[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3470":3,"related-3470":105},{"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":29,"primary_topic":35,"secondary_topics":41,"classification":42,"search_phrases":74,"slug":77,"view_count":78,"doi":79,"paper":80,"created_at":104},3470,"AI-generated advice as a reinforcement layer in climate-smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.landusepol.2026.108336","Climate-smart agricultural (CSA) practices are central to food-system decarbonisation, yet adoption often falls short of farmers’ stated intentions, weakening the impact of incentives and extension under capacity constraints. We test whether spatially targeted, AI-generated advice can narrow this intention-action gap in a randomised field experiment with 1529 row crop farmers in Iowa, Illinois and Indiana during the cover crop decision window. Farmers assigned to receive four AI-generated emails were 4.45 %age points more likely to plant cover crops than controls (z = 2.81, p = 0.005), despite high baseline intentions in both groups. Effects operated on the extensive margin: there was no detectable change in the share of land planted among adopters. Survey responses indicate high engagement and a shift from untested optimism to more calibrated trust after exposure. Supervised AI advice can provide a low-cost, scalable complement to existing extension, improving follow-through and modestly expanding uptake without displacing human expertise.","气候智慧型农业（CSA）实践是食品系统脱碳的核心，然而在能力受限的情况下，农户的实际采用往往低于其声称的意愿，削弱了激励措施和推广服务的效果。我们在爱荷华州、伊利诺伊州和印第安纳州开展了一项随机田间试验，覆盖1529名大田作物种植户，在覆盖作物决策窗口期测试了空间靶向的AI生成建议能否缩小这一意愿—行动差距。被分配接收四封AI生成电子邮件的农户种植覆盖作物的概率比对照组高4.45个百分点（z = 2.81，p = 0.005），尽管两组基线意愿均较高。效应体现在广延边际上：采用者中种植土地比例未检测到显著变化。调查回复表明参与度较高，且在接触建议后，农户从未经检验的乐观转向更为校准的信任。有监督的AI建议可作为现有推广服务的低成本、可扩展补充，改善后续落实并适度扩大采用，而不会取代人类专业知识。",null,"Land Use Policy","2026-09-24T00:00:00Z","论文",25,false,87,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,19,14,9,1,"随机对照试验证实AI生成建议可低成本缩小农户意愿与行动差距，对智慧农业推广具参考价值。",[25,26],{"name":10,"url":6},{"name":27,"url":28},"Apollo","https:\u002F\u002Fdoi.org\u002F10.17863\u002Fcam.134741",[30,31,32,33,34],"智慧农业","农业人工智能","农业技术推广","覆盖作物","气候智慧农业",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":49},[48,54,59,64,69],{"code":36,"score":21,"supported":49,"title":50,"abstract":52,"tags":53},true,[51],"smart agriculture",[],[30],{"code":55,"score":22,"supported":14,"title":56,"abstract":57,"tags":58},"A7",[],[],[33],{"code":60,"score":22,"supported":14,"title":61,"abstract":62,"tags":63},"G1",[],[],[32],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"H7",[],[],[34],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"I2",[],[],[31],[75,76],"AI生成建议 覆盖作物","爱荷华 伊利诺伊 印第安纳 覆盖作物","AI生成建议覆盖作物-3470",7,"10.1016\u002Fj.landusepol.2026.108336",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":95,"oa_url":6,"card":96,"direction":102,"ingested_from":103},"W7214223143",[83,86,88,90,93],{"name":84,"orcid":85},"Callum Alexander","https:\u002F\u002Forcid.org\u002F0009-0007-5275-4583",{"name":87,"orcid":9},"Aiora Zabala",{"name":89,"orcid":9},"Andreas Kontoleon",{"name":91,"orcid":92},"Shalamar Armstrong","https:\u002F\u002Forcid.org\u002F0000-0002-1326-9936",{"name":94,"orcid":9},"Anuoluwa Sangotayo",0,{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"随机试验检验AI生成建议能否缩小农户覆盖作物种植的意图-行动差距。","1529户美国中西部农户随机对照试验，四次AI生成邮件干预。","AI建议使覆盖作物种植率提高4.45个百分点，效果体现在是否采纳而非种植面积。","农业人工智能与决策模型","可探索AI建议与人工推广协同、长期持续效果及不同作物区域的异质性影响。","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:09.887867Z",{"total":106,"page":22,"page_size":106,"items":107},6,[108,165,213,268,324,374],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":9,"content":9,"source_name":113,"source_url":111,"published_at":114,"category":12,"cover_url":9,"hotness":115,"is_selected":14,"score":116,"score_detail":117,"sources":122,"tags":124,"primary_topic":128,"secondary_topics":129,"classification":130,"search_phrases":144,"slug":147,"view_count":95,"doi":148,"paper":149,"created_at":164},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",10,79,{"impact":118,"substance":119,"depth":120,"authority":20,"freshness":115,"relevant":22,"comment":121},16,21,18,"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。",[123],{"name":113,"url":111},[30,31,125,126,127],"精准农业","机器视觉","作物行检测",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":131},{"candidates":132,"agricultural_evidence":49},[133,139],{"code":36,"score":134,"supported":49,"title":135,"abstract":136,"tags":138},5,[],[137],"precision agriculture",[30,125],{"code":70,"score":140,"supported":14,"title":141,"abstract":142,"tags":143},2,[],[],[31,126],[145,146],"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":148,"openalex_id":150,"authors":151,"venue":113,"cited_by_count":95,"oa_url":9,"card":9,"direction":9,"ingested_from":103},"W7223117828",[152,155,158,161],{"name":153,"orcid":154},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":156,"orcid":157},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":159,"orcid":160},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":162,"orcid":163},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":172,"category":12,"cover_url":9,"hotness":115,"is_selected":14,"score":116,"score_detail":173,"sources":176,"tags":178,"primary_topic":181,"secondary_topics":182,"classification":183,"search_phrases":196,"slug":199,"view_count":95,"doi":200,"paper":201,"created_at":212},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":120,"substance":17,"depth":120,"authority":174,"freshness":21,"relevant":22,"comment":175},12,"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。",[177],{"name":171,"url":168},[30,31,179,125,180],"数字孪生","植物表型",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":184},{"candidates":185,"agricultural_evidence":49},[186,192],{"code":36,"score":187,"supported":49,"title":188,"abstract":190,"tags":191},20,[189],"digital twin",[125,179,137],[179,30,125],{"code":70,"score":22,"supported":14,"title":193,"abstract":194,"tags":195},[],[],[31],[197,198],"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":200,"openalex_id":202,"authors":203,"venue":171,"cited_by_count":95,"oa_url":168,"card":206,"direction":210,"ingested_from":103},"W7222885652",[204],{"name":205,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":207,"method":208,"finding":209,"direction":210,"opportunity":211},"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":214,"title":215,"url":216,"summary":217,"summary_zh":9,"content":9,"source_name":218,"source_url":216,"published_at":219,"category":12,"cover_url":9,"hotness":115,"is_selected":14,"score":220,"score_detail":221,"sources":225,"tags":227,"primary_topic":229,"secondary_topics":230,"classification":231,"search_phrases":248,"slug":251,"view_count":140,"doi":252,"paper":253,"created_at":267},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":174,"substance":118,"depth":222,"authority":20,"freshness":223,"relevant":22,"comment":224},17,8,"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。",[226],{"name":218,"url":216},[30,31,125,228],"生物防治",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":232},{"candidates":233,"agricultural_evidence":49},[234,239,244],{"code":36,"score":235,"supported":49,"title":236,"abstract":237,"tags":238},13,[137],[137],[30,125],{"code":240,"score":22,"supported":14,"title":241,"abstract":242,"tags":243},"F4",[],[],[228],{"code":70,"score":22,"supported":14,"title":245,"abstract":246,"tags":247},[],[],[31],[249,250],"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":252,"openalex_id":254,"authors":255,"venue":218,"cited_by_count":95,"oa_url":9,"card":9,"direction":102,"ingested_from":103},"W7221746877",[256,259,262,264],{"name":257,"orcid":258},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":260,"orcid":261},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":263,"orcid":9},"İsmail Alper Susurluk",{"name":265,"orcid":266},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":9,"content":9,"source_name":113,"source_url":271,"published_at":219,"category":12,"cover_url":9,"hotness":115,"is_selected":14,"score":273,"score_detail":274,"sources":276,"tags":278,"primary_topic":282,"secondary_topics":283,"classification":284,"search_phrases":306,"slug":309,"view_count":22,"doi":310,"paper":311,"created_at":323},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":118,"substance":187,"depth":222,"authority":20,"freshness":223,"relevant":22,"comment":275},"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。",[277],{"name":113,"url":271},[30,279,31,280,281],"无人机","遥感","大麦",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":285},{"candidates":286,"agricultural_evidence":49},[287,292,297,301],{"code":36,"score":288,"supported":49,"title":289,"abstract":290,"tags":291},4,[],[137],[30],{"code":293,"score":22,"supported":14,"title":294,"abstract":295,"tags":296},"A5",[],[],[281],{"code":70,"score":22,"supported":14,"title":298,"abstract":299,"tags":300},[],[],[31],{"code":302,"score":22,"supported":14,"title":303,"abstract":304,"tags":305},"I3",[],[],[280],[307,308],"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":332,"substance":140,"depth":140,"authority":106,"freshness":21,"relevant":22,"comment":333},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。",[335],{"name":329,"url":327},[30,31,337],"科研平台",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":341},{"candidates":342,"agricultural_evidence":49},[343,347],{"code":36,"score":174,"supported":49,"title":344,"abstract":345,"tags":346},[51],[51],[30],{"code":70,"score":22,"supported":14,"title":348,"abstract":349,"tags":350},[],[],[31],[352,353],"农业人工智能 智慧农业 科研平台","农业人工智能 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Climate-Smart Agriculture: A Case Study of Weather Forecasting in Uganda","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23283885","The Role of Indigenous Knowledge in Climate-Smart Agriculture: A Case Study of Weather Forecasting in Uganda。Zenodo (CERN European Organization for Nuclear Research)","本土知识在气候智慧型农业中的作用：乌干达天气预报案例研究。Zenodo（CERN欧洲核子研究组织）",{"impact":174,"substance":120,"depth":118,"authority":235,"freshness":223,"relevant":22,"comment":381},"非洲本土知识与气候智慧农业结合的案例研究，对国内农业气象信息化有借鉴价值，但地域相关性有限。",[383],{"name":171,"url":377},[30,34,385,386,387],"乌干达","传统知识","气象预报",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":391},{"candidates":392,"agricultural_evidence":49},[393,397],{"code":36,"score":174,"supported":49,"title":394,"abstract":395,"tags":396},[51],[51],[30],{"code":65,"score":22,"supported":14,"title":398,"abstract":399,"tags":400},[],[],[34],[402,403],"乌干达 气象预报 传统知识","气候智慧农业 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