[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3623":3,"related-3623":82},{"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":28,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":58,"slug":61,"view_count":62,"doi":63,"paper":64,"created_at":81},3623,"Artificial Intelligence Literacy as a Predictor of Readiness for Digital Agriculture among Agricultural Education Students in Colleges of Education in South-East Nigeria","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22979479","Artificial Intelligence Literacy as a Predictor of Readiness for Digital Agriculture among Agricultural Education Students in Colleges of Education in South-East Nigeria。Zenodo (CERN European Organization for Nuclear Research)","人工智能素养作为尼日利亚东南部教育学院农业教育专业学生数字农业准备度的预测因素。Zenodo（CERN欧洲核子研究组织）。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-26T00:00:00Z","论文",25,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,15,12,9,1,"尼日利亚农业教育学生AI素养与数字农业就绪度的实证研究，对农业信息化人才培养有参考价值，但属区域性学术成果，影响范围有限。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22979480",[29,30,31,32],"数字农业","农业人工智能","农业教育","尼日利亚",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,53],{"code":47,"score":19,"supported":48,"title":49,"abstract":51,"tags":52},"I1",true,[50],"digital agriculture",[29,50],[29],{"code":34,"score":20,"supported":48,"title":54,"abstract":56,"tags":57},[55],"artificial intelligence",[55],[30],[59,60],"尼日利亚 农业教育 数字农业","AI素养 数字农业 就绪度","尼日利亚农业教育数字农业-3623",3,"10.5281\u002Fzenodo.22979479",{"doi":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":73,"oa_url":6,"card":74,"direction":78,"ingested_from":80},"W7214458041",[67,69,71],{"name":68,"orcid":9},"Dr. Owo Emmanuel Ozuma",{"name":70,"orcid":9},"Abel Charles Izuchukwu",{"name":72,"orcid":9},"Adu Chukwuebuka Jeremiah",0,{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"研究尼日利亚东南部教育学院农教学生的人工智能素养对数字农业准备度的预测作用。","对教育学院农业教育学生进行问卷调查与回归分析。","人工智能素养显著正向预测学生参与数字农业的准备度。","数字乡村与农业信息化","可拓展至不同地区与院校，并探究AI素养培养课程对数字农业采纳的干预效果。","openalex","2026-09-27T23:30:57.296547Z",{"total":83,"page":22,"page_size":83,"items":84},6,[85,151,227,289,333,409],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":9,"source_name":10,"source_url":88,"published_at":91,"category":12,"cover_url":9,"hotness":92,"is_selected":14,"score":93,"score_detail":94,"sources":97,"tags":99,"primary_topic":103,"secondary_topics":104,"classification":105,"search_phrases":133,"slug":136,"view_count":22,"doi":137,"paper":138,"created_at":150},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",10,54,{"impact":17,"substance":95,"depth":20,"authority":92,"freshness":92,"relevant":22,"comment":96},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。",[98],{"name":10,"url":88},[29,100,30,101,102],"智慧农业","作物推荐","农业决策支持",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":106},{"candidates":107,"agricultural_evidence":48},[108,115,122,127],{"code":34,"score":18,"supported":48,"title":109,"abstract":111,"tags":114},[110],"decision support",[112,113,55,110],"机器学习","machine learning",[30],{"code":116,"score":83,"supported":48,"title":117,"abstract":118,"tags":121},"H2",[],[119,120],"土壤","soil",[],{"code":47,"score":123,"supported":48,"title":124,"abstract":125,"tags":126},5,[],[100],[29,100],{"code":128,"score":62,"supported":48,"title":129,"abstract":130,"tags":132},"I6",[],[131],"agricultural data",[],[134,135],"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":137,"openalex_id":139,"authors":140,"venue":10,"cited_by_count":73,"oa_url":88,"card":143,"direction":149,"ingested_from":80},"W7221331787",[141],{"name":142,"orcid":9},"B. Siddhu, G. Naveen, S. Guru Charan, T. Ajay, C. Naresh",{"tldr":144,"method":145,"finding":146,"direction":147,"opportunity":148},"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。","农业人工智能与决策模型","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。","智慧农业 \u002F 农业物联网","2026-10-11T23:30:07.538409Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":92,"is_selected":14,"score":159,"score_detail":160,"sources":163,"tags":165,"primary_topic":168,"secondary_topics":169,"classification":170,"search_phrases":207,"slug":210,"view_count":211,"doi":212,"paper":213,"created_at":226},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","2026-10-02T00:00:00Z",69,{"impact":18,"substance":161,"depth":95,"authority":20,"freshness":21,"relevant":22,"comment":162},16,"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。",[164],{"name":157,"url":154},[29,100,30,166,167],"精准农业","印度农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":171},{"candidates":172,"agricultural_evidence":48},[173,177,182,189,196,202],{"code":34,"score":20,"supported":48,"title":174,"abstract":175,"tags":176},[55],[55],[30],{"code":47,"score":21,"supported":48,"title":178,"abstract":179,"tags":181},[],[166,180],"precision agriculture",[29,100,166],{"code":183,"score":83,"supported":48,"title":184,"abstract":185,"tags":188},"A8",[],[186,187],"粮食安全","food security",[],{"code":190,"score":83,"supported":48,"title":191,"abstract":192,"tags":195},"H7",[],[193,194],"气候变化","climate change",[],{"code":197,"score":62,"supported":48,"title":198,"abstract":199,"tags":201},"H8",[],[200],"可持续农业",[],{"code":203,"score":22,"supported":14,"title":204,"abstract":205,"tags":206},"J3",[],[],[167],[208,209],"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":212,"openalex_id":214,"authors":215,"venue":9,"cited_by_count":73,"oa_url":154,"card":221,"direction":78,"ingested_from":80},"W7215740376",[216,219],{"name":217,"orcid":218},"Seema Wankhade","https:\u002F\u002Forcid.org\u002F0009-0005-6663-3729",{"name":220,"orcid":9},"Subhash Patil",{"tldr":222,"method":223,"finding":224,"direction":149,"opportunity":225},"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。","2026-10-03T23:30:42.370830Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":232,"content":9,"source_name":233,"source_url":230,"published_at":234,"category":12,"cover_url":9,"hotness":92,"is_selected":14,"score":159,"score_detail":235,"sources":237,"tags":239,"primary_topic":241,"secondary_topics":242,"classification":243,"search_phrases":272,"slug":275,"view_count":22,"doi":276,"paper":277,"created_at":288},3899,"Leveraging on Artificial Intelligence Technology in Boosting Cassava Production in Nigeria: The Gains and Constraints","https:\u002F\u002Fdoi.org\u002F10.64348\u002Fzije.2026560","Artificial Intelligence (AI) technology is reshaping agricultural landscape globally by employing data-driven, efficient, and scalable production methods. Nigeria, as the world’s largest producer of cassava, faces persistent challenges like low yield per hectare, pest infestations, climate variability, and inefficient farming techniques. This study focuses on the potential of AI technology to exploit and enhance the nation’s agricultural potentials and productivity. The study decisively examines the application of AI in cassava production in Nigeria, focusing on its gains and constraints. It adopted ways to addressing the critical challenges like climate variability, scarce access to digital farming techniques, and the need for sustainable practices. Using a descriptive and analytical approach based on secondary data from FAO, NBS, IITA, and recent scholarly publications, the study explores AI-driven technologies such as precision agriculture, machine learning, remote sensing, and automation. Findings from the study reveal that AI technology application in cassava production can significantly improve productivity, optimize resource utilization, and reduce post-harvest losses. However, adoption can be constrained by high costs, poor infrastructure, low digital literacy, and weak policy frameworks. This paper investigates how AI technology can be employed to achieve the mass production of cassava in Nigeria, while critically examining the associated gains or benefits and constraints.","人工智能（AI）技术正通过采用数据驱动、高效且可扩展的生产方式重塑全球农业格局。尼日利亚作为世界最大的木薯生产国，长期面临单位面积产量低、病虫害侵袭、气候多变以及耕作技术低效等挑战。本研究聚焦于AI技术在开发与提升该国农业潜力及生产力方面的潜力，系统考察AI在尼日利亚木薯生产中的应用，重点关注其收益与制约因素。研究探讨了应对气候多变、数字农业技术获取困难以及可持续实践需求等关键挑战的途径。基于联合国粮农组织（FAO）、尼日利亚国家统计局（NBS）、国际热带农业研究所（IITA）及近期学术文献的二手数据，采用描述性与分析性方法，探讨了精准农业、机器学习、遥感及自动化等AI驱动技术。研究发现，AI技术在木薯生产中的应用可显著提高生产力、优化资源利用并减少采后损失。然而，其推广可能受制于高成本、基础设施薄弱、数字素养偏低及政策框架不健全等因素。本文探讨如何运用AI技术实现尼日利亚木薯的大规模生产，同时批判性地审视相关收益与制约因素。","Federal University Gusau Faculty of Education Journal","2026-09-30T00:00:00Z",{"impact":18,"substance":161,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":236},"基于二手数据的综述性论文，系统梳理AI在尼日利亚木薯生产中的增益与制约，对非洲农业数字化有参考价值，但方法创新与一手数据有限。",[238],{"name":233,"url":230},[100,30,166,240,32],"木薯",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":244},{"candidates":245,"agricultural_evidence":48},[246,250,255,262,267],{"code":34,"score":18,"supported":48,"title":247,"abstract":248,"tags":249},[55],[112,113,55],[30],{"code":47,"score":251,"supported":48,"title":252,"abstract":253,"tags":254},11,[],[166,29,180],[100,166],{"code":256,"score":83,"supported":48,"title":257,"abstract":258,"tags":261},"I3",[],[259,260],"遥感","remote sensing",[],{"code":203,"score":62,"supported":48,"title":263,"abstract":264,"tags":266},[],[265],"全球农业",[],{"code":268,"score":22,"supported":14,"title":269,"abstract":270,"tags":271},"A5",[],[],[240],[273,274],"尼日利亚 木薯 人工智能","木薯 精准农业 遥感","尼日利亚木薯人工智能-3899","10.64348\u002Fzije.2026560",{"doi":276,"openalex_id":278,"authors":279,"venue":233,"cited_by_count":73,"oa_url":230,"card":282,"direction":287,"ingested_from":80},"W7214923259",[280],{"name":281,"orcid":9},"Richard Igbunu",{"tldr":283,"method":284,"finding":285,"direction":147,"opportunity":286},"分析AI在尼日利亚木薯生产中的应用收益与制约因素。","基于FAO、NBS、IITA等二手数据的描述性与分析性研究。","AI可提升木薯产量、优化资源利用并减少采后损失，但受成本、基础设施、数字素养和政策制约。","可针对小农户低成本AI工具、数字素养培训与政策框架开展实证研究。","农业遥感与作物表型","2026-10-01T23:30:29.024444Z",{"id":290,"title":291,"url":292,"summary":293,"summary_zh":9,"content":9,"source_name":294,"source_url":9,"published_at":295,"category":12,"cover_url":9,"hotness":92,"is_selected":14,"score":296,"score_detail":297,"sources":301,"tags":303,"primary_topic":306,"secondary_topics":307,"classification":308,"search_phrases":320,"slug":323,"view_count":211,"doi":9,"paper":324,"created_at":332},3439,"《Agentic Artificial Intelligence in Agriculture: A Systematic Mapping Review of Reported Architectures, Applications, Challenges, and Future Directions》","https:\u002F\u002Fwww.mdpi.com\u002F2227-7080\u002F14\u002F9\u002F591","作者按PRISMA 2020方案从4111项记录筛选至181项研究，对2020—2026年农业代理式人工智能文献做系统映射综述：领域跨度30多年但LLM子集非常年轻（2024年才出现、46\u002F47项发表于2025—2026年）；82%研究报告合作能力，计划和推理分别仅31%、记忆6%、反思4%；只有33项研究报告现场或实际部署，68项仍停留在概念性阶段，仅11项报告了一个季度以上的评估。","《Technologies》2026, 14(9), 591 \u002F MDPI","2026-09-22T00:00:00Z",80,{"impact":18,"substance":298,"depth":18,"authority":299,"freshness":17,"relevant":22,"comment":300},23,13,"基于PRISMA的农业代理式AI系统映射综述，量化揭示LLM应用年轻化与落地不足，信息增量与专业深度突出，值得进入每日精选。",[302],{"name":294,"url":292},[29,100,30,304,305],"农业大模型","智能体",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":309},{"candidates":310,"agricultural_evidence":48},[311,315],{"code":34,"score":251,"supported":48,"title":312,"abstract":313,"tags":314},[55],[],[30,304,305],{"code":47,"score":316,"supported":14,"title":317,"abstract":318,"tags":319},2,[],[],[29,100],[321,322],"农业代理式人工智能 系统映射综述","农业人工智能 农业大模型 数字农业 智慧农业","农业代理式人工智能系统映射综述-3439",{"doi":9,"openalex_id":9,"authors":325,"venue":9,"cited_by_count":73,"oa_url":9,"card":326,"direction":147,"ingested_from":331},[],{"tldr":327,"method":328,"finding":329,"direction":147,"opportunity":330},"系统映射181项研究，梳理农业代理式AI的架构、应用、挑战与未来方向。","按PRISMA 2020筛选4111项记录至181项，做系统映射综述。","LLM代理2024年才出现，多具合作能力但规划、记忆、反思薄弱，实际部署少。","农业LLM代理的长期田间部署、记忆与反思机制及跨季度评估仍是明显空白。","agent","2026-09-25T00:09:33.995675Z",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":339,"source_url":336,"published_at":340,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":341,"score_detail":342,"sources":344,"tags":349,"primary_topic":351,"secondary_topics":356,"classification":358,"search_phrases":376,"slug":379,"view_count":211,"doi":380,"paper":381,"created_at":408},3358,"One Toolchain, Six Domains: A Multiple-Case, Document-Based Study of Rapid IoT Prototypes Built in a One-Week Immersive Course on a Master’s Program in Applied Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.2011.v1","This paper reports a document-based, multiple-case study of six Internet-of-Things (IoT) prototypes designed and simulated during a one-week immersive course, “IoT for Data Intelligence,” delivered in July 2026 within the professional Master in Applied Artificial Intelligence (Maestría en Inteligencia Artificial Aplicada, MNA) at Tecnológico de Monterrey. Six teams followed the same five-day toolchain IoT theory; Oracle Application Express (APEX), SQL, and REST service design; MIT App Inventor; ESP32\u002FWokwi simulation; and generative-AI integration and produced Wokwi-simulated prototypes spanning industrial energy monitoring, agricultural hazard response, residential automation, cardiovascular telemonitoring, industrial waste reduction, and precision agriculture. A fixed coding framework was applied across architecture, AI-integration pattern, platform-level failure modes, security debt, and Sustainable Development Goal alignment, distinguishing findings that the course structure itself prescribes from findings the teams introduced independently. The six cases converged on a shared five-layer architecture and, in a pattern only partly prescribed by the course, on keeping generative AI in an advisory or fail-safe-wrapped role. Deposited results were also compared, for illustrative purposes only, against the course’s internal competency rubric. An observed proposal from a Pontifical Catholic University of Chile’s collaboration is discussed as an informal reference point rather than as evidence for generalization. This paper discusses the implications and limits of this small, single-institution, single-cohort, simulation-only case set.","本文报告了一项基于文档的多案例研究，研究对象为六项物联网（Internet of Things, IoT）原型，这些原型是在2026年7月于蒙特雷理工学院（Tecnológico de Monterrey）应用人工智能专业硕士（Maestría en Inteligencia Artificial Aplicada, MNA）项目内开设的一周沉浸式课程“面向数据智能的物联网”（IoT for Data Intelligence）中设计与仿真的。六个团队遵循了相同的五日工具链——物联网理论；Oracle Application Express（APEX）、SQL与REST服务设计；MIT App Inventor；ESP32\u002FWokwi仿真；以及生成式AI集成——并产出了基于Wokwi仿真的原型，涵盖工业能源监测、农业灾害响应、住宅自动化、心血管远程监护、工业减废和精准农业。研究采用固定编码框架，从架构、AI集成模式、平台级失效模式、安全债务和可持续发展目标对齐五个维度进行分析，并区分了课程结构本身所规定的发现与各团队独立引入的发现。六个案例收敛于一个共享的五层架构，并在一种仅部分由课程规定的模式中，将生成式AI保持在顾问性或故障安全包裹的角色中。所提交的成果还仅出于示例目的与课程内部能力量规进行了比较。智利天主教大学一项合作中提出的方案作为非正式参照点加以讨论，而非作为可推广的证据。本文讨论了这一小型、单一机构、单一批次、仅仿真案例集的启示与局限。","Preprints.org","2026-09-23T00:00:00Z",50,{"impact":83,"substance":161,"depth":19,"authority":211,"freshness":21,"relevant":22,"comment":343},"单校单期小样本的预印本教学案例研究，含农业物联网原型与生成式AI集成经验，但样本与仿真局限明显，公共价值有限。",[345,346],{"name":339,"url":336},{"name":347,"url":348},"Computers","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fcomputers15100663",[100,30,350,166,31],"农业物联网",{"code":352,"slug":353,"name":354,"path":355,"parent_code":38},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[357],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":359},{"candidates":360,"agricultural_evidence":48},[361,368,372],{"code":352,"score":18,"supported":48,"title":362,"abstract":364,"tags":367},[363],"IoT",[365,366,363],"物联网","internet of things",[350],{"code":34,"score":20,"supported":48,"title":369,"abstract":370,"tags":371},[55],[55],[30],{"code":47,"score":17,"supported":48,"title":373,"abstract":374,"tags":375},[],[166,180],[100,166],[377,378],"Tecnológico de Monterrey 物联网 课程","ESP32 Wokwi 农业物联网 原型","TecnológicodeMonterrey物联网课程-3358","10.20944\u002Fpreprints202609.2011.v1",{"doi":380,"openalex_id":382,"authors":383,"venue":339,"cited_by_count":73,"oa_url":336,"card":402,"direction":149,"ingested_from":80},"W7214071608",[384,387,390,393,396,399],{"name":385,"orcid":386},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":388,"orcid":389},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":391,"orcid":392},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":394,"orcid":395},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":397,"orcid":398},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":400,"orcid":401},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":403,"method":404,"finding":405,"direction":406,"opportunity":407},"基于六组一周IoT课程原型文档，分析其架构、AI集成与安全模式。","文档多案例研究，固定编码框架，Wokwi仿真与生成式AI集成。","六案例收敛于五层架构，生成式AI多限于建议或故障保护角色。","其他","可探究仿真原型向真实农田部署时，安全债务与AI角色如何演变。","2026-09-24T23:30:13.353443Z",{"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":92,"is_selected":14,"score":417,"score_detail":418,"sources":422,"tags":424,"primary_topic":427,"secondary_topics":433,"classification":435,"search_phrases":471,"slug":474,"view_count":316,"doi":475,"paper":476,"created_at":488},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。","Journal of Scientific Research and Reports","2026-09-09T00:00:00Z",76,{"impact":18,"substance":419,"depth":420,"authority":299,"freshness":17,"relevant":22,"comment":421},20,17,"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。",[423],{"name":415,"url":412},[29,100,30,425,426],"病虫害监测","精准植保",{"code":428,"slug":429,"name":430,"path":431,"parent_code":432},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[434],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":436},{"candidates":437,"agricultural_evidence":48},[438,444,451,459,463,467],{"code":47,"score":439,"supported":48,"title":440,"abstract":441,"tags":443},19,[50],[29,442,50],"数字孪生",[29,100],{"code":428,"score":420,"supported":48,"title":445,"abstract":447,"tags":450},[446],"crop protection",[448,449,446],"农药","pesticide",[],{"code":34,"score":92,"supported":48,"title":452,"abstract":453,"tags":458},[],[454,112,455,113,456,55,457,110],"深度学习","计算机视觉","deep learning","computer vision",[30],{"code":256,"score":83,"supported":48,"title":460,"abstract":461,"tags":462},[],[259,260],[],{"code":352,"score":83,"supported":48,"title":464,"abstract":465,"tags":466},[],[365,366],[],{"code":197,"score":62,"supported":48,"title":468,"abstract":469,"tags":470},[],[200],[],[472,473],"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","10.9734\u002Fjsrr\u002F2026\u002Fv32i94503",{"doi":475,"openalex_id":477,"authors":478,"venue":415,"cited_by_count":73,"oa_url":412,"card":483,"direction":149,"ingested_from":80},"W7212081296",[479,481],{"name":480,"orcid":9},"R. Saritha",{"name":482,"orcid":9},"M. Swathi",{"tldr":484,"method":485,"finding":486,"direction":147,"opportunity":487},"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。","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。","2026-09-10T23:30:09.692422Z"]