[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1403":3,"related-1403":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":30,"secondary_topics":36,"classification":37,"search_phrases":60,"slug":63,"view_count":21,"doi":64,"paper":65,"created_at":84},1403,"CFAEVM-UNet: cross-fusion attention enhanced VM-UNet for UAV image segmentation of small and dense field crop pests","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1896392","Precise segmentation of small and dense field crop pests in UAV images remains challenging due to limited long-range dependency modeling in CNNs and quadratic complexity in ViTs. Although VM-UNet captures global context efficiently, it lacks local texture detail and multi-scale feature integration for small targets. To address these limitations, this paper proposes CFAEVM-UNet (CrossFusion Attention Enhanced VM-UNet), which incorporates three core modules: a Visual State Space (VSS) block for efficient long-range dependency modeling, a Local-Global Spatial-Channel Gated Attention (LG-SCGA) module for multi-scale gated attention fusion, and a Multi-Stage Channel-Wise Attention (MSCWA) module for cross-stage channel reweighting. Extensive experiments on an integrated dataset (IP102 subset combined with UAV-captured field images) demonstrate that CFAEVM-UNet achieves state-of-the-art performance, attaining an mIoU of 81.23% and a DSC of 83.67%, outperforming U-Net by 8.89% in mIoU. This work provides an effective and practical solution for automated pest monitoring in precision agriculture.","无人机图像中密集小尺度农作物害虫的精确分割仍面临挑战，原因在于卷积神经网络（CNN）中长距离依赖建模能力有限，以及视觉Transformer（ViT）中的二次复杂度问题。尽管VM-UNet能够高效捕获全局上下文，但其在局部纹理细节和小目标多尺度特征整合方面存在不足。为解决上述局限，本文提出CFAEVM-UNet（CrossFusion Attention Enhanced VM-UNet），该模型融合了三个核心模块：用于高效长距离依赖建模的视觉状态空间（VSS）模块、用于多尺度门控注意力融合的局部-全局空间-通道门控注意力（LG-SCGA）模块，以及用于跨阶段通道重加权的多阶段通道注意力（MSCWA）模块。在综合数据集（IP102子集结合无人机拍摄的田间图像）上进行的大量实验表明，CFAEVM-UNet达到了最先进的性能，其平均交并比（mIoU）为81.23%，Dice相似系数（DSC）为83.67%，相较于U-Net在mIoU上提升了8.89%。本研究为精准农业中的自动化害虫监测提供了一种有效且实用的解决方案。",null,"Frontiers in Plant Science","2026-09-01T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,7,1,"提出针对无人机图像小目标害虫分割的新模型，性能显著提升，对精准农业有实用价值。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","农业人工智能","遥感","病虫害防治",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,50,55],{"code":31,"score":20,"supported":44,"title":45,"abstract":46,"tags":49},true,[],[47,48],"精准农业","precision agriculture",[26],{"code":51,"score":21,"supported":14,"title":52,"abstract":53,"tags":54},"I2",[],[],[27],{"code":56,"score":21,"supported":14,"title":57,"abstract":58,"tags":59},"I3",[],[],[28],[61,62],"农业人工智能 病虫害防治 智慧农业 遥感","农业人工智能 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农业物联网","openalex","2026-09-02T23:30:10.832021Z",{"total":86,"page":21,"page_size":86,"items":87},6,[88,149,200,248,299,348],{"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":103,"tags":105,"primary_topic":108,"secondary_topics":109,"classification":110,"search_phrases":131,"slug":134,"view_count":21,"doi":135,"paper":136,"created_at":148},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","Precision 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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","2026-10-22T00:00:00Z",79,{"impact":97,"substance":157,"depth":17,"authority":100,"freshness":13,"relevant":21,"comment":158},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。",[160],{"name":93,"url":152},[26,27,47,162,163],"机器视觉","作物行检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":167},{"candidates":168,"agricultural_evidence":44},[169,174],{"code":31,"score":170,"supported":44,"title":171,"abstract":172,"tags":173},5,[],[48],[26,47],{"code":51,"score":175,"supported":14,"title":176,"abstract":177,"tags":178},2,[],[],[27,162],[180,181],"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":183,"openalex_id":185,"authors":186,"venue":93,"cited_by_count":74,"oa_url":9,"card":9,"direction":9,"ingested_from":83},"W7223117828",[187,190,193,196],{"name":188,"orcid":189},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":191,"orcid":192},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":194,"orcid":195},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":197,"orcid":198},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":9,"source_name":206,"source_url":203,"published_at":207,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":155,"score_detail":208,"sources":212,"tags":214,"primary_topic":217,"secondary_topics":218,"classification":219,"search_phrases":231,"slug":234,"view_count":74,"doi":235,"paper":236,"created_at":247},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":209,"freshness":210,"relevant":21,"comment":211},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。",[213],{"name":206,"url":203},[26,27,215,47,216],"数字孪生","植物表型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":220},{"candidates":221,"agricultural_evidence":44},[222,227],{"code":31,"score":98,"supported":44,"title":223,"abstract":225,"tags":226},[224],"digital twin",[47,215,48],[215,26,47],{"code":51,"score":21,"supported":14,"title":228,"abstract":229,"tags":230},[],[],[27],[232,233],"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":235,"openalex_id":237,"authors":238,"venue":206,"cited_by_count":74,"oa_url":203,"card":241,"direction":245,"ingested_from":83},"W7222885652",[239],{"name":240,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":242,"method":243,"finding":244,"direction":245,"opportunity":246},"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":249,"title":250,"url":251,"summary":252,"summary_zh":9,"content":9,"source_name":253,"source_url":251,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":254,"score_detail":255,"sources":257,"tags":259,"primary_topic":261,"secondary_topics":262,"classification":263,"search_phrases":279,"slug":282,"view_count":175,"doi":283,"paper":284,"created_at":298},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",67,{"impact":209,"substance":97,"depth":99,"authority":100,"freshness":101,"relevant":21,"comment":256},"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。",[258],{"name":253,"url":251},[26,27,47,260],"生物防治",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":264},{"candidates":265,"agricultural_evidence":44},[266,270,275],{"code":31,"score":19,"supported":44,"title":267,"abstract":268,"tags":269},[48],[48],[26,47],{"code":271,"score":21,"supported":14,"title":272,"abstract":273,"tags":274},"F4",[],[],[260],{"code":51,"score":21,"supported":14,"title":276,"abstract":277,"tags":278},[],[],[27],[280,281],"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":283,"openalex_id":285,"authors":286,"venue":253,"cited_by_count":74,"oa_url":9,"card":9,"direction":82,"ingested_from":83},"W7221746877",[287,290,293,295],{"name":288,"orcid":289},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":291,"orcid":292},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":294,"orcid":9},"İsmail Alper Susurluk",{"name":296,"orcid":297},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":9,"content":9,"source_name":93,"source_url":302,"published_at":304,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":305,"sources":307,"tags":309,"primary_topic":312,"secondary_topics":313,"classification":314,"search_phrases":325,"slug":328,"view_count":21,"doi":329,"paper":330,"created_at":347},4787,"3D modeling for crop applications in precision agriculture: a review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10466-y","3D modeling for crop applications in precision agriculture: a review。Precision Agriculture","2026-10-12T00:00:00Z",{"impact":17,"substance":98,"depth":17,"authority":100,"freshness":101,"relevant":21,"comment":306},"核心期刊发表的作物三维建模综述，方法学价值高，对精准农业与表型研究有参考意义。",[308],{"name":93,"url":302},[26,47,28,310,311],"作物表型","三维建模",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":315},{"candidates":316,"agricultural_evidence":44},[317,321],{"code":31,"score":19,"supported":44,"title":318,"abstract":319,"tags":320},[48],[48],[26,47],{"code":56,"score":175,"supported":14,"title":322,"abstract":323,"tags":324},[],[],[310,28],[326,327],"三维建模 作物表型 智慧农业 精准农业","三维建模 作物表型","三维建模作物表型智慧农业精准农业-4787","10.1007\u002Fs11119-026-10466-y",{"doi":329,"openalex_id":331,"authors":332,"venue":93,"cited_by_count":74,"oa_url":302,"card":9,"direction":82,"ingested_from":83},"W7221570971",[333,336,339,341,344],{"name":334,"orcid":335},"Valeria Maritano","https:\u002F\u002Forcid.org\u002F0000-0002-5844-3184",{"name":337,"orcid":338},"Orou Berme Herve Gonroudobou","https:\u002F\u002Forcid.org\u002F0000-0003-0614-2731",{"name":340,"orcid":9},"Offer Rozenstein",{"name":342,"orcid":343},"Paolo Gay","https:\u002F\u002Forcid.org\u002F0000-0002-8342-425X",{"name":345,"orcid":346},"Lorenzo Comba","https:\u002F\u002Forcid.org\u002F0000-0001-5466-0918","2026-10-13T23:30:02.534333Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":9,"content":9,"source_name":353,"source_url":351,"published_at":354,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":18,"score_detail":355,"sources":358,"tags":360,"primary_topic":362,"secondary_topics":363,"classification":364,"search_phrases":376,"slug":379,"view_count":74,"doi":380,"paper":381,"created_at":398},4659,"Platform for Smart Agriculture -Research and Technological 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":356,"substance":175,"depth":175,"authority":86,"freshness":210,"relevant":21,"comment":357},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。",[359],{"name":353,"url":351},[26,27,361],"科研平台",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":365},{"candidates":366,"agricultural_evidence":44},[367,372],{"code":31,"score":209,"supported":44,"title":368,"abstract":370,"tags":371},[369],"smart agriculture",[369],[26],{"code":51,"score":21,"supported":14,"title":373,"abstract":374,"tags":375},[],[],[27],[377,378],"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":380,"openalex_id":382,"authors":383,"venue":9,"cited_by_count":74,"oa_url":351,"card":9,"direction":82,"ingested_from":83},"W7221337880",[384,386,388,390,392,394,396],{"name":385,"orcid":9},"Ing José Antonio",{"name":387,"orcid":9},"Valles Romero",{"name":389,"orcid":9},"Gerardo Manuel",{"name":391,"orcid":9},"Alonzo Medina",{"name":393,"orcid":9},"Emilio Raymundo Morales Maldonado",{"name":395,"orcid":9},"Ing José",{"name":397,"orcid":9},"Antonio Valles","2026-10-11T23:30:07.700109Z"]