[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1823":3,"related-1823":114},{"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":44,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":113},1823,"Integrating a pesticide risk indicator into a multi-criteria assessment tool for comparing crop protection scenarios","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104945","CONTEXT Chemical pesticides remain central to agricultural productivity but pose well documented risks to human health and ecosystems. While Integrated Pest Management (IPM) offers a more sustainable alternative, its adoption remains limited, partly due to the complex trade-offs that are not easily quantified and compared. OBJECTIVE This study presents the IPM Self-Assessment Tool (ISAT), a web-based application designed to enable transparent comparison of crop protection scenarios at the field level, integrating environmental, human health, agronomic, and economic indicators. METHODS A new harmonized pesticide risk indicator for Europe (PL EU – Pesticide Load Indicator for Europe) was developed. The PL EU covers 41 hazard metrics across three sub-indicators (Environmental Fate, Ecotoxicity, and Human Health). The PL EU was integrated into the ISAT alongside indicators for greenhouse gas (GHG) emissions and production costs, calculated using life cycle based methods. The tool was developed using data from ten European countries covering eight crops, with scenarios described by regional specialists and validated by farm advisors. RESULTS AND CONCLUSIONS The PL EU was calculated for 502 active ingredients (441 synthetic, 61 biopesticides) across eight pesticide groups. A case study on winter wheat in Germany showed that, relative to conventional practice, an IPM scenario replacing herbicide use by mechanical weeding reduced pesticide risk by up to 75%, but at the cost of GHG emissions comparable to the conventional scenario. The ISAT enables users to identify risk-driving active ingredients and explore lower-risk alternatives, supporting informed decision-making for IPM adoption. SIGNIFICANCE To our knowledge, ISAT is the first freely accessible, web-based tool integrating a harmonized pesticide risk indicator with GHG emissions and economic outcomes within a single, comprehensive field level scenario comparison. It provides a transparent decision-support platform that can help farmers, advisors, and researchers make informed decisions when designing and comparing crop protection strategies across diverse European cropping systems.","化学农药仍是农业生产力的核心，但对人类健康和生态系统构成的风险已有充分记录。尽管有害生物综合治理（IPM）提供了更具可持续性的替代方案，但其采用率仍然有限，部分原因在于复杂的权衡关系难以量化和比较。目的 本研究介绍了IPM自我评估工具（ISAT），这是一个基于网络的应用程序，旨在实现田间层面作物保护方案的透明比较，整合环境、人类健康、农学和经济指标。方法 开发了欧洲统一农药风险指标（PL EU——欧洲农药负荷指标）。PL EU涵盖三个子指标（环境归趋、生态毒性和人类健康）下的41项危害指标。PL EU被整合到ISAT中，同时还包括基于生命周期方法计算的温室气体（GHG）排放和生产成本指标。该工具利用覆盖八个作物、来自十个欧洲国家的数据开发，情景由区域专家描述并由农场顾问验证。结果与结论 针对八个农药类别中的502种有效成分（441种合成农药，61种生物农药）计算了PL EU。德国冬小麦案例研究表明，相对于常规实践，用机械除草替代除草剂使用的IPM情景可将农药风险降低高达75%，但代价是温室气体排放与常规情景相当。ISAT使用户能够识别风险驱动型有效成分并探索低风险替代方案，为IPM采用提供知情决策支持。意义 据我们所知，ISAT是首个免费访问的基于网络的工具，将统一的农药风险指标与温室气体排放和经济结果整合在单一、全面的田间层面情景比较中。它提供了一个透明的决策支持平台，可帮助农民、顾问和研究人员在设计和比较欧洲多样化种植制度中的作物保护策略时做出明智决策。",null,"Agricultural Systems","2026-09-06T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,7,1,"论文提出首个整合农药风险、温室气体与成本的田间级评估工具，数据覆盖广，对IPM决策有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","决策支持","欧洲","农药风险","作物保护",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,60,66,74],{"code":32,"score":51,"supported":52,"title":53,"abstract":56,"tags":59},25,true,[54,55],"pesticide","crop protection",[57,58,54,55],"农药","除草",[],{"code":39,"score":61,"supported":52,"title":62,"abstract":63,"tags":65},9,[],[41,64,40],"winter wheat",[],{"code":67,"score":68,"supported":52,"title":69,"abstract":70,"tags":73},"B7",6,[],[71,72],"温室","greenhouse",[],{"code":75,"score":76,"supported":14,"title":77,"abstract":78,"tags":79},"I2",2,[],[],[26,27],[81,82],"农业人工智能 作物保护 农药风险 决策支持","农业人工智能 作物保护","农业人工智能作物保护农药风险决策支持-1823",3,"10.1016\u002Fj.agsy.2026.104945",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":104,"oa_url":105,"card":106,"direction":110,"ingested_from":112},"W7168097222",[89,91,93,96,99,102],{"name":90,"orcid":9},"Vera Krause",{"name":92,"orcid":9},"Jörn Strassemeyer",{"name":94,"orcid":95},"Virginia Nichols","https:\u002F\u002Forcid.org\u002F0000-0003-1850-8243",{"name":97,"orcid":98},"Per Kudsk","https:\u002F\u002Forcid.org\u002F0000-0003-2431-3610",{"name":100,"orcid":101},"Noé Vandevoorde","https:\u002F\u002Forcid.org\u002F0000-0003-1959-7524",{"name":103,"orcid":9},"Daniel Daehmlow",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0308521X26003136\u002Fpdf",{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"开发了IPM自评估工具ISAT，集成新的农药风险指标PL EU，比较作物保护情景的环境、健康和经济影","开发欧洲统一农药风险指标PL EU，覆盖41种危害指标，集成到网络工具ISAT中","德国冬小麦案例中，IPM机械除草替代除草剂可降低农药风险75%，但温室气体排放与传统相当。","农业人工智能与决策模型","可扩展ISAT至更多作物和地区，或开发更精细的生态毒理模型，并考虑生物农药的长期风险。","openalex","2026-09-07T23:30:05.419008Z",{"total":68,"page":21,"page_size":68,"items":115},[116,246,306,359,407,459],{"id":117,"title":118,"url":119,"summary":120,"summary_zh":121,"content":9,"source_name":122,"source_url":119,"published_at":123,"category":12,"cover_url":9,"hotness":13,"is_selected":52,"score":124,"score_detail":125,"sources":130,"tags":132,"primary_topic":139,"secondary_topics":140,"classification":142,"search_phrases":198,"slug":201,"view_count":76,"doi":202,"paper":203,"created_at":245},4083,"Wheat Rust Diseases: Global Impact, Integrated Disease Management, and Emerging Biotechnological Innovations for Sustainable Crop Protection","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fbiology15191756","Wheat (Triticum aestivum L.) remains one of the most important staple crops worldwide and serves as a primary source of calories and protein for billions of people. Global wheat production, however, continues to be threatened by rust diseases caused by Puccinia triticina, Puccinia striiformis f. sp. tritici and Puccinia graminis f. sp. tritici. These pathogens are highly adaptive and frequently give rise to new virulent races, causing resistance in wheat cultivars to breakdown. Under favorable environmental conditions, severe epidemics can develop and substantial yield losses may occur. In response to these challenges, traditional management approaches such as resistant cultivars, crop rotation, sanitation and fungicide application have long been employed in wheat production systems. Yet these strategies have increasingly been constrained by resistance breakdown, genetic uniformity in modern cultivars, and climate-driven changes in pathogen distribution. Conventional management practices are evaluated alongside emerging technological solutions including genomic-assisted breeding, gene pyramiding, CRISPR-based genome editing, biological control agents and precision agriculture tools. In addition, nanotechnology has emerged as a promising tool in plant disease management, where nanoparticles such as silver, copper and zinc oxide exhibit strong antifungal activity and enable targeted delivery of agrochemicals. Nano-enabled formulations can enhance fungicide efficiency, reduce chemical inputs and improve plant defense responses, offering a sustainable complement to existing wheat rust management strategies. Particular attention is given to integrated disease management strategies, which combine traditional agronomic practices with modern surveillance technologies such as remote sensing, artificial intelligence-based forecasting models, and pathogen genomics. These combined approaches can improve resistance durability and build more resilient wheat production systems. Future directions emphasize climate adaptation, global surveillance and collaborative breeding. In an era of increasing climatic uncertainty and evolving pathogen populations, durable rust management will depend on coordinated global efforts that align scientific innovation, farmer adoption, and policy support to safeguard wheat productivity and global food security.","小麦（Triticum aestivum L.）仍是全球最重要的主粮作物之一，是数十亿人热量和蛋白质的主要来源。然而，全球小麦生产持续受到由小麦叶锈菌（Puccinia triticina）、小麦条锈菌（Puccinia striiformis f. sp. tritici）和小麦秆锈菌（Puccinia graminis f. sp. tritici）引起的锈病威胁。这些病原菌适应性极强，频繁产生新的毒性小种，导致小麦品种抗性丧失。在有利的环境条件下，病害可严重流行并造成大幅产量损失。为应对这些挑战，小麦生产体系中长期采用抗病品种、轮作、田园卫生和杀菌剂施用等传统管理措施。然而，这些策略日益受到抗性丧失、现代品种遗传单一化以及气候驱动的病原菌分布变化的制约。本文在评估传统管理实践的同时，也探讨了新兴技术解决方案，包括基因组辅助育种、基因聚合、基于CRISPR的基因组编辑、生物防治制剂和精准农业工具。此外，纳米技术已成为植物病害管理中有前景的工具，其中银、铜和氧化锌等纳米颗粒表现出强抗真菌活性，并能够实现农化品的靶向递送。纳米制剂可提高杀菌剂效率、减少化学投入并改善植物防御反应，为现有小麦锈病管理策略提供了可持续的补充。本文特别关注病害综合管理策略，即将传统农艺措施与遥感、基于人工智能的预测模型和病原菌基因组学等现代监测技术相结合。这些综合方法可提高抗性持久性并构建更具韧性的小麦生产体系。未来方向强调气候适应、全球监测和协作育种。在气候不确定性日益加剧和病原菌群体不断演化的时代，持久的锈病管理将依赖于协调一致的全球努力，将科学创新、农民采纳和政策支持相结合，以保障小麦生产和全球粮食安全。","Biology","2026-10-02T00:00:00Z",82,{"impact":18,"substance":126,"depth":17,"authority":127,"freshness":128,"relevant":21,"comment":129},21,13,8,"系统综述小麦锈病全球影响与基因组育种、纳米农药、遥感与AI预测等前沿防控手段，专业性强，对保障粮食安全具参考价值。",[131],{"name":122,"url":119},[133,134,135,136,137,138],"智慧农业","粮食安全","基因编辑","纳米农药","遥感监测","小麦锈病",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[141],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":143},{"candidates":144,"agricultural_evidence":52},[145,149,155,161,168,175,182,188,193],{"code":39,"score":19,"supported":52,"title":146,"abstract":147,"tags":148},[40],[41,40],[],{"code":32,"score":150,"supported":52,"title":151,"abstract":152,"tags":154},12,[55],[153],"生物防治",[136],{"code":156,"score":20,"supported":52,"title":157,"abstract":158,"tags":160},"A8",[],[134,159],"food security",[134],{"code":162,"score":20,"supported":52,"title":163,"abstract":164,"tags":167},"I1",[],[165,166],"精准农业","precision agriculture",[133],{"code":169,"score":20,"supported":52,"title":170,"abstract":171,"tags":174},"I3",[],[172,173],"遥感","remote sensing",[137],{"code":176,"score":68,"supported":52,"title":177,"abstract":178,"tags":181},"A7",[],[179,180],"轮作","crop rotation",[],{"code":183,"score":84,"supported":52,"title":184,"abstract":185,"tags":187},"H7",[],[186],"气候适应",[],{"code":75,"score":84,"supported":52,"title":189,"abstract":190,"tags":192},[],[191],"artificial intelligence",[],{"code":194,"score":21,"supported":14,"title":195,"abstract":196,"tags":197},"F2",[],[],[135],[199,200],"小麦锈病 综合防控","Puccinia triticina 抗性育种","小麦锈病综合防控-4083","10.3390\u002Fbiology15191756",{"doi":202,"openalex_id":204,"authors":205,"venue":122,"cited_by_count":104,"oa_url":119,"card":239,"direction":244,"ingested_from":112},"W7215995869",[206,209,212,215,218,221,223,225,227,230,233,236],{"name":207,"orcid":208},"Mingzheng Duan","https:\u002F\u002Forcid.org\u002F0000-0002-3486-024X",{"name":210,"orcid":211},"Muhammad Asif Shabbir","https:\u002F\u002Forcid.org\u002F0009-0002-5452-5040",{"name":213,"orcid":214},"Mustansar Mubeen","https:\u002F\u002Forcid.org\u002F0009-0003-9231-9560",{"name":216,"orcid":217},"Yasir Iftikhar","https:\u002F\u002Forcid.org\u002F0000-0002-4182-8969",{"name":219,"orcid":220},"Naureen Anwar","https:\u002F\u002Forcid.org\u002F0000-0003-1254-1720",{"name":222,"orcid":9},"Shiping Jiang",{"name":224,"orcid":9},"Zhifang He",{"name":226,"orcid":9},"Sirui Han",{"name":228,"orcid":229},"Ahmed Mahmoud Ismail","https:\u002F\u002Forcid.org\u002F0000-0001-9679-640X",{"name":231,"orcid":232},"Khalid Mushabab Al Syaad","https:\u002F\u002Forcid.org\u002F0000-0002-2372-9975",{"name":234,"orcid":235},"Ahmed Ezzat Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-4532-0636",{"name":237,"orcid":238},"Muhammad Umer","https:\u002F\u002Forcid.org\u002F0000-0002-3957-0196",{"tldr":240,"method":241,"finding":242,"direction":110,"opportunity":243},"综述小麦锈病全球影响与综合防控，并展望基因编辑、纳米技术、遥感与AI预测等新兴手段。","文献综述，整合抗病育种、CRISPR、纳米制剂、遥感与AI预测模型。","综合传统农艺与现代监测技术可提升抗性持久性，纳米与基因编辑是可持续补充。","可探索遥感与AI预测模型同病原基因组数据融合的实时预警系统，并评估纳米制剂田间长期生态效应。","农业遥感与作物表型","2026-10-03T23:30:32.282761Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":9,"content":9,"source_name":251,"source_url":249,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":257,"tags":259,"primary_topic":261,"secondary_topics":262,"classification":268,"search_phrases":289,"slug":292,"view_count":84,"doi":293,"paper":294,"created_at":305},3807,"Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10698325\u002Fv1","Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift。Research Square","Research Square","2026-09-28T00:00:00Z",52,{"impact":128,"substance":19,"depth":255,"authority":68,"freshness":61,"relevant":21,"comment":256},15,"预印本论文，聚焦小麦产量决策支持系统的可靠性审计与时空迁移问题，方法有一定新意但尚未经同行评审，公共影响有限。",[258],{"name":251,"url":249},[133,26,27,137,260],"小麦产量",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[263],{"code":75,"slug":264,"name":265,"path":266,"parent_code":267},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":269},{"candidates":270,"agricultural_evidence":52},[271,276,281,285],{"code":75,"score":127,"supported":52,"title":272,"abstract":274,"tags":275},[273],"decision support",[273],[26,27],{"code":39,"score":277,"supported":52,"title":278,"abstract":279,"tags":280},11,[40],[40],[],{"code":162,"score":21,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[133],{"code":169,"score":21,"supported":14,"title":286,"abstract":287,"tags":288},[],[],[137],[290,291],"小麦产量 决策支持 遥感","Research Square 小麦 产量预测","小麦产量决策支持遥感-3807","10.21203\u002Frs.3.rs-10698325\u002Fv1",{"doi":293,"openalex_id":295,"authors":296,"venue":251,"cited_by_count":104,"oa_url":249,"card":9,"direction":110,"ingested_from":112},"W7214624863",[297,300,303],{"name":298,"orcid":299},"Xin Li","https:\u002F\u002Forcid.org\u002F0009-0005-0670-5006",{"name":301,"orcid":302},"Bojian Guo","https:\u002F\u002Forcid.org\u002F0009-0005-8353-2436",{"name":304,"orcid":9},"Asel Kartanova","2026-09-29T23:31:02.997580Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":9,"content":9,"source_name":311,"source_url":9,"published_at":312,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":313,"score_detail":314,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":324,"classification":325,"search_phrases":346,"slug":349,"view_count":104,"doi":9,"paper":350,"created_at":358},5365,"基于经济型多光谱成像与知识蒸馏模型压缩的小麦线条花叶病毒早期检测","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-10\u002F20261017000601916.htm","研究人员利用配备六个光谱滤光片的消费级数码单反(DSLR)相机在自然环境条件下获取了一个新的小麦线条花叶病毒(WhSMV)多光谱数据集。评估十二个深度学习模型识别 WhSMV 感染不同阶段的性能,并首次将模型压缩技术应用于多光谱图像用于 WhSMV 感染的早期检测。比较了深度学习算法与压缩模型的性能,揭示压缩模型达到与大型深度学习模型相当的性能。WhSMV 是小麦土传媒介传播病毒,巴西与南非均有报道。","Smart Agricultural Technology 2026-10-17","2026-10-17T00:00:00Z",78,{"impact":17,"substance":18,"depth":315,"authority":127,"freshness":128,"relevant":21,"comment":316},17,"该研究构建了新的多光谱数据集，并首次将模型压缩用于小麦病毒早期检测，方法新颖、结论可靠，对智慧农业植保具有参考价值。",[318],{"name":311,"url":309},[133,26,320,321,322],"多光谱成像","小麦病害","模型压缩",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":326},{"candidates":327,"agricultural_evidence":52},[328,332,338,342],{"code":39,"score":277,"supported":52,"title":329,"abstract":330,"tags":331},[41],[41],[],{"code":75,"score":333,"supported":52,"title":334,"abstract":335,"tags":337},4,[],[336],"深度学习",[26],{"code":162,"score":21,"supported":14,"title":339,"abstract":340,"tags":341},[],[],[133],{"code":169,"score":21,"supported":14,"title":343,"abstract":344,"tags":345},[],[],[320],[347,348],"小麦线条花叶病毒 多光谱 检测","知识蒸馏 模型压缩 小麦","小麦线条花叶病毒多光谱检测-5365",{"doi":9,"openalex_id":9,"authors":351,"venue":9,"cited_by_count":104,"oa_url":9,"card":352,"direction":244,"ingested_from":357},[],{"tldr":353,"method":354,"finding":355,"direction":244,"opportunity":356},"用经济型多光谱成像结合知识蒸馏压缩模型，实现小麦线条花叶病毒早期检测。","消费级单反加六波段滤光片采集多光谱数据，评估12个深度学习模型并做模型压缩。","压缩模型性能与大型深度学习模型相当，可用于WhSMV感染早期识别。","可探索低成本多光谱设备与轻量模型在田间病害早期预警中的部署与跨区域泛化。","agent","2026-10-20T02:47:54.487590Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":9,"content":9,"source_name":364,"source_url":9,"published_at":365,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":366,"sources":368,"tags":370,"primary_topic":374,"secondary_topics":375,"classification":376,"search_phrases":395,"slug":398,"view_count":104,"doi":9,"paper":399,"created_at":406},5362,"深度语义分割模型的时序泛化评估:无人机小麦倒伏监测 5 年跨年测试","https:\u002F\u002Fbioengineer.org\u002Fai-models-trained-on-one-year-of-wheat-fail-the-next-five-year-drone-study-shows\u002F","Zhang G., Wang X., Wang D., Sun D., Zhang Y., Zhang D. (2026) 利用 5 年田间试验评估深度语义分割模型在无人机(UAV)小麦倒伏监测中的时序泛化能力。研究发现:同年内高精度不能保证跨年可靠性,模型从训练年份迁移到其他年份时性能显著下降,基于注意力的模型通常比全卷积网络更稳健,可能因为层级窗口自注意力能整合更长范围上下文,而卷积强调局部模式;即便最优架构在大时序漂移下也出现严重退化。强调时序泛化测试应成为农业 AI 模型部署前的必备环节。DOI: 10.1016\u002Fj.atech.2026.102631。","Smart Agricultural Technology Vol.15 Article 102631 2026-10-16","2026-10-16T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":127,"freshness":128,"relevant":21,"comment":367},"基于5年跨年田间试验揭示农业AI模型时序泛化瓶颈，结论扎实且具部署指导价值，值得入选。",[369],{"name":364,"url":362},[133,26,371,372,373],"无人机遥感","小麦倒伏","模型泛化",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":377},{"candidates":378,"agricultural_evidence":52},[379,383,387,391],{"code":39,"score":277,"supported":52,"title":380,"abstract":381,"tags":382},[41],[41],[],{"code":162,"score":21,"supported":14,"title":384,"abstract":385,"tags":386},[],[],[133],{"code":75,"score":21,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[26],{"code":169,"score":21,"supported":14,"title":392,"abstract":393,"tags":394},[],[],[371],[396,397],"无人机 小麦倒伏 监测","深度语义分割 时序泛化","无人机小麦倒伏监测-5362",{"doi":9,"openalex_id":9,"authors":400,"venue":9,"cited_by_count":104,"oa_url":9,"card":401,"direction":244,"ingested_from":357},[],{"tldr":402,"method":403,"finding":404,"direction":244,"opportunity":405},"用5年田间试验评估深度语义分割模型在无人机小麦倒伏监测中的跨年泛化能力。","5年无人机小麦倒伏影像，对比注意力模型与全卷积网络的跨年迁移表现。","同年内高精度不能保证跨年可靠，注意力模型比全卷积网络更稳健，但大时序漂移下仍严重退化。","可研究时序域自适应与跨年增量学习，建立农业AI模型部署前的时序泛化评测基准。","2026-10-20T02:47:54.289085Z",{"id":408,"title":409,"url":410,"summary":411,"summary_zh":9,"content":412,"source_name":413,"source_url":9,"published_at":365,"category":414,"cover_url":9,"hotness":13,"is_selected":14,"score":415,"score_detail":416,"sources":420,"tags":422,"primary_topic":426,"secondary_topics":427,"classification":428,"search_phrases":454,"slug":457,"view_count":21,"doi":9,"paper":9,"created_at":458},5108,"广西西林:粤桂协作赋能 深山桔园请来「AI管家」 化肥农药减量20%以上","https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm","广西百色市西林县古障镇仙仁掌村桔园,无人机升空巡测快速识别叶片黄化、缺水、病虫害等异常。依托粤桂协作对口帮扶资源,西林联合深圳市罗湖区、深圳市丰农控股,在233亩核心示范基地搭建「无人机+AI」全域监测平台,气象、墒情、虫情、灾情监测设备全天候运行。核心示范区化肥、农药用量减少20%以上,人工管护成本下降30%,果树亩均产量提升15%,优质果率提高8个百分点;全镇果园管理效率提升60%,每年减少农业灾害损失100万至150万元,全域每年节约农资成本超1426万元。","![Image 3](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002FgmwLogo_share.jpg)\n\n点击右上角![Image 4](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Ficonshare.png)微信好友\n\n![Image 5](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Fpengyouquan.png)朋友圈\n\n![Image 6](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Flayershare.png)\n\n请使用浏览器分享功能进行分享\n\n![Image 7](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Farrow.png)\n\n[![Image 8: 地方频道](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fcontentlogo\u002F12684.gif)](https:\u002F\u002Fdifang.gmw.cn\u002F)\n\n![Image 9](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Fmline.png)![Image 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[时评](https:\u002F\u002Fguancha.gmw.cn\u002F)\n*   [理论](https:\u002F\u002Ftheory.gmw.cn\u002F)\n*   [文化](https:\u002F\u002Fculture.gmw.cn\u002F)\n*   [科技](https:\u002F\u002Ftech.gmw.cn\u002F)\n*   [教育](https:\u002F\u002Fedu.gmw.cn\u002F)\n*   [![Image 13](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fcon_headIcon.png)](https:\u002F\u002Fwww.gmw.cn\u002Fmap.htm)\n\n正在阅读：广西西林:粤桂协作赋能，深山桔园请来 “AI 管家”\n\n[首页](https:\u002F\u002Fwww.gmw.cn\u002F)>[![Image 14](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fcontentlogo\u002F12684.gif)](https:\u002F\u002Fdifang.gmw.cn\u002F)[地方频道](https:\u002F\u002Fdifang.gmw.cn\u002F)>[地方频道－广西](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F)>[当地新闻](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002Fnode_12944.htm)>正文\n\n# 广西西林:粤桂协作赋能，深山桔园请来 “AI 管家”\n\n来源：[光明日报客户端](https:\u002F\u002Fapp.gmdaily.cn\u002Fas\u002Fopened\u002Fn\u002F59cdd34806ab4ec7b66540c2cb9dc89e)2026-10-16 12:43\n\n[](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)[X](javascript:;)[](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)\n\n清晨，广西百色市西林县古障镇仙仁掌村桔园，晨露未消，无人机升空巡测，快速识别叶片黄化、缺水、病虫害等果园异常。智慧管护，正成为当地沙糖桔产业的生产常态。\n\n西林是“中国沙糖桔之乡”，沙糖桔种植面积达22.46万亩，是群众增收的支柱产业。但境内山地多、果园地块零散，长期沿用传统管护模式，施肥打药依靠经验，收成受天气影响大。粗放种植造成果品品质不稳定，病虫害、自然灾害带来减产风险，制约产业效益提升。\n\n![Image 15: twitter](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fpng\u002Fsite2\u002F20261016\u002Fe4232a100a384446b18875424e94dd72.png)\n\n依托粤桂协作对口帮扶资源，西林联合深圳市罗湖区、深圳市丰农控股，在古障镇仙仁掌村打造智慧农业试点，搭建“无人机+AI”全域监测平台，探索山地桔园数字化管护，破解“看天吃饭”困境。\n\n在233亩核心示范基地，气象、墒情、虫情、灾情监测设备全天候运行，无人机定期空中巡园，土壤、病虫害、气象等数据实时回传。AI系统自动分析研判，给出农事指导，改变过去果农肉眼巡查果园的传统方式。\n\n项目按照“基地做示范，全镇一张图，全县一个码”三步走推进智慧果园建设。先以上、下俄屯基地打造样板，配套海拔梯度采集点，跑通“数据采集—AI建模—标准输出”流程，优化适配本地沙糖桔的算法模型，解决智慧农业技术“水土不服”问题。第二步完成全镇7.13万亩沙糖桔标准化航测，建成“产业一张图”数字平台，地块信息、产业风险一屏可查，产业管理由凭经验估变为凭数据管。第三步打造“掌上桔事”小程序，把灾害预警、农事指导、AI问诊直达农户手机。目前小程序正在内测，上线后将惠及全县8万多户果农。\n\n![Image 16: twitter](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fpng\u002Fsite2\u002F20261016\u002Ff025b13ba3384034afba14495fc87f4d.png)\n\n“以前种果全靠摸索，天天跑果园巡查，病虫害也不好分辨。现在靠AI系统管护省心很多，期待明年好收成。”首批试点果农、古障镇仙仁掌村上俄屯组长秦锋说。\n\n数字化落地成效显著。核心示范区化肥、农药用量减少20%以上，人工管护成本下降30%，果树亩均产量提升15%，优质果率提高8个百分点；全镇果园管理效率提升60%，每年减少农业灾害损失100万至150万元，全域每年节约农资成本超1426万元。\n\n不少地方智慧农业容易变成观赏“盆景”。西林立足山地特点，聚焦生产实效、农户增收，让数字技术扎根田间。丰农控股广西总经理陈洁介绍，依托AI技术加持，深山沙糖桔产业迎来新机遇。西林计划用三到五年推动沙糖桔产业从“靠人力、凭经验”转向“靠系统、用数据”，打造山地沙糖桔全链路数智化样板。（光明日报全媒体记者 周仕兴 通讯员 杨先能）\n\n[ 责编：王宏泽 ]\n\n阅读剩余全文（）\n\n![Image 17](https:\u002F\u002Fimg.gmw.cn\u002Fcontent_banner\u002Fcontent_840x120_qrcodes.jpg)\n\n## 相关阅读\n\n*   [![Image 18](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)](javascript:;)    \n*   [![Image 19](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)](javascript:;)    \n*   [](javascript:;)[](javascript:;)[](javascript:;)[](javascript:;)\n\n## 您此时的心情\n\n[![Image 20: 光明云投](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fyuntoulogo.png)](https:\u002F\u002Fvote.gmw.cn\u002F)\n\n新闻表情排行[日](https:\u002F\u002Fwww.gmw.cn\u002Fmotionsdaytop.htm)\u002F[周](https:\u002F\u002Fwww.gmw.cn\u002Fmotionsweektop.htm)\n\n*   [![Image 21](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fico_motion_1.png)](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)开心   0   \n*   [![Image 22](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fico_motion_2.png)](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)难过   0   \n*   [![Image 23](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fico_motion_3.png)](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)点赞   0   \n*   [![Image 24](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fico_motion_4.png)](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)飘过   0   \n\n## 视觉焦点\n\n*   [![Image 25](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20261016\u002F3ab721cf47be4960823a8636e4478b8e.jpg)](https:\u002F\u002Fkepu.gmw.cn\u002F2026-10\u002F16\u002Fcontent_39050695.htm)  \n来中国科技馆，和科学家击掌！\n\n*   [![Image 26](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20261016\u002F115e67cd19084c568c2edd94f9e6391c.jpg)](https:\u002F\u002Ftheory.gmw.cn\u002F2026-10\u002F16\u002Fcontent_39046807.htm)  \n理响中国·幸福有“理”\n\n## 最热文章\n\n*   [一见·习近平主席三封广交会贺信的“开放”底色](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-10\u002F16\u002Fcontent_39050949.htm) 1\n*   [和总书记面对面丨每一代人都要走好自己的长征路](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-10\u002F16\u002Fcontent_39050945.htm) 2\n*   [微视频丨聆听总书记深情话语 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27](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260918\u002Fdcd47048fbc148c18e29bbf71cbb75c6.jpg)](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_170734.htm) [赓续长征精神 奋进复兴征程](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_170734.htm)  [第十一个全民国家安全教育日](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_168693.htm) [![Image 28](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260415\u002F005056b837832b49959d02.jpg)](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_168693.htm)  \n*   [![Image 29](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260918\u002F0aba89129d0f45b8a77ac2b07d7dbfeb.jpg)](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_170718.htm) [2026年亚运会](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_170718.htm)  [树立和践行正确政绩观](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_168213.htm) [![Image 30](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260308\u002Ff44d305ea6dd2b175a4037.jpg)](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_168213.htm)  \n*   [![Image 31](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260617\u002Ff44d305ea6dd2b9c4b4626.jpg)](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_169453.htm) [庆祝中国共产党成立105周年](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_169453.htm)  [2026年全国两会](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_168093.htm) [![Image 32](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260303\u002Ff44d305ea48e2b1074620b.jpg)](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_168093.htm)  \n\n推荐阅读\n\n[](https:\u002F\u002Fkepu.gmw.cn\u002Feco\u002F2026-10\u002F16\u002Fcontent_39050641.htm)\n\n[2026世界生态环境十大科技进展揭晓](https:\u002F\u002Fkepu.gmw.cn\u002Feco\u002F2026-10\u002F16\u002Fcontent_39050641.htm)\n\n当前全球生态环境挑战日益严峻，科技创新是应对可持续发展挑战的核心力量。\n\n2026-10-16 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日","报道",77,{"impact":17,"substance":417,"depth":418,"authority":127,"freshness":13,"relevant":21,"comment":419},20,16,"粤桂协作推动山地桔园AI全域监测落地，化肥农药减量20%以上、亩均增产15%等数据具体，是智慧农业从示范盆景走向生产实效的可复制样本。",[421],{"name":413,"url":410},[423,133,26,424,425],"数字乡村","粤桂协作","沙糖桔",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":429},{"candidates":430,"agricultural_evidence":52},[431,435,441,445,449],{"code":32,"score":277,"supported":52,"title":432,"abstract":433,"tags":434},[57],[57],[],{"code":436,"score":84,"supported":52,"title":437,"abstract":438,"tags":440},"B2",[],[439],"果树",[],{"code":162,"score":21,"supported":14,"title":442,"abstract":443,"tags":444},[],[],[133],{"code":75,"score":21,"supported":14,"title":446,"abstract":447,"tags":448},[],[],[26],{"code":450,"score":21,"supported":14,"title":451,"abstract":452,"tags":453},"I6",[],[],[423],[455,456],"广西西林 沙糖桔 AI管家","粤桂协作 智慧果园 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