[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2046":3,"related-2046":124},{"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":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":101,"slug":104,"view_count":105,"doi":106,"paper":107,"created_at":123},2046,"AI-Powered Crop Protection: Transforming Pest Surveillance and Decision-Making Through Digital Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjsrr\u002F2026\u002Fv32i94503","Crop protection is increasingly challenged by changing pest population dynamics, pesticide resistance and the need for more sustainable agricultural production. Artificial intelligence (AI) and digital agriculture offer complementary tools for improving pest surveillance, forecasting and decision-making through automated detection, continuous monitoring and more precise interventions. This review synthesises the applications of machine learning, deep learning, computer vision, Internet of Things-enabled sensing, smart traps, remote sensing, unmanned aerial vehicles, robotics, predictive analytics and intelligent decision support systems in pest management. These approaches support pest identification, population monitoring, outbreak prediction, risk assessment and site-specific management, thereby strengthening the information base required for integrated pest management. The review also examines emerging technologies, including generative AI, foundation models, digital twins, explainable AI, edge intelligence and autonomous agricultural platforms, in relation to future crop protection systems. Despite rapid technological progress, practical deployment remains constrained by limited high-quality datasets, variable field conditions, model generalisation, interoperability, digital infrastructure, cost and farmer adoption. Effective implementation therefore requires transparent, robust and scalable systems that are validated under diverse agricultural conditions and aligned with ecological pest management principles. Overall, integrating AI-enabled surveillance and decision support with integrated pest management can support more timely, adaptive and resource-efficient crop protection while reducing unnecessary interventions.","作物保护正日益受到害虫种群动态变化、农药抗性以及更可持续农业生产需求的挑战。人工智能（AI）与数字农业通过自动检测、持续监测和更精准的干预措施，为改善害虫监测、预测和决策提供了互补性工具。本文综述了机器学习、深度学习、计算机视觉、物联网传感、智能诱捕器、遥感、无人机、机器人技术、预测分析和智能决策支持系统在害虫管理中的应用。这些方法支持害虫识别、种群监测、暴发预测、风险评估和位点特异性管理，从而强化了有害生物综合治理所需的信息基础。本文还探讨了新兴技术，包括生成式人工智能、基础模型、数字孪生、可解释人工智能、边缘智能和自主农业平台，及其在未来作物保护系统中的前景。尽管技术进展迅速，实际部署仍受限于高质量数据集不足、田间条件多变、模型泛化能力、互操作性、数字基础设施、成本以及农民采纳程度等因素。因此，有效实施需要透明、稳健且可扩展的系统，这些系统应在多样化农业条件下得到验证，并与生态害虫管理原则相一致。总体而言，将人工智能驱动的监测与决策支持同有害生物综合治理相结合，可支持更及时、更具适应性和资源效率更高的作物保护，同时减少不必要的干预。",null,"Journal of Scientific Research and Reports","2026-09-09T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"系统综述AI与数字农业在病虫害监测预警与决策支持中的应用与瓶颈，信息密度高、结论可靠，对智慧植保方向有参考价值，但属综述类论文而非突破性成果，适合进入精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","智慧农业","农业人工智能","病虫害监测","精准植保",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":54},[51,60,67,79,87,94],{"code":52,"score":53,"supported":54,"title":55,"abstract":57,"tags":59},"I1",19,true,[56],"digital agriculture",[27,58,56],"数字孪生",[27,28],{"code":33,"score":19,"supported":54,"title":61,"abstract":63,"tags":66},[62],"crop protection",[64,65,62],"农药","pesticide",[],{"code":40,"score":13,"supported":54,"title":68,"abstract":69,"tags":78},[],[70,71,72,73,74,75,76,77],"深度学习","机器学习","计算机视觉","machine learning","deep learning","artificial intelligence","computer vision","decision support",[29],{"code":80,"score":81,"supported":54,"title":82,"abstract":83,"tags":86},"I3",6,[],[84,85],"遥感","remote sensing",[],{"code":88,"score":81,"supported":54,"title":89,"abstract":90,"tags":93},"I5",[],[91,92],"物联网","internet of things",[],{"code":95,"score":96,"supported":54,"title":97,"abstract":98,"tags":100},"H8",3,[],[99],"可持续农业",[],[102,103],"农业人工智能 病虫害监测 数字农业 智慧农业","农业人工智能 病虫害监测","农业人工智能病虫害监测数字农业智慧农业-2046",2,"10.9734\u002Fjsrr\u002F2026\u002Fv32i94503",{"doi":106,"openalex_id":108,"authors":109,"venue":10,"cited_by_count":114,"oa_url":6,"card":115,"direction":121,"ingested_from":122},"W7212081296",[110,112],{"name":111,"orcid":9},"R. Saritha",{"name":113,"orcid":9},"M. Swathi",0,{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"综述AI与数字农业在害虫监测、预测及决策支持中的应用与挑战。","综述机器学习、深度学习、计算机视觉、物联网、遥感、无人机与决策支持系统。","AI可提升害虫识别、暴发预测和精准管理，但受数据、泛化、成本与采纳制约。","农业人工智能与决策模型","可探索生成式AI、数字孪生与边缘智能在田间害虫管理中的可解释、可扩展落地验证。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.692422Z",{"total":81,"page":22,"page_size":81,"items":125},[126,252,303,350,409,471],{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":9,"source_name":132,"source_url":129,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":134,"score_detail":135,"sources":139,"tags":141,"primary_topic":145,"secondary_topics":151,"classification":154,"search_phrases":207,"slug":210,"view_count":96,"doi":211,"paper":212,"created_at":251},1325,"Changing pest dynamics and pesticide use in cotton: Challenges and sustainable management","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16195","Cotton (Gossypium spp.) is one of the world’s most important fibre crops, yet its sustainability is increasingly challenged by evolving pest complexes, intensified pesticide use, climate change and climate variability. The evidence indicates current knowledge on pest dynamics, pesticide dependence and ecological interactions in cotton agroecosystems, highlighting the shift from bollworm-dominated pest complexes to the increasing prevalence of secondary pests following the widespread adoption of Bacillus thuringiensis (Bt) cotton. Although Bt (Cry toxin)-based transgenic cotton technology initially reduced insecticide use against the bollworm complex, primarily lepidopteran species, the emergence of resistance among bollworms and the upsurge of certain sucking pests in recent decades have reinstated insecticide dependence for pest management. This has led to ecological imbalance, non-target toxicity, the decline of pollinator populations and natural enemy communities, biodiversity loss, environmental contamination, accumulation of residues in products and yield challenges, in addition to increased production costs. The review emphasises the critical role of integrated pest management (IPM) strategies that integrate biological control, habitat diversification, resistant cultivars and threshold-based pesticide applications to restore ecological stability. Alternative approaches such as botanical pesticides, nanoformulations and plant volatile-mediated pest regulation are highlighted as promising tools for sustainable pest control. Advances in precision agriculture, including artificial intelligence (AI), machine learning and unmanned aerial vehicle (UAV)-based pesticide delivery systems, have the potential to improve pest surveillance and reduce chemical inputs. Supply-chain procurement, certification, traceability and consumer demand can encourage lower-pesticide production, although these mechanisms require fair price incentives to avoid transferring compliance costs to smallholders. Sustainable cotton therefore requires region-specific, climate-resilient, evidence-based IPM supported by extension, market incentives, resistance monitoring and coordinated policy.","棉花（Gossypium spp.）是全球最重要的纤维作物之一，然而其可持续性正日益受到害虫群落演变、农药使用强度增加、气候变化及气候变率等因素的挑战。现有证据表明，当前对棉花农业生态系统中害虫动态、农药依赖及生态互作的认识，凸显了自苏云金芽孢杆菌（Bacillus thuringiensis, Bt）棉花广泛推广以来，害虫群落从以棉铃虫为主向次生害虫日益增多的转变。尽管基于Bt（Cry毒素）的转基因棉花技术最初减少了对棉铃虫复合体（主要为鳞翅目害虫）的杀虫剂使用，但近几十年来棉铃虫抗药性的出现及某些刺吸式害虫的激增，重新导致害虫管理对杀虫剂的依赖。这引发了生态失衡、非靶标毒性、传粉昆虫种群及天敌群落减少、生物多样性丧失、环境污染、产品中残留物积累及产量挑战，同时增加了生产成本。本综述强调了综合害虫管理（IPM）策略的关键作用，该策略整合生物防治、生境多样化、抗性品种及基于阈值的农药施用，以恢复生态稳定性。植物源农药、纳米制剂及植物挥发物介导的害虫调控等替代方法，被强调为可持续害虫防治的有前景工具。精准农业的进展，包括人工智能（AI）、机器学习及基于无人机（UAV）的农药施用系统，有望改善害虫监测并减少化学投入。供应链采购、认证、可追溯性及消费者需求可促进低农药生产，但这些机制需辅以公平价格激励，以避免将合规成本转嫁给小农户。因此，可持续棉花生产需要区域特异性、气候韧性、基于证据的IPM，并辅以推广服务、市场激励、抗性监测及协调政策支持。","Plant Science Today","2026-08-31T00:00:00Z",72,{"impact":17,"substance":136,"depth":17,"authority":137,"freshness":105,"relevant":22,"comment":138},22,12,"综述性论文，系统分析棉花害虫动态与农药依赖问题，提出IPM与精准农业等可持续路径，对农业绿色发展有参考价值。",[140],{"name":132,"url":129},[28,29,142,143,144],"棉花","农药减量","病虫害防治",{"code":146,"slug":147,"name":148,"path":149,"parent_code":150},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[152,153],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":155},{"candidates":156,"agricultural_evidence":54},[157,162,168,172,181,188,195,201],{"code":33,"score":17,"supported":54,"title":158,"abstract":159,"tags":161},[65],[64,160,65],"生物防治",[143],{"code":146,"score":163,"supported":54,"title":164,"abstract":166,"tags":167},15,[165],"cotton",[142,165],[142],{"code":40,"score":13,"supported":54,"title":169,"abstract":170,"tags":171},[],[71,73,75],[29],{"code":173,"score":174,"supported":54,"title":175,"abstract":176,"tags":180},"H6",9,[],[177,178,179],"生物多样性","农业生态","biodiversity",[],{"code":52,"score":182,"supported":54,"title":183,"abstract":184,"tags":187},7,[],[185,186],"精准农业","precision agriculture",[28],{"code":189,"score":81,"supported":54,"title":190,"abstract":191,"tags":194},"H7",[],[192,193],"气候变化","climate change",[],{"code":196,"score":96,"supported":54,"title":197,"abstract":198,"tags":200},"A4",[],[199],"花生",[],{"code":202,"score":96,"supported":54,"title":203,"abstract":204,"tags":206},"G2",[],[205],"小农户",[],[208,209],"农业人工智能 病虫害防治 农药减量 智慧农业","农业人工智能 病虫害防治","农业人工智能病虫害防治农药减量智慧农业-1325","10.14719\u002Fpst.16195",{"doi":211,"openalex_id":213,"authors":214,"venue":132,"cited_by_count":114,"oa_url":129,"card":245,"direction":119,"ingested_from":122},"W7204806269",[215,218,221,224,227,230,233,236,239,242],{"name":216,"orcid":217},"V Veeragowtham","https:\u002F\u002Forcid.org\u002F0009-0002-9822-4901",{"name":219,"orcid":220},"A. Suganthi","https:\u002F\u002Forcid.org\u002F0000-0001-7013-4459",{"name":222,"orcid":223},"M. Murugan","https:\u002F\u002Forcid.org\u002F0000-0002-7485-1153",{"name":225,"orcid":226},"P. Renukadevi","https:\u002F\u002Forcid.org\u002F0000-0001-9665-1681",{"name":228,"orcid":229},"S. Vellaikumar","https:\u002F\u002Forcid.org\u002F0000-0001-9277-457X",{"name":231,"orcid":232},"Ali Iqra","https:\u002F\u002Forcid.org\u002F0009-0008-6095-9471",{"name":234,"orcid":235},"J Kousika","https:\u002F\u002Forcid.org\u002F0000-0002-2455-5268",{"name":237,"orcid":238},"Sheela Venugopal","https:\u002F\u002Forcid.org\u002F0000-0002-4689-4105",{"name":240,"orcid":241},"Prakash Kolanchi","https:\u002F\u002Forcid.org\u002F0000-0003-1244-199X",{"name":243,"orcid":244},"Murugesh Dharani","https:\u002F\u002Forcid.org\u002F0009-0004-6705-1344",{"tldr":246,"method":247,"finding":248,"direction":249,"opportunity":250},"综述棉花害虫动态与农药使用变化，提出可持续管理策略。","综述文献，分析Bt棉影响、害虫抗性及IPM、精准农业等技术。","Bt棉导致次生害虫上升和抗性，需综合IPM与精准农业减少农药。","农业绿色发展与碳","可研究AI与无人机在棉花害虫监测与精准施药中的实际应用效果及小农采纳机制。","2026-09-01T23:30:34.470501Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":9,"content":257,"source_name":258,"source_url":9,"published_at":259,"category":260,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":261,"sources":264,"tags":266,"primary_topic":271,"secondary_topics":272,"classification":274,"search_phrases":298,"slug":301,"view_count":96,"doi":9,"paper":9,"created_at":302},2563,"中国农业科学院 AI赋能农产品贸易跨境有害生物风险管控 植保所登Artificial Intelligence in Agriculture","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F6e1f3ee4b85d402c9b490a571ade9af0.htm","植保所农业入侵生物预防与监控创新团队利用人工智能技术有效破解跨境有害生物多阶段风险评估与管控决策衔接脱节难题，相关研究成果发表在农业人工智能期刊上。研究团队首次解析了柑橘害虫随跨境贸易传入的243条风险路径和98个关键风险节点，将多阶段定量风险评估信息与大语言模型等生成式人工智能相结合，构建了证据可追溯、结论可解释的智能风险研判与辅助决策应用系统。","[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[](https:\u002F\u002Fcaas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n官方微信 \n\n![Image 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002F020ac4d3d8b6451782a8702c72604336.jpg)农科专家在线微信公众号\n\n![Image 2](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002Fbf6579e4651e437db31a22cd2249b319.jpg)中国农科院微信公众号\n\n[![Image 3](https:\u002F\u002Fcaas.cn\u002Fstatic2022\u002Fimages\u002Fmenu_logo.png)](https:\u002F\u002Fcaas.cn\u002Findex.htm)\n\n*   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Agriculture**_）上。\n\n随着全球贸易持续加速，柑橘等重要果品进口量攀升，植物有害生物随贸易“搭车”入境的风险日益加剧。然而，跨境植物有害生物伴随果品贸易“传入—定殖—扩散”的多阶段风险尚不明确。\n\n研究团队首次解析了柑橘害虫随跨境贸易传入的243条风险路径和98个关键风险节点，精准锁定介壳虫、实蝇等高风险类群及其关键来源地与传入地区，发现我国南方柑橘主产区面临多种害虫共同传入与定殖的复合威胁。\n\n在此基础上，该研究将多阶段定量风险评估信息等数据与大语言模型等生成式人工智能相结合，突破通用大语言模型缺乏领域知识的固有局限，构建了证据可追溯、结论可解释的智能风险研判与辅助决策应用系统，为跨境有害生物风险管控提供科技支撑。\n\n该研究得到国家重点研发计划等项目支持。\n\n相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.07.004\n\n(单位: 中国农业科学院植物保护研究所)\n\n打印本页\n\n关闭本页\n\n[院网信息发布与管理](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)[最新动态](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)\n\n*   [[科学网] 弯刺蔷薇缘何成为月季的抗寒育种亲本](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F71f0fd687f5a4cbcb5d8e14dd8ca5ab7.htm)2026-09-11  \n*   [[科学网] 高效引导编辑系统创制双抗黄瓜新种质](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F918aff6c268a4196880a4514b117bf34.htm)2026-09-11  \n*   [[总台中国乡村之声]《乡村FM》东北粮仓的秋粮冲刺](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F2a01860de3d54f759501beb9fb666fee.htm)2026-09-11  \n*  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2026-09","2026-09-13T01:00:00Z","报道",{"impact":136,"substance":262,"depth":163,"authority":163,"freshness":21,"relevant":22,"comment":263},16,"国家级科研机构在AI农业期刊发表跨境有害生物风险管控成果，专业性与权威性突出，但正文信息有限，属细分领域科研进展。",[265],{"name":258,"url":255},[29,267,268,269,270],"植物保护","智慧植保","跨境有害生物","农产品贸易",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[273],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":275},{"candidates":276,"agricultural_evidence":54},[277,281,287,293],{"code":40,"score":137,"supported":54,"title":278,"abstract":279,"tags":280},[75],[29],[29],{"code":33,"score":282,"supported":54,"title":283,"abstract":285,"tags":286},11,[284],"植保",[284],[],{"code":288,"score":96,"supported":54,"title":289,"abstract":290,"tags":292},"B2",[],[291],"柑橘",[],{"code":294,"score":22,"supported":14,"title":295,"abstract":296,"tags":297},"E5",[],[],[270],[299,300],"农业人工智能 跨境有害生物 农产品贸易 智慧植保","农业人工智能 跨境有害生物","农业人工智能跨境有害生物农产品贸易智慧植保-2563","2026-09-16T00:03:46.695280Z",{"id":304,"title":305,"url":306,"summary":307,"summary_zh":308,"content":9,"source_name":309,"source_url":306,"published_at":310,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":311,"score_detail":312,"sources":314,"tags":316,"primary_topic":318,"secondary_topics":319,"classification":320,"search_phrases":335,"slug":337,"view_count":114,"doi":338,"paper":339,"created_at":349},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":21,"substance":17,"depth":19,"authority":174,"freshness":13,"relevant":22,"comment":313},"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。",[315],{"name":309,"url":306},[28,29,317,30],"精准施药",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":321},{"candidates":322,"agricultural_evidence":54},[323,327,331],{"code":40,"score":174,"supported":54,"title":324,"abstract":325,"tags":326},[75],[],[29],{"code":52,"score":182,"supported":54,"title":328,"abstract":329,"tags":330},[],[185,186],[28],{"code":33,"score":22,"supported":14,"title":332,"abstract":333,"tags":334},[],[],[317],[336,103],"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智慧农业精准施药-5589","10.5281\u002Fzenodo.23544107",{"doi":338,"openalex_id":340,"authors":341,"venue":309,"cited_by_count":114,"oa_url":306,"card":344,"direction":119,"ingested_from":122},"W7223116087",[342],{"name":343,"orcid":9},"Achieng' Atim Okello",{"tldr":345,"method":346,"finding":347,"direction":119,"opportunity":348},"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":351,"title":352,"url":353,"summary":354,"summary_zh":355,"content":9,"source_name":309,"source_url":353,"published_at":356,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":357,"score_detail":358,"sources":361,"tags":363,"primary_topic":366,"secondary_topics":367,"classification":368,"search_phrases":393,"slug":396,"view_count":22,"doi":397,"paper":398,"created_at":408},4657,"Agriculture and Farmer Digital Assistant: An AI-Based Digital Decision Support Platform for Smart Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23278187","Abstract Farmers frequently make decisions under uncertain weather, incomplete agricultural information, changing soil conditions, and limited access to timely expert guidance. The Agriculture and Farmer Digital Assistant (AFDA) is a web-based digital decision-support platform designed to bring agricultural data, artificial intelligence, recommendation services, weather information, and role-based administration into a single system. The platform accepts agricultural inputs from farmers and processes them to provide crop recommendations and basic farming guidance. The design also considers soil analysis, pest and disease support, market information, notifications, multilingual assistance, and future sensor integration. The implemented project version focuses on user registration and login, agricultural data input, crop recommendation, basic weather guidance, and a recommendation dashboard, while several advanced capabilities remain future extensions. A layered architecture separates the user interface, application logic, AI processing, database, and notification\u002Frecommendation functions. The project further defines functional and non-functional requirements, data-flow and entity relationships, user workflows, testing procedures, and maintenance considerations. The reported evaluation is functional rather than numerical: the major input, authentication, recommendation, navigation, and output-display workflows were checked during development, while no measured accuracy or response-time benchmark is claimed. AFDA therefore provides a foundation for a connected and extensible agricultural assistance system intended to support better-informed and more sustainable farming decisions. Keywords Agriculture and Farmer Digital Assistant, AFDA, artificial intelligence, machine learning, crop recommendation, smart farming, agricultural decision support, weather guidance, role-based access control.","农户常常在天气不确定、农业信息不完整、土壤条件不断变化以及难以及时获得专家指导的情况下作出决策。农业与农户数字助手（Agriculture and Farmer Digital Assistant，AFDA）是一个基于网络的数字决策支持平台，旨在将农业数据、人工智能、推荐服务、天气信息和基于角色的管理整合到一个系统中。该平台接收农户输入的农业数据，并对其进行处理，以提供作物推荐和基本农耕指导。其设计还考虑了土壤分析、病虫害支持、市场信息、通知、多语言辅助以及未来的传感器集成。已实现的项目版本侧重于用户注册与登录、农业数据输入、作物推荐、基本天气指导以及推荐仪表板，而若干高级功能仍留作未来扩展。分层架构将用户界面、应用逻辑、人工智能处理、数据库以及通知\u002F推荐功能相互分离。该项目进一步定义了功能性与非功能性需求、数据流与实体关系、用户工作流程、测试程序以及维护方面的考虑。所报告的评估是功能性的而非数值性的：在开发过程中检查了主要输入、身份验证、推荐、导航和输出显示等工作流程，但未声称有任何经过测量的准确性或响应时间基准。因此，AFDA为一个互联且可扩展的农业辅助系统提供了基础，旨在支持更明智且更可持续的农耕决策。关键词：农业与农户数字助手，AFDA，人工智能，机器学习，作物推荐，智慧农业，农业决策支持，天气指导，基于角色的访问控制。","2026-10-10T00:00:00Z",54,{"impact":21,"substance":359,"depth":137,"authority":13,"freshness":13,"relevant":22,"comment":360},14,"面向农户的AI决策支持平台论文，架构与需求设计较完整，但仅做功能验证、无精度或性能数据，属早期原型，参考价值有限。",[362],{"name":309,"url":353},[27,28,29,364,365],"作物推荐","农业决策支持",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":369},{"candidates":370,"agricultural_evidence":54},[371,375,382,387],{"code":40,"score":17,"supported":54,"title":372,"abstract":373,"tags":374},[77],[71,73,75,77],[29],{"code":376,"score":81,"supported":54,"title":377,"abstract":378,"tags":381},"H2",[],[379,380],"土壤","soil",[],{"code":52,"score":383,"supported":54,"title":384,"abstract":385,"tags":386},5,[],[28],[27,28],{"code":388,"score":96,"supported":54,"title":389,"abstract":390,"tags":392},"I6",[],[391],"agricultural data",[],[394,395],"AFDA 农业数字助手","农业人工智能 农业决策支持 作物推荐 数字农业","AFDA农业数字助手-4657","10.5281\u002Fzenodo.23278187",{"doi":397,"openalex_id":399,"authors":400,"venue":309,"cited_by_count":114,"oa_url":353,"card":403,"direction":121,"ingested_from":122},"W7221331787",[401],{"name":402,"orcid":9},"B. Siddhu, G. Naveen, S. Guru Charan, T. Ajay, C. Naresh",{"tldr":404,"method":405,"finding":406,"direction":119,"opportunity":407},"开发了基于AI的农业数字决策支持平台AFDA，为农民提供作物推荐和农事指导。","采用分层架构的Web平台，集成AI处理、数据库和推荐服务，支持多语言。","平台实现了用户认证、数据输入、作物推荐和天气指导等核心功能，但未提供精度或响应时间基准。","可补充传感器实时数据集成与模型精度评估，提升决策支持的可靠性和实时性。","2026-10-11T23:30:07.538409Z",{"id":410,"title":411,"url":412,"summary":413,"summary_zh":414,"content":9,"source_name":415,"source_url":412,"published_at":416,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":417,"score_detail":418,"sources":420,"tags":422,"primary_topic":425,"secondary_topics":426,"classification":427,"search_phrases":445,"slug":448,"view_count":96,"doi":449,"paper":450,"created_at":470},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。","Revista de Investigación en Tecnologías de la Información","2026-10-02T00:00:00Z",63,{"impact":137,"substance":17,"depth":163,"authority":13,"freshness":21,"relevant":22,"comment":419},"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。",[421],{"name":415,"url":412},[28,29,423,424,30],"设施农业","番茄种植",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":428},{"candidates":429,"agricultural_evidence":54},[430,434,441],{"code":40,"score":13,"supported":54,"title":431,"abstract":432,"tags":433},[],[70,72,74,75,76],[29],{"code":435,"score":182,"supported":54,"title":436,"abstract":437,"tags":440},"B1",[],[438,439],"番茄","tomato",[424],{"code":52,"score":22,"supported":14,"title":442,"abstract":443,"tags":444},[],[],[28],[446,447],"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","10.36825\u002Friti.14.34.010",{"doi":449,"openalex_id":451,"authors":452,"venue":415,"cited_by_count":114,"oa_url":412,"card":465,"direction":119,"ingested_from":122},"W7216085028",[453,455,458,461,463],{"name":454,"orcid":9},"Jesús Alberto Alarcón Luna",{"name":456,"orcid":457},"María Salomé Alejandre Apolinar","https:\u002F\u002Forcid.org\u002F0009-0008-0232-8217",{"name":459,"orcid":460},"Hugo Amores Pérez","https:\u002F\u002Forcid.org\u002F0009-0009-0260-4872",{"name":462,"orcid":9},"Jacel Adame García",{"name":464,"orcid":9},"María Angélica Cerdán",{"tldr":466,"method":467,"finding":468,"direction":119,"opportunity":469},"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。","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。","2026-10-03T23:30:53.163959Z",{"id":472,"title":473,"url":474,"summary":475,"summary_zh":476,"content":9,"source_name":477,"source_url":474,"published_at":416,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":478,"score_detail":479,"sources":481,"tags":483,"primary_topic":485,"secondary_topics":486,"classification":487,"search_phrases":518,"slug":521,"view_count":522,"doi":523,"paper":524,"created_at":538},4105,"Artificial Intelligence in Agriculture: Enhancing Yield and Sustainable Farming Practices among Farmers in India","https:\u002F\u002Fdoi.org\u002F10.31235\u002Fosf.io\u002Fg7ypt_v1","Artificial intelligence (AI) is increasingly being applied across sectors, including agriculture. In India, agriculture remains central to employment, rural livelihoods, and national food security. Rising population pressure, climate change, declining land availability, pest and disease risks, labour shortages, and resource constraints have intensified the need for more productive and sustainable farming systems. This paper uses secondary data to examine how AI can enhance agricultural yield and support sustainable farming practices among farmers in India. It reviews applications such as precision agriculture, drones, crop disease detection, digital advisory services, market information systems, and resource optimisation tools. The paper also analyses the major barriers to AI adoption, including weak rural connectivity, high initial investment, limited digital literacy, and the need for localised solutions. The study concludes that AI has strong potential to improve productivity and sustainability in Indian agriculture, provided that adoption is supported through public investment, affordable access models, local-language advisory systems, farmer training, and public- private partnerships.","人工智能（AI）正日益应用于包括农业在内的各个领域。在印度，农业仍然是就业、农村生计和国家粮食安全的核心。人口压力不断上升、气候变化、可用土地减少、病虫害风险、劳动力短缺以及资源约束，加剧了对更高生产力和更可持续农业体系的需求。本文利用二手数据，考察人工智能如何提升印度农民的农业产量并支持可持续耕作方式。文章综述了精准农业、无人机、作物病害检测、数字咨询服务、市场信息系统和资源优化工具等应用。本文还分析了人工智能应用的主要障碍，包括农村连通性薄弱、初始投资高、数字素养有限以及对本地化解决方案的需求。研究认为，人工智能在提升印度农业生产力和可持续性方面具有巨大潜力，前提是通过公共投资、可负担的获取模式、本地语言咨询系统、农民培训以及公私合作来支持其应用。","OpenAlex",69,{"impact":17,"substance":262,"depth":359,"authority":137,"freshness":174,"relevant":22,"comment":480},"基于二手数据综述印度AI农业应用与推广障碍，结论有参考价值但缺乏一手数据与实证方法创新。",[482],{"name":477,"url":474},[27,28,29,185,484],"印度农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":488},{"candidates":489,"agricultural_evidence":54},[490,494,498,505,509,513],{"code":40,"score":137,"supported":54,"title":491,"abstract":492,"tags":493},[75],[75],[29],{"code":52,"score":174,"supported":54,"title":495,"abstract":496,"tags":497},[],[185,186],[27,28,185],{"code":499,"score":81,"supported":54,"title":500,"abstract":501,"tags":504},"A8",[],[502,503],"粮食安全","food security",[],{"code":189,"score":81,"supported":54,"title":506,"abstract":507,"tags":508},[],[192,193],[],{"code":95,"score":96,"supported":54,"title":510,"abstract":511,"tags":512},[],[99],[],{"code":514,"score":22,"supported":14,"title":515,"abstract":516,"tags":517},"J3",[],[],[484],[519,520],"印度 农业人工智能 可持续农业","印度 精准农业 无人机","印度农业人工智能可持续农业-4105",4,"10.31235\u002Fosf.io\u002Fg7ypt_v1",{"doi":523,"openalex_id":525,"authors":526,"venue":9,"cited_by_count":114,"oa_url":474,"card":532,"direction":537,"ingested_from":122},"W7215740376",[527,530],{"name":528,"orcid":529},"Seema Wankhade","https:\u002F\u002Forcid.org\u002F0009-0005-6663-3729",{"name":531,"orcid":9},"Subhash Patil",{"tldr":533,"method":534,"finding":535,"direction":121,"opportunity":536},"基于二手数据综述AI在印度农业中提升产量与可持续性的应用及障碍。","使用二手数据，综述精准农业、无人机、病害检测等AI应用。","AI能提升印度农业生产力与可持续性，但受网络、成本、数字素养等制约。","可研究小农户低成本AI采纳路径、本地语言咨询系统及公私合作模式的效果评估。","数字乡村与农业信息化","2026-10-03T23:30:42.370830Z"]