabled surveillance and decision support with integrated pest management can support more timely, adaptive and resource-efficient crop protection while reducing unnecessary interventions.","作物保护正日益受到害虫种群动态变化、农药抗性以及更可持续农业生产需求的挑战。人工智能（AI）与数字农业通过自动检测、持续监测和更精准的干预措施，为改善害虫监测、预测和决策提供了互补性工具。本文综述了机器学习、深度学习、计算机视觉、物联网传感、智能诱捕器、遥感、无人机、机器人技术、预测分析和智能决策支持系统在害虫管理中的应用。这些方法支持害虫识别、种群监测、暴发预测、风险评估和位点特异性管理，从而强化了有害生物综合治理所需的信息基础。本文还探讨了新兴技术，包括生成式人工智能、基础模型、数字孪生、可解释人工智能、边缘智能和自主农业平台，及其在未来作物保护系统中的前景。尽管技术进展迅速，实际部署仍受限于高质量数据集不足、田间条件多变、模型泛化能力、互操作性、数字基础设施、成本以及农民采纳程度等因素。因此，有效实施需要透明、稳健且可扩展的系统，这些系统应在多样化农业条件下得到验证，并与生态害虫管理原则相一致。总体而言，将人工智能驱动的监测与决策支持同有害生物综合治理相结合，可支持更及时、更具适应性和资源效率更高的作物保护，同时减少不必要的干预。","Journal of Scientific Research and Reports","2026-09-09T00:00:00Z",76,{"impact":18,"substance":384,"depth":19,"authority":338,"freshness":17,"relevant":21,"comment":385},20,"系统综述AI与数字农业在病虫害监测预警与决策支持中的应用与瓶颈，信息密度高、结论可靠，对智慧植保方向有参考价值，但属综述类论文而非突破性成果，适合进入精选。",[387],{"name":380,"url":377},[389,26,27,29,390],"数字农业","精准植保",{"code":58,"slug":392,"name":393,"path":394,"parent_code":395},"crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[397],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":399},{"candidates":400,"agricultural_evidence":44},[401,408,415,422,429,436],{"code":50,"score":402,"supported":44,"title":403,"abstract":405,"tags":407},19,[404],"digital agriculture",[389,406,404],"数字孪生",[389,26],{"code":58,"score":19,"supported":44,"title":409,"abstract":411,"tags":414},[410],"crop protection",[412,413,410],"农药","pesticide",[],{"code":31,"score":13,"supported":44,"title":416,"abstract":417,"tags":421},[],[110,418,111,419,112,46,113,420],"机器学习","machine learning","decision support",[27],{"code":423,"score":82,"supported":44,"title":424,"abstract":425,"tags":428},"I3",[],[426,427],"遥感","remote sensing",[],{"code":430,"score":82,"supported":44,"title":431,"abstract":432,"tags":435},"I5",[],[433,434],"物联网","internet of things",[],{"code":437,"score":130,"supported":44,"title":438,"abstract":439,"tags":441},"H8",[],[440],"可持续农业",[],[443,64],"农业人工智能 病虫害监测 数字农业 智慧农业","农业人工智能病虫害监测数字农业智慧农业-2046","10.9734\u002Fjsrr\u002F2026\u002Fv32i94503",{"doi":445,"openalex_id":447,"authors":448,"venue":380,"cited_by_count":66,"oa_url":377,"card":453,"direction":240,"ingested_from":79},"W7212081296",[449,451],{"name":450,"orcid":9},"R. Saritha",{"name":452,"orcid":9},"M. Swathi",{"tldr":454,"method":455,"finding":456,"direction":77,"opportunity":457},"综述AI与数字农业在害虫监测、预测及决策支持中的应用与挑战。","综述机器学习、深度学习、计算机视觉、物联网、遥感、无人机与决策支持系统。","AI可提升害虫识别、暴发预测和精准管理，但受数据、泛化、成本与采纳制约。","可探索生成式AI、数字孪生与边缘智能在田间害虫管理中的可解释、可扩展落地验证。","2026-09-10T23:30:09.692422Z",{"id":460,"title":461,"url":462,"summary":463,"summary_zh":464,"content":9,"source_name":10,"source_url":462,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":465,"score_detail":466,"sources":469,"tags":471,"primary_topic":475,"secondary_topics":481,"classification":483,"search_phrases":499,"slug":502,"view_count":66,"doi":503,"paper":504,"created_at":518},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。",69,{"impact":17,"substance":467,"depth":18,"authority":94,"freshness":13,"relevant":21,"comment":468},21,"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[470],{"name":10,"url":462},[26,27,472,473,474],"小农户","植物病害识别","可解释性",{"code":476,"slug":477,"name":478,"path":479,"parent_code":480},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[482],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":484},{"candidates":485,"agricultural_evidence":44},[486,491,495],{"code":476,"score":94,"supported":44,"title":487,"abstract":489,"tags":490},[488],"smallholder",[488],[472],{"code":31,"score":13,"supported":44,"title":492,"abstract":493,"tags":494},[113],[],[27,473],{"code":50,"score":21,"supported":14,"title":496,"abstract":497,"tags":498},[],[],[26],[500,501],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":503,"openalex_id":505,"authors":506,"venue":10,"cited_by_count":66,"oa_url":462,"card":513,"direction":77,"ingested_from":79},"W7223137323",[507,509,511],{"name":508,"orcid":9},"Akshay Ramgoolam",{"name":510,"orcid":9},"Priya Bundhoo Appadoo",{"name":512,"orcid":9},"Jean-Noël LEspérance",{"tldr":514,"method":515,"finding":516,"direction":77,"opportunity":517},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","2026-10-22T11:21:10.425290Z"]