[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4108":3,"related-4108":97},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":96},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。",null,"Revista de Investigación en Tecnologías de la Información","2026-10-02T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,18,15,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","设施农业","番茄种植","病虫害监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,54,62],{"code":32,"score":13,"supported":45,"title":46,"abstract":47,"tags":53},true,[],[48,49,50,51,52],"深度学习","计算机视觉","deep learning","artificial intelligence","computer vision",[27],{"code":55,"score":56,"supported":45,"title":57,"abstract":58,"tags":61},"B1",7,[],[59,60],"番茄","tomato",[29],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"I1",[],[],[26],[68,69],"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":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":88,"oa_url":6,"card":89,"direction":93,"ingested_from":95},"W7216085028",[76,78,81,84,86],{"name":77,"orcid":9},"Jesús Alberto Alarcón Luna",{"name":79,"orcid":80},"María Salomé Alejandre Apolinar","https:\u002F\u002Forcid.org\u002F0009-0008-0232-8217",{"name":82,"orcid":83},"Hugo Amores Pérez","https:\u002F\u002Forcid.org\u002F0009-0009-0260-4872",{"name":85,"orcid":9},"Jacel Adame García",{"name":87,"orcid":9},"María Angélica Cerdán",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"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。","农业人工智能与决策模型","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。","openalex","2026-10-03T23:30:53.163959Z",{"total":98,"page":21,"page_size":98,"items":99},6,[100,153,230,316,360,423],{"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":117,"secondary_topics":118,"classification":119,"search_phrases":137,"slug":140,"view_count":88,"doi":141,"paper":142,"created_at":152},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",62,{"impact":20,"substance":18,"depth":110,"authority":111,"freshness":13,"relevant":21,"comment":112},17,9,"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。",[114],{"name":106,"url":103},[26,27,116,30],"精准施药",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":120},{"candidates":121,"agricultural_evidence":45},[122,126,132],{"code":32,"score":111,"supported":45,"title":123,"abstract":124,"tags":125},[51],[],[27],{"code":63,"score":56,"supported":45,"title":127,"abstract":128,"tags":131},[],[129,130],"精准农业","precision agriculture",[26],{"code":133,"score":21,"supported":14,"title":134,"abstract":135,"tags":136},"F4",[],[],[116],[138,139],"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","10.5281\u002Fzenodo.23544107",{"doi":141,"openalex_id":143,"authors":144,"venue":106,"cited_by_count":88,"oa_url":103,"card":147,"direction":93,"ingested_from":95},"W7223116087",[145],{"name":146,"orcid":9},"Achieng' Atim Okello",{"tldr":148,"method":149,"finding":150,"direction":93,"opportunity":151},"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。","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。","2026-10-22T11:21:02.845718Z",{"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":170,"secondary_topics":171,"classification":172,"search_phrases":183,"slug":186,"view_count":71,"doi":187,"paper":188,"created_at":229},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",80,{"impact":18,"substance":163,"depth":18,"authority":164,"freshness":20,"relevant":21,"comment":165},22,14,"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。",[167],{"name":159,"url":156},[26,27,49,30,169],"种群模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":173},{"candidates":174,"agricultural_evidence":45},[175,179],{"code":32,"score":20,"supported":45,"title":176,"abstract":177,"tags":178},[],[49,52],[27,49],{"code":63,"score":21,"supported":14,"title":180,"abstract":181,"tags":182},[],[],[26],[184,185],"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":187,"openalex_id":189,"authors":190,"venue":159,"cited_by_count":88,"oa_url":156,"card":222,"direction":228,"ingested_from":95},"W7214074071",[191,194,196,199,202,204,207,210,213,216,219],{"name":192,"orcid":193},"Telmo De Cesaro Júnior","https:\u002F\u002Forcid.org\u002F0000-0002-2886-229X",{"name":195,"orcid":9},"Bárbara Stella Wehrmann",{"name":197,"orcid":198},"Alexandre Tagliari Lazzaretti","https:\u002F\u002Forcid.org\u002F0009-0004-8345-8216",{"name":200,"orcid":201},"Roberto Wiest","https:\u002F\u002Forcid.org\u002F0000-0001-7982-6603",{"name":203,"orcid":9},"Jorge Luis Boeira Bavaresco",{"name":205,"orcid":206},"Brenda Slongo Taca","https:\u002F\u002Forcid.org\u002F0009-0000-6408-5523",{"name":208,"orcid":209},"Nicolas Welfer Kirinus","https:\u002F\u002Forcid.org\u002F0009-0003-5260-3161",{"name":211,"orcid":212},"Crislaine Sartori Suzana Milan","https:\u002F\u002Forcid.org\u002F0000-0003-0562-7286",{"name":214,"orcid":215},"Jayme Garcia Arnal Barbedo","https:\u002F\u002Forcid.org\u002F0000-0002-1156-8270",{"name":217,"orcid":218},"Douglas Lau","https:\u002F\u002Forcid.org\u002F0000-0001-8648-0102",{"name":220,"orcid":221},"Rafael Rieder","https:\u002F\u002Forcid.org\u002F0000-0002-7435-9054",{"tldr":223,"method":224,"finding":225,"direction":226,"opportunity":227},"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。","智慧农业 \u002F 农业物联网","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":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":9,"source_name":236,"source_url":233,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":242,"tags":244,"primary_topic":247,"secondary_topics":252,"classification":259,"search_phrases":288,"slug":291,"view_count":292,"doi":293,"paper":294,"created_at":315},2805,"Synergistic effects of spectral preprocessing and machine learning algorithms for nitrogen estimation in tomato using hyperspectral spectral data","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71908-1","Abstract Accurate estimation of leaf nitrogen content is essential for optimizing fertilization management and improving crop productivity. This study proposes a nondestructive hyperspectral imaging framework combined with advanced machine learning algorithms to classify nitrogen levels in tomato leaves (Solanum lycopersicum L., Royal variety). A total of 300 hyperspectral samples were collected from plants subjected to three nitrogen treatments (N-30%, N-60%, and N-90%) under controlled conditions. Reflectance data (400–1000 nm) were calibrated and preprocessed using Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky–Golay (SG) filtering. Both unsupervised and supervised algorithms were systematically evaluated. Clustering results demonstrated that preprocessing substantially influenced class separability. The MSC + GMM combination yielded the best unsupervised results, with the lowest Davies–Bouldin index (0.443) and the highest silhouette coefficient (0.975), indicating improved cluster compactness and separation. In supervised learning, Neural Networks (NN) consistently outperformed the other models, achieving 100% accuracy and an ROC–AUC of 1.00 under SNV, MSC, and SG preprocessing. Gradient Boosting (GB) and Support Vector Machine (SVM) also demonstrated strong predictive capability, whereas conventional models, including LDA, LR, KNN, and NB, showed moderate improvements following preprocessing. Statistical analysis confirmed the significant effect of spectral preprocessing on both clustering and classification outcomes ( p \u003C 0.05). Overall, integrating hyperspectral imaging with appropriate preprocessing and nonlinear machine learning models provided strong classification performance under the controlled experimental conditions. However, the exceptionally high performance observed for some models should be interpreted cautiously because of the relatively small dataset and experimentally controlled nitrogen treatments. Moreover, the absence of an independent external validation dataset limits the assessment of generalizability across growing conditions, cultivars, and field environments. Therefore, independent validation using larger and more diverse datasets is required before broader application of the proposed framework in practical precision agriculture.","摘要 准确估算叶片氮含量对于优化施肥管理和提高作物生产力至关重要。本研究提出了一种无损高光谱成像框架，结合先进的机器学习算法对番茄叶片（Solanum lycopersicum L.，Royal品种）的氮水平进行分类。在受控条件下，从接受三种氮处理（N-30%、N-60%和N-90%）的植株中共采集了300个高光谱样本。反射率数据（400–1000 nm）经标准正态变量变换（SNV）、多元散射校正（MSC）和Savitzky–Golay（SG）滤波进行校准和预处理。系统评估了无监督和有监督算法。聚类结果表明，预处理对类别可分性产生了显著影响。MSC + GMM组合取得了最佳无监督结果，具有最低的Davies–Bouldin指数（0.443）和最高的轮廓系数（0.975），表明聚类紧密度和分离度得到改善。在有监督学习中，神经网络（NN）始终优于其他模型，在SNV、MSC和SG预处理下均达到100%的准确率和1.00的ROC–AUC。梯度提升（GB）和支持向量机（SVM）也表现出较强的预测能力，而传统模型（包括LDA、LR、KNN和NB）在预处理后表现出中等程度的改善。统计分析证实了光谱预处理对聚类和分类结果均有显著影响（p \u003C 0.05）。总体而言，将高光谱成像与适当的预处理及非线性机器学习模型相结合，在受控实验条件下提供了较强的分类性能。然而，由于数据集相对较小且氮处理为实验受控条件，某些模型所表现出的极高性能应谨慎解读。此外，缺乏独立的外部验证数据集限制了对跨生长条件、品种和田间环境泛化能力的评估。因此，在将该框架广泛应用于实际精准农业之前，需要使用更大规模、更多样化的数据集进行独立验证。","Scientific Reports","2026-09-17T00:00:00Z",77,{"impact":19,"substance":240,"depth":110,"authority":164,"freshness":13,"relevant":21,"comment":241},21,"高光谱结合机器学习实现番茄叶片氮素无损估测，方法系统、结论明确，但样本量小且缺乏外部验证，属细分领域方法学进展。",[243],{"name":236,"url":233},[26,27,245,29,246],"精准施肥","高光谱遥感",{"code":55,"slug":248,"name":249,"path":250,"parent_code":251},"vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[253,254],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":255,"slug":256,"name":257,"path":258,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":260},{"candidates":261,"agricultural_evidence":45},[262,266,272,277,284],{"code":55,"score":19,"supported":45,"title":263,"abstract":264,"tags":265},[60],[59,60],[29],{"code":32,"score":19,"supported":45,"title":267,"abstract":269,"tags":271},[268],"machine learning",[270,268],"机器学习",[27],{"code":255,"score":17,"supported":45,"title":273,"abstract":275,"tags":276},[274],"hyperspectral",[274],[246],{"code":278,"score":56,"supported":45,"title":279,"abstract":280,"tags":283},"F3",[],[281,282],"施肥","fertilization",[245],{"code":63,"score":56,"supported":45,"title":285,"abstract":286,"tags":287},[],[129,130],[26],[289,290],"农业人工智能 高光谱遥感 智慧农业 番茄种植","农业人工智能 高光谱遥感","农业人工智能高光谱遥感智慧农业番茄种植-2805",2,"10.1038\u002Fs41598-026-71908-1",{"doi":293,"openalex_id":295,"authors":296,"venue":236,"cited_by_count":88,"oa_url":233,"card":309,"direction":93,"ingested_from":95},"W7213472002",[297,300,303,306],{"name":298,"orcid":299},"Mohammad Vahedi Torshizi","https:\u002F\u002Forcid.org\u002F0000-0003-3648-1515",{"name":301,"orcid":302},"Sajad Sabzi","https:\u002F\u002Forcid.org\u002F0000-0003-2439-5329",{"name":304,"orcid":305},"Mohsen Azadbakht","https:\u002F\u002Forcid.org\u002F0000-0002-5726-9321",{"name":307,"orcid":308},"Razieh Pourdarbani","https:\u002F\u002Forcid.org\u002F0000-0003-0766-8305",{"tldr":310,"method":311,"finding":312,"direction":313,"opportunity":314},"用高光谱成像结合预处理与机器学习，对番茄叶片氮素水平进行分类。","300个高光谱样本，SNV、MSC、SG预处理，聚类与多种监督分类算法对比。","MSC+GMM聚类最优，神经网络在三种预处理下均达100%准确率。","农业遥感与作物表型","样本少且无外部验证，可扩展多品种、多环境田间数据并做独立验证以提升泛化性。","2026-09-17T23:30:59.361085Z",{"id":317,"title":318,"url":319,"summary":320,"summary_zh":9,"content":321,"source_name":322,"source_url":9,"published_at":323,"category":324,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":330,"tags":332,"primary_topic":335,"secondary_topics":336,"classification":337,"search_phrases":355,"slug":358,"view_count":71,"doi":9,"paper":9,"created_at":359},2729,"WAFI2026世界农业科技创新大会在京举行——辽宁省农科院等亮相","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7686105927610630691\u002F","2026世界农业科技创新大会(WAFI2026)9月16—19日在北京平谷举行。中国农业大学校长孙其信指出,人工智能已经从过去的示范,变成了部分大型新型经营主体的主要生产方式。中国农业大学王耀君副教授介绍神农大模型3.0已在非洲落地。北京数智京园智慧设施管控技术体系亮相。","潮新闻 记者 沈爱群 侴雪妍\n\n2026世界农业科技创新大会，正在北京举行。\n\n9月16日上午，在大会举行的“人工智能与农业论坛”上，潮新闻记者捕捉到了一个话题：人工智能如何造福农民？\n\n这个话题，由中国农业大学全球食物经济与政策研究院院长樊胜根教授在论坛致辞中提出。\n\n会上，与会国内外专家学者、业界代表见仁见智给出了答案：通过农业人工智能，可以让农民种得好、种得起、种得稳、种得赚。其中，深谙东方智慧的“中国方案”得到了与会嘉宾的点赞与关注。\n\n世界农业科技创新大会（英文缩写WAFI），是以“创新农业 共享未来”为宗旨的世界农业盛会，致力于打造农业“达沃斯”。自2023年成功举办以来，WAFI赢得了国内外同行的高度认可，被誉为世界三大农业盛会之一。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F59a79b9b009545259d6d2d50486c2167~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790208715&x-signature=Mbp32uTVtyQxX7rWipDc9RKtXnU%3D)\n活动现场（记者 沈爱群 摄）\n\n在这个世界级农业盛会上，与会嘉宾为何特别关注“人工智能与农民”？\n\n答案，可以从人工智能时代全球农民、小农户面临的挑战找到。\n\n农业人工智能论坛上，中国农业大学校长、中国工程院院士陈卫就谈到：当前全球农食系统面临前所未有的挑战，气候变化加剧了农业生产的风险，土地、水资源和生物多样性承受着越来越大的压力。“我们必须生产更多更有营养的食物，降低农业对环境的影响，为农村地区创造更好的发展机会。然而这些挑战在不同地区的分布并不均衡，一端是资本和技术高度密集的现代化农场，另一边是数以亿计、以有限的资源支撑全球重要粮食供给的农民与小农户。”\n\n挑战面前，人工智能为农业转型注入了新动能。正如与会嘉宾在本次论坛上提及，人工智能正在推动智能育种、精准种植和农业生产全产业链系统发生新的变化，以卫星遥感数据、气象预警数据、土壤变化数据、种子种植数据以及营销数据分析等，帮助农民实现种植方案自动生成、无人机出苗率检测、卫星遥感旱涝、摄像头自动巡田、智能拼车等农业生产及农事经营。\n\n推动这些新变化的“中国方案”中，有着力农业教育的中国高校，有从事农业生产的中国农业企业，也有站在消费端的中国城市设施农业。\n\n先看中国高校。会上，中国农业大学信息与电子工程学院副教授王耀君和中国农业大学全球食物经济与政策研究院教授张玉梅，分别在主旨演讲中提到了“神农大模型”以及“农业食物经济与政策AI模型”。\n\n神农大模型，去年就已做到了3.0版。作为国内首个实现农业系统智能的大模型，神农1.0版于2023年问世，可以实现农业专业知识的精准查询与问答。2024年迭代的神农2.0版，拓展了技术边界，可以整合文本、图像等多模态数据进行分析决策。2025年全球首发的神农3.0版，打破了农业学科壁垒，让AI成为汇聚农业智慧的载体。\n\n神农大模型3.0是“小麦育种智能助手”。它融合了国家级种质资源与专家知识，可以赋能育种决策，实现从“田间试错”到“精准育种”。\n\n神农3.0还是个农业病虫害智能体。可以识别70类、600余种病虫害，实现用一部手机就能获得如同多个专业植保专家的指导。\n\n值得一提的是，神农3.0还可以让全球农业科技人员和从业者自主构建，以最低成本推动AI应用，让农业AI在科研院所和田间地头普惠落地。“经过过去一年的推广，神农大模型已经跨越千山万水，在非洲落地了。”神农大模型团队负责人王耀君说。\n\n农业食物经济与政策AI模型，不仅面向政府、科研机构和企业，也面向广大农业生产者，旨在将数据、经济模型与人工智能相结合，为农业市场研判和科学决策提供支持。为此，张玉梅教授提醒：“对于农民和小农户来说，这个模型提供的国内外农产品价格监测与异常预警、农业生产成本收益分析、膳食营养评价和国际市场动态分析，可以帮助他们及时了解市场变化 、比较政策方案、评估生产经营收益与营养状况。”\n\n其次，看看农业企业端。会上，北大荒信息有限公司总经理任荣荣向大家介绍了“未来农场”这个各项农艺技术集成平台。以深耕智慧农业培育发展新动能为己任的北大荒信息有限公司，既自主研发了智能装备管理平台覆盖111个农场、接入8.4万台智能装备；也让“未来农场”为60万种植户提供产前、产中、产后服务，实现农业资金交易1000亿元。\n\n再看城市设施农业。会上，北京市数字农业农村促进中心副主任、正高级农艺师芦天罡，向大家展示了北京市“数智京园”智慧设施管控技术体系和连栋温室“赛马制”中试熟化场景。\n\n据芦天罡介绍，北京市目前正通过AI+城市设施农业，实现了连栋温室的国产化技术攻关和日光温室的数智场景改造，快速推动农业产业智能化。“从系统到装备到模型，人工智能可以帮助农户解放劳动力、提高精细化生产水平，还可以调节生产周期，助推农户增收。”芦天罡表示。\n\n“转载请注明出处”","今日头条 2026年9月16日","2026-09-16T00:00:00Z","报道",78,{"impact":327,"substance":328,"depth":110,"authority":20,"freshness":111,"relevant":21,"comment":329},24,20,"世界级农业盛会现场报道，汇聚神农大模型3.0、未来农场、数智京园等多方实质进展，信息增量足，值得进入每日精选。",[331],{"name":322,"url":319},[26,27,28,333,334],"神农大模型","智能育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":338},{"candidates":339,"agricultural_evidence":45},[340,346,351],{"code":32,"score":341,"supported":45,"title":342,"abstract":343,"tags":345},5,[],[344],"大模型",[27,333],{"code":347,"score":21,"supported":14,"title":348,"abstract":349,"tags":350},"F2",[],[],[334],{"code":63,"score":21,"supported":14,"title":352,"abstract":353,"tags":354},[],[],[26],[356,357],"农业人工智能 神农大模型 智慧农业 智能育种","农业人工智能 神农大模型","农业人工智能神农大模型智慧农业智能育种-2729","2026-09-17T00:04:39.330857Z",{"id":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":9,"source_name":366,"source_url":363,"published_at":323,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":375,"secondary_topics":380,"classification":382,"search_phrases":407,"slug":410,"view_count":71,"doi":411,"paper":412,"created_at":422},2682,"Machine Learning-Based Decision Support System for Greenhouse Crop Management Under Mite Infestation Conditions","https:\u002F\u002Fdoi.org\u002F10.36099\u002Fjess.v1i3.001","This paper presents an integrated machine learning-based Decision Support System (DSS) for greenhouse crop management under mite infestation conditions, specifically designed for Sri Lankan agricultural contexts. The research addresses critical challenges faced by greenhouse farmers regarding pest management and crop productivity optimization through a comprehensive system combining NetLogo simulation for synthetic data generation, ensemble machine learning models, and a web-based interface. Field research with Sri Lankan greenhouse farmers revealed that mite infestations cause up to 40% crop losses, driving panic-induced pesticide overuse and knowledge gaps in pest management timing. The system integrates environmental monitoring, pest prediction, and crop yield forecasting to provide actionable recommendations for farmers. The mite infestation prediction model achieved 86% accuracy, while the system successfully addresses data scarcity challenges through agent-based modeling. The DSS demonstrates potential for transforming reactive farming practices into predictive, data-driven approaches while accommodating the technological constraints of developing agricultural contexts.","本文提出了一种基于机器学习的集成决策支持系统（DSS），用于螨虫侵染条件下的温室作物管理，专为斯里兰卡农业情境设计。该研究针对温室农户在害虫管理和作物生产力优化方面面临的关键挑战，通过一个综合系统加以解决，该系统结合了用于合成数据生成的NetLogo仿真、集成机器学习模型以及基于网络的界面。针对斯里兰卡温室农户的实地研究表明，螨虫侵染可导致高达40%的作物损失，进而引发恐慌性农药过度使用以及害虫管理时机方面的知识缺口。该系统整合了环境监测、害虫预测和作物产量预测，为农户提供可操作的推荐建议。螨虫侵染预测模型达到了86%的准确率，同时该系统通过基于智能体的建模成功应对了数据稀缺的挑战。该决策支持系统展现出将被动应对式耕作实践转变为预测性、数据驱动方法的潜力，同时兼顾了发展中农业情境的技术约束。","Journal of Environmental and Sustainability Science",74,{"impact":19,"substance":328,"depth":110,"authority":17,"freshness":13,"relevant":21,"comment":369},"将机器学习与智能体仿真结合用于温室螨害预测与决策支持，方法新颖、数据翔实，对设施农业植保信息化有参考价值。",[371],{"name":366,"url":363},[26,27,28,373,374],"决策支持系统","病虫害预警",{"code":376,"slug":377,"name":378,"path":379,"parent_code":251},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[381],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":383},{"candidates":384,"agricultural_evidence":45},[385,391,397,403],{"code":32,"score":386,"supported":45,"title":387,"abstract":389,"tags":390},27,[268,388],"decision support",[270,268,388],[27,373],{"code":376,"score":164,"supported":45,"title":392,"abstract":394,"tags":396},[393],"greenhouse",[395,393],"温室",[],{"code":133,"score":98,"supported":45,"title":398,"abstract":399,"tags":402},[],[400,401],"农药","pesticide",[],{"code":63,"score":21,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[26],[408,409],"农业人工智能 决策支持系统 病虫害预警 智慧农业","农业人工智能 决策支持系统","农业人工智能决策支持系统病虫害预警智慧农业-2682","10.36099\u002Fjess.v1i3.001",{"doi":411,"openalex_id":413,"authors":414,"venue":366,"cited_by_count":88,"oa_url":363,"card":417,"direction":93,"ingested_from":95},"W7213235680",[415],{"name":416,"orcid":9},"S. Nasiketha",{"tldr":418,"method":419,"finding":420,"direction":93,"opportunity":421},"为斯里兰卡温室农户开发基于机器学习的决策支持系统，预测螨害并优化作物管理。","NetLogo仿真生成合成数据，集成机器学习模型与网页界面。","螨害预测准确率达86%，可缓解数据稀缺并减少农药滥用。","可探索小样本下合成数据与迁移学习结合，提升发展中国家温室病虫害预测泛化能力。","2026-09-16T23:30:51.573509Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":429,"source_url":426,"published_at":430,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":431,"sources":434,"tags":436,"primary_topic":439,"secondary_topics":440,"classification":441,"search_phrases":457,"slug":460,"view_count":21,"doi":461,"paper":462,"created_at":488},2289,"High-throughput phenotyping platform for facility crops based on optical sensing technology: A review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102545","High-throughput acquisition of crop phenotypic information is one of the key technologies for achieving intelligent facility agriculture and precision breeding. Traditional phenotypic data collection methods suffer from low efficiency and strong subjectivity, making it difficult to achieve multi-scale continuous monitoring and meet the demands of modern research and production. This paper systematically reviews the technological framework and development trajectory of optical sensing technology-driven phenotypic platforms for facility crops. First, starting from optical sensing technologies, a comparative analysis highlights the advantages and limitations of RGB, multi-\u002Fhyperspectral, thermal infrared, and LiDAR sensors in phenotypic perception. Second, the characteristics and applicable scenarios of stationary, rail-mounted, mobile robot, and unmanned aerial vehicle (UAV) platform architectures are summarized. Furthermore, the evolution of phenotypic data processing methods is examined, focusing on the shift from traditional feature engineering to deep learning-driven approaches. Finally, key challenges such as multimodal data fusion, system cost, and real-time performance are discussed, along with the future direction of phenotypic platforms toward intelligent closed-loop decision-making systems. This article systematically reviews the facility agriculture phenotyping platforms driven by optical sensing technology, and also incorporates representative research progress in field phenotyping studies. These advances provide transferable sensing technologies, methodological frameworks, and platform design concepts that can facilitate the development of phenotyping platforms for controlled-environment agriculture.","高通量获取作物表型信息是实现智能设施农业和精准育种的关键技术之一。传统表型数据采集方式效率低、主观性强，难以实现多尺度连续监测，无法满足现代研究与生产需求。本文系统综述了光学传感技术驱动的设施作物表型平台的技术框架与发展脉络。首先，从光学传感技术出发，对比分析了RGB、多光谱\u002F高光谱、热红外和激光雷达传感器在表型感知中的优势与局限。其次，总结了固定式、轨道式、移动机器人和无人机平台架构的特点与适用场景。进而，梳理了表型数据处理方法的演进，重点分析了从传统特征工程到深度学习驱动方法的转变。最后，讨论了多模态数据融合、系统成本和实时性等关键挑战，并展望了表型平台向智能闭环决策系统发展的未来方向。本文系统综述了光学传感技术驱动的设施农业表型平台，同时纳入了田间表型研究中的代表性进展。这些进展提供了可迁移的传感技术、方法框架和平台设计理念，有助于推动受控环境农业表型平台的发展。","Smart Agricultural Technology","2026-09-10T00:00:00Z",{"impact":18,"substance":328,"depth":110,"authority":432,"freshness":98,"relevant":21,"comment":433},13,"系统综述光学传感驱动的设施作物高通量表型平台，涵盖传感器对比、平台架构与深度学习数据处理演进，方法框架清晰、可迁移性强，对智慧设施农业与精准育种有参考价值。",[435],{"name":429,"url":426},[26,27,28,437,438],"高通量表型","光学传感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":442},{"candidates":443,"agricultural_evidence":45},[444,448,453],{"code":32,"score":56,"supported":45,"title":445,"abstract":446,"tags":447},[],[48,50],[27],{"code":255,"score":98,"supported":45,"title":449,"abstract":450,"tags":452},[],[451,274],"作物表型",[],{"code":63,"score":21,"supported":14,"title":454,"abstract":455,"tags":456},[],[],[26],[458,459],"农业人工智能 高通量表型 光学传感 智慧农业","农业人工智能 高通量表型","农业人工智能高通量表型光学传感智慧农业-2289","10.1016\u002Fj.atech.2026.102545",{"doi":461,"openalex_id":463,"authors":464,"venue":429,"cited_by_count":88,"oa_url":482,"card":483,"direction":313,"ingested_from":95},"W7212130452",[465,467,469,471,473,475,478,480],{"name":466,"orcid":9},"Xiaodong Zhang",{"name":468,"orcid":9},"Zhaowei Li",{"name":470,"orcid":9},"Yi Zhang",{"name":472,"orcid":9},"Chuandong Guo",{"name":474,"orcid":9},"Zonghua Leng",{"name":476,"orcid":477},"Xiangyu Han","https:\u002F\u002Forcid.org\u002F0000-0003-0412-9859",{"name":479,"orcid":9},"Hanping Mao",{"name":481,"orcid":9},"Yixue Zhang","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007707\u002Fpdf",{"tldr":484,"method":485,"finding":486,"direction":313,"opportunity":487},"综述光学传感驱动的设施作物高通量表型平台，涵盖传感器、平台架构与数据处理。","综述RGB、多\u002F高光谱、热红外、LiDAR及固定\u002F轨道\u002F机器人\u002F无人机平台与深度","表型平台正从传统特征工程转向深度学习，并迈向智能闭环决策系统。","多模态数据融合、低成本实时表型平台及闭环决策在设施农业中仍待突破。","2026-09-13T23:30:04.259059Z"]