[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3569":3,"related-3569":85},{"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":58,"slug":61,"view_count":62,"doi":63,"paper":64,"created_at":84},3569,"Efficient and explainable attention-based multitask learning for fruit type, ripeness, and disease classification","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2108","Precision agriculture increasingly relies on automated crop monitoring to support disease prevention and harvest decisions. However, most existing systems treat fruit type, ripeness, and disease classification as separate tasks, leading to high computational costs in real-world deployments. This paper proposes an efficient and explainable multitask learning (MTL) model for fruit monitoring that performs all three tasks simultaneously. The MTL model uses a shared lightweight MobileNetV2 backbone with a convolutional block attention module (CBAM) to extract both shared and discriminative features. Dedicated classification heads then utilize these features to generate predictions for each task. To address the scarcity of fully annotated agricultural datasets, we combine multiple public datasets and apply a partial label-masking strategy to handle missing annotations. In addition, the proposed model is evaluated using several backbone architectures, including ResNet-50, EfficientNetV2-M, and ViT-B\u002F16, under both single optimizer and separate optimizers. Experimental results demonstrate that the proposed MTL model achieves 99.39% (fruit type), 96.35% (ripeness), and 99.7% (disease) accuracy, while using only 5.5M parameters, outperforming single-task solutions. Furthermore, gradient-based explainability analysis provides transparency into the model's decision-making process.","精准农业日益依赖自动化作物监测，以支持病害预防和收获决策。然而，现有大多数系统将水果类型、成熟度和病害分类视为独立任务，导致实际部署中的计算成本较高。本文提出了一种高效且可解释的多任务学习（MTL）模型用于水果监测，可同时执行上述三项任务。该MTL模型采用共享的轻量级MobileNetV2主干网络，并结合卷积块注意力模块（CBAM）以提取共享特征和判别特征。随后，专用分类头利用这些特征为每个任务生成预测。为应对完全标注农业数据集稀缺的问题，我们整合了多个公开数据集，并采用部分标签掩码策略来处理缺失标注。此外，我们在单一优化器和独立优化器两种设置下，使用包括ResNet-50、EfficientNetV2-M和ViT-B\u002F16在内的多种主干架构对所提模型进行了评估。实验结果表明，所提MTL模型在仅使用5.5M参数的情况下，分别达到了99.39%（水果类型）、96.35%（成熟度）和99.7%（病害）的准确率，优于单任务方案。此外，基于梯度的可解释性分析为模型的决策过程提供了透明度。",null,"Journal of Agricultural Engineering","2026-09-25T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"提出轻量可解释多任务模型，同时识别水果种类、成熟度与病害，精度高、参数少，对智慧果园部署有实用价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","病害识别","多任务学习","水果检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},7,true,[],[49,50],"精准农业","precision agriculture",[26],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I2",2,[],[],[27,28],[59,60],"MobileNetV2 CBAM 水果分类","多任务学习 果实成熟度 病害","MobileNetV2CBAM水果分类-3569",4,"10.4081\u002Fjae.2026.2108",{"doi":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":76,"oa_url":6,"card":77,"direction":81,"ingested_from":83},"W7214312987",[67,70,73],{"name":68,"orcid":69},"Rida El Chall","https:\u002F\u002Forcid.org\u002F0000-0002-7620-7767",{"name":71,"orcid":72},"Sarah Alayan","https:\u002F\u002Forcid.org\u002F0009-0007-5706-4688",{"name":74,"orcid":75},"Abed Ellatif Samhat","https:\u002F\u002Forcid.org\u002F0000-0002-1137-621X",0,{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"提出轻量多任务模型，同时分类水果种类、成熟度和病害，并具可解释性。","MobileNetV2+CBAM共享主干，多数据集合并与部分标签掩码。","三任务准确率达99.39%、96.35%、99.7%，仅5.5M参数，优于单任务。","农业人工智能与决策模型","可探索多任务模型在田间实时部署与跨作物泛化，结合弱监督降低标注成本。","openalex","2026-09-26T23:30:57.555191Z",{"total":86,"page":21,"page_size":86,"items":87},6,[88,140,189,244,299,350],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":9,"content":9,"source_name":93,"source_url":91,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":95,"score_detail":96,"sources":100,"tags":102,"primary_topic":105,"secondary_topics":106,"classification":107,"search_phrases":119,"slug":122,"view_count":76,"doi":123,"paper":124,"created_at":139},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":97,"substance":98,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":99},16,21,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[101],{"name":93,"url":91},[26,27,49,103,104],"机器视觉","作物行检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":108},{"candidates":109,"agricultural_evidence":46},[110,115],{"code":32,"score":111,"supported":46,"title":112,"abstract":113,"tags":114},5,[],[50],[26,49],{"code":53,"score":54,"supported":14,"title":116,"abstract":117,"tags":118},[],[],[27,103],[120,121],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":123,"openalex_id":125,"authors":126,"venue":93,"cited_by_count":76,"oa_url":9,"card":9,"direction":9,"ingested_from":83},"W7223117828",[127,130,133,136],{"name":128,"orcid":129},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":131,"orcid":132},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":134,"orcid":135},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":137,"orcid":138},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":141,"title":142,"url":143,"summary":144,"summary_zh":145,"content":9,"source_name":146,"source_url":143,"published_at":147,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":95,"score_detail":148,"sources":152,"tags":154,"primary_topic":157,"secondary_topics":158,"classification":159,"search_phrases":172,"slug":175,"view_count":76,"doi":176,"paper":177,"created_at":188},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":149,"freshness":150,"relevant":21,"comment":151},12,9,"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[153],{"name":146,"url":143},[26,27,155,49,156],"数字孪生","植物表型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":160},{"candidates":161,"agricultural_evidence":46},[162,168],{"code":32,"score":163,"supported":46,"title":164,"abstract":166,"tags":167},20,[165],"digital twin",[49,155,50],[155,26,49],{"code":53,"score":21,"supported":14,"title":169,"abstract":170,"tags":171},[],[],[27],[173,174],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":176,"openalex_id":178,"authors":179,"venue":146,"cited_by_count":76,"oa_url":143,"card":182,"direction":186,"ingested_from":83},"W7222885652",[180],{"name":181,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","农业遥感与作物表型","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":9,"content":9,"source_name":194,"source_url":192,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":200,"tags":202,"primary_topic":204,"secondary_topics":205,"classification":206,"search_phrases":223,"slug":226,"view_count":54,"doi":227,"paper":228,"created_at":243},5082,"Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jip.2026.108765","Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives。Journal of Invertebrate Pathology","Journal of Invertebrate Pathology","2026-10-14T00:00:00Z",67,{"impact":149,"substance":97,"depth":198,"authority":19,"freshness":20,"relevant":21,"comment":199},17,"核心期刊综述，聚焦昆虫病原线虫在精准农业中的应用方法与挑战，专业性强但属细分领域，产业影响有限。",[201],{"name":194,"url":192},[26,27,49,203],"生物防治",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":207},{"candidates":208,"agricultural_evidence":46},[209,214,219],{"code":32,"score":210,"supported":46,"title":211,"abstract":212,"tags":213},13,[50],[50],[26,49],{"code":215,"score":21,"supported":14,"title":216,"abstract":217,"tags":218},"F4",[],[],[203],{"code":53,"score":21,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[27],[224,225],"昆虫病原线虫 精准农业","Journal of Invertebrate Pathology 线虫","昆虫病原线虫精准农业-5082","10.1016\u002Fj.jip.2026.108765",{"doi":227,"openalex_id":229,"authors":230,"venue":194,"cited_by_count":76,"oa_url":9,"card":9,"direction":242,"ingested_from":83},"W7221746877",[231,234,237,239],{"name":232,"orcid":233},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":235,"orcid":236},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":238,"orcid":9},"İsmail Alper Susurluk",{"name":240,"orcid":241},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","智慧农业 \u002F 农业物联网","2026-10-16T23:30:09.556034Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":9,"content":9,"source_name":93,"source_url":247,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":252,"tags":254,"primary_topic":258,"secondary_topics":259,"classification":260,"search_phrases":281,"slug":284,"view_count":21,"doi":285,"paper":286,"created_at":298},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture",75,{"impact":97,"substance":163,"depth":198,"authority":19,"freshness":20,"relevant":21,"comment":251},"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[253],{"name":93,"url":247},[26,255,27,256,257],"无人机","遥感","大麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":261},{"candidates":262,"agricultural_evidence":46},[263,267,272,276],{"code":32,"score":62,"supported":46,"title":264,"abstract":265,"tags":266},[],[50],[26],{"code":268,"score":21,"supported":14,"title":269,"abstract":270,"tags":271},"A5",[],[],[257],{"code":53,"score":21,"supported":14,"title":273,"abstract":274,"tags":275},[],[],[27],{"code":277,"score":21,"supported":14,"title":278,"abstract":279,"tags":280},"I3",[],[],[256],[282,283],"无人机 大麦 穗检测","YOLOv10 大麦 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Innovation PlaSmA Research Group (PRG) Platform for Smart Agriculture -Research and Technological Innovation","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22742.61769","Platform for Smart Agriculture -Research and Technological Innovation PlaSmA Research Group (PRG) Platform for Smart Agriculture -Research and Technological Innovation。OpenAlex","OpenAlex","2026-10-10T00:00:00Z",{"impact":307,"substance":54,"depth":54,"authority":86,"freshness":150,"relevant":21,"comment":308},3,"仅有标题与来源、无实质摘要内容的论文条目，信息增量极低，不建议进入每日精选。",[310],{"name":304,"url":302},[26,27,312],"科研平台",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":316},{"candidates":317,"agricultural_evidence":46},[318,323],{"code":32,"score":149,"supported":46,"title":319,"abstract":321,"tags":322},[320],"smart agriculture",[320],[26],{"code":53,"score":21,"supported":14,"title":324,"abstract":325,"tags":326},[],[],[27],[328,329],"农业人工智能 智慧农业 科研平台","农业人工智能 智慧农业","农业人工智能智慧农业科研平台-4659","10.13140\u002Frg.2.2.22742.61769",{"doi":331,"openalex_id":333,"authors":334,"venue":9,"cited_by_count":76,"oa_url":302,"card":9,"direction":242,"ingested_from":83},"W7221337880",[335,337,339,341,343,345,347],{"name":336,"orcid":9},"Ing José Antonio",{"name":338,"orcid":9},"Valles Romero",{"name":340,"orcid":9},"Gerardo Manuel",{"name":342,"orcid":9},"Alonzo Medina",{"name":344,"orcid":9},"Emilio Raymundo Morales Maldonado",{"name":346,"orcid":9},"Ing José",{"name":348,"orcid":9},"Antonio Valles","2026-10-11T23:30:07.700109Z",{"id":351,"title":352,"url":353,"summary":354,"summary_zh":9,"content":9,"source_name":355,"source_url":9,"published_at":356,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":357,"score_detail":358,"sources":360,"tags":362,"primary_topic":365,"secondary_topics":366,"classification":367,"search_phrases":383,"slug":386,"view_count":21,"doi":9,"paper":387,"created_at":395},4647,"[预印本]植物病害数据集的数据中心综述:分类、关键分析、环境变异性与精准农业启示(A Data-Centric Review of Plant Disease Datasets: Taxonomy, Critical Analysis, Environmental Variability, and Implications for Precision Agriculture)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.07087","arXiv综述(2610.07087,2026年10月5日提交)从数据采集、可访问性、植物多样性、病害构成、类别结构与不平衡严重度建立植物病害数据集分类法,对比实验室与田间数据集差异,分析类内、跨作物、跨数据集多层次不平衡及环境变异性对模型性能的影响,提出融合温湿度、叶面湿润度等多模态环境参数的方向。[预印本未经同行评议]。","arXiv 2026-10-05 [预印本]","2026-10-05T00:00:00Z",71,{"impact":19,"substance":98,"depth":17,"authority":150,"freshness":150,"relevant":21,"comment":359},"arXiv预印本系统梳理植物病害数据集分类与不平衡问题，对农业AI数据建设有参考价值，但未经同行评议且偏学术，适合主题聚合而非头条精选。",[361],{"name":355,"url":353},[26,27,49,363,364],"植物病害","农业数据集",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":368},{"candidates":369,"agricultural_evidence":46},[370,374,378],{"code":32,"score":17,"supported":46,"title":371,"abstract":372,"tags":373},[49,50],[],[26,49],{"code":53,"score":21,"supported":14,"title":375,"abstract":376,"tags":377},[],[],[27],{"code":379,"score":21,"supported":14,"title":380,"abstract":381,"tags":382},"I6",[],[],[364],[384,385],"植物病害 环境变异性 精准农业","农业人工智能 农业数据集 智慧农业 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