[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3347":3,"related-3347":69},{"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":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":68},3347,"Integrating Material Flow Cost Accounting and IoT-Based Monitoring for Eco-Efficient Goat Farm Management","https:\u002F\u002Fdoi.org\u002F10.35145\u002F6e5wnv18","Goat farming plays an important role in supporting rural livelihoods, food production, and agricultural sustainability. However, conventional goat farm management often separates environmental monitoring, financial accounting, and livestock management, limiting the ability to identify resource inefficiencies and associated environmental impacts. This study aims to develop and implement GEMBALA (Green Eco-smart Management-Based Automation for Livestock and Accounting), an integrated digital platform that combines Internet of Things (IoT)-based environmental monitoring, Material Flow Cost Accounting (MFCA), emission analysis, artificial intelligence-based livestock management, and analytical reporting. The research employed a research and development approach in collaboration with CV Cahaya Firdaus (Fathur Farm). An IoT sensor prototype was developed, installed, and tested in a real goat farming environment to monitor temperature, humidity, Heat Index (THI), ammonia gas, and dust density. The platform also incorporates MFCA, emission, AI Estrus, AI Health, and analytical reporting modules. The results demonstrate progress toward integrating environmental, economic, and livestock management information within a unified digital platform. However, further validation is required to improve sensor data transmission, synchronization, emission calculations, MFCA data consistency, and AI performance evaluation. The study provides a foundation for eco-economic decision support, sustainable livestock management, and future commercialization of digital livestock technologies.","山羊养殖在支撑农村生计、粮食生产和农业可持续性方面发挥着重要作用。然而，传统的山羊养殖场管理往往将环境监测、财务核算和畜牧管理相互分离，限制了识别资源低效利用及相关环境影响的能力。本研究旨在开发并实施GEMBALA（基于绿色生态智能管理的畜牧与会计自动化平台），这是一个集成了基于物联网（IoT）的环境监测、物料流成本会计（MFCA）、排放分析、基于人工智能的畜牧管理以及分析报告的综合数字平台。研究采用研发方法，与CV Cahaya Firdaus（Fathur Farm）合作开展。研究开发了物联网传感器原型，并在真实山羊养殖环境中进行安装和测试，用于监测温度、湿度、热指数（THI）、氨气和粉尘密度。该平台还整合了MFCA、排放、AI发情检测、AI健康和分析报告模块。结果表明，在将环境、经济和畜牧管理信息整合到统一数字平台方面取得了进展。然而，仍需进一步验证，以改进传感器数据传输、同步、排放计算、MFCA数据一致性以及AI性能评估。本研究为生态经济决策支持、可持续畜牧管理以及数字畜牧技术的未来商业化提供了基础。",null,"Journal of Applied Business and Technology","2026-09-24T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":13,"relevant":20,"comment":21},8,18,16,1,"论文提出融合物联网监测与物料流成本核算的山羊养殖数字平台，方法有创新但尚处原型验证阶段，产业影响有限。",[23],{"name":10,"url":6},[25,26,27,28,29],"数字农业","智慧农业","农业人工智能","农业物联网","畜牧养殖",[31,32],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347",0,"10.35145\u002F6e5wnv18",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":6,"card":61,"direction":65,"ingested_from":67},"W7214075234",[39,41,43,45,47,49,52,55,57,59],{"name":40,"orcid":9},"Nicholas Renaldo",{"name":42,"orcid":9},"Sulaiman Musa",{"name":44,"orcid":9},"Jaswar Koto",{"name":46,"orcid":9},"Kristy Veronica",{"name":48,"orcid":9},"Umar Faruq",{"name":50,"orcid":51},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":53,"orcid":54},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":56,"orcid":9},"Wilda Susanti",{"name":58,"orcid":9},"Achmad Tavip Junaedi",{"name":60,"orcid":9},"Nabila Wahid",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"开发集成物联网监测与物料流成本核算的山羊养殖数字平台GEMBALA。","研发方法，在真实羊场部署物联网传感器并集成MFCA、排放分析与AI模块。","平台初步实现环境、经济与养殖信息整合，但传感器传输与数据一致性仍需验证。","智慧农业 \u002F 农业物联网","可延伸研究物联网数据与MFCA实时耦合的算法优化及AI模块的长期性能验证。","openalex","2026-09-24T23:30:09.863218Z",{"total":70,"page":20,"page_size":70,"items":71},6,[72,112,163,201,224,256],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":77,"content":9,"source_name":78,"source_url":75,"published_at":79,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":85,"tags":87,"search_phrases":90,"slug":93,"view_count":34,"doi":94,"paper":95,"created_at":111},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。","Preprints.org","2026-09-22T00:00:00Z",67,{"impact":19,"substance":82,"depth":83,"authority":70,"freshness":17,"relevant":20,"comment":84},20,17,"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[86],{"name":78,"url":75},[25,26,27,88,89],"巴西农业","农村数字化",[91,92],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367","10.20944\u002Fpreprints202609.1880.v1",{"doi":94,"openalex_id":96,"authors":97,"venue":78,"cited_by_count":34,"oa_url":75,"card":105,"direction":109,"ingested_from":67},"W7214109425",[98,101,103],{"name":99,"orcid":100},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":102,"orcid":9},"Eduardo Dias",{"name":104,"orcid":9},"Lidia Scoton",{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","数字乡村与农业信息化","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","2026-09-24T23:30:27.046035Z",{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":78,"source_url":115,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":125,"tags":127,"search_phrases":130,"slug":133,"view_count":34,"doi":134,"paper":135,"created_at":162},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。","2026-09-23T00:00:00Z",50,{"impact":70,"substance":19,"depth":121,"authority":122,"freshness":123,"relevant":20,"comment":124},15,4,9,"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。",[126],{"name":78,"url":115},[26,27,28,128,129],"精准农业","农业教育",[131,132],"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":134,"openalex_id":136,"authors":137,"venue":78,"cited_by_count":34,"oa_url":115,"card":156,"direction":65,"ingested_from":67},"W7214071608",[138,141,144,147,150,153],{"name":139,"orcid":140},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":142,"orcid":143},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":145,"orcid":146},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":148,"orcid":149},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":151,"orcid":152},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":154,"orcid":155},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":157,"method":158,"finding":159,"direction":160,"opportunity":161},"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":164,"title":165,"url":166,"summary":167,"summary_zh":168,"content":9,"source_name":169,"source_url":166,"published_at":170,"category":12,"cover_url":9,"hotness":171,"is_selected":14,"score":172,"score_detail":173,"sources":177,"tags":181,"search_phrases":185,"slug":188,"view_count":34,"doi":189,"paper":190,"created_at":200},3357,"AI-Driven Precision Agriculture and Crop Resilience: Integrating Artificial Intelligence, IoT and Remote Sensing for Climate-Resilient Indian Agriculture: A Vision for Viksit Bharat 2047","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914538","Abstract Agriculture is central to India's economic development, food security, rural employment, and the achievement of the Viksit Bharat@2047 vision. However, Indian agriculture faces increasingly complex challenges, including climate variability, water scarcity, soil degradation, pest and disease outbreaks, fragmented landholdings, market uncertainty, and unequal access to agricultural knowledge. These challenges require a transition from conventional, input-intensive agriculture towards data-driven, resource-efficient, climate-resilient and farmer-centric production systems. Agriculture in India is increasingly affected by climate variability, water scarcity, soil degradation, pest and disease outbreaks, and unpredictable weather conditions. These challenges threaten crop productivity and food security, particularly among small and marginal farmers. Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and machine learning offer new opportunities to transform conventional agricultural practices into data-driven precision agriculture systems. This paper presents a conceptual framework for AI-driven precision agriculture aimed at improving crop resilience under changing climatic conditions. Artificial Intelligence (AI), combined with precision agriculture, Internet of Things (IoT), remote sensing, satellite imagery, drones, machine learning, robotics and digital public infrastructure, offers significant opportunities to transform Indian agriculture. AI can support crop and yield prediction, disease and pest identification, weather-based advisories, irrigation optimisation, soil management, crop insurance, market intelligence and early-warning systems. The paper also discusses challenges related to digital inclusion, data governance, affordability, AI reliability, farmer skills, privacy and institutional coordination. It argues that India's objective should not simply be the digitisation of agriculture, but the creation of an intelligent, inclusive and resilient agricultural ecosystem in which technology augments farmer knowledge and decision-making. By 2047, India can aspire to establish globally competitive agriculture that produces more with fewer resources, withstands climate shocks, generates higher and more stable farm incomes, and ensures sustainable food and nutritional security.","摘要 农业对印度的经济发展、粮食安全、农村就业以及“发达印度@2047”愿景的实现至关重要。然而，印度农业面临日益复杂的挑战，包括气候变异性、水资源短缺、土壤退化、病虫害暴发、土地持有碎片化、市场不确定性以及农业知识获取不平等。这些挑战要求从传统的投入密集型农业向数据驱动、资源高效、气候韧性且以农民为中心的生产体系转型。印度农业日益受到气候变异性、水资源短缺、土壤退化、病虫害暴发及不可预测天气条件的影响。这些挑战威胁着作物生产力和粮食安全，尤其是对小农和边缘农民而言。人工智能（AI）、物联网（IoT）、遥感和机器学习为将传统农业实践转变为数据驱动的精准农业系统提供了新机遇。本文提出了一个AI驱动的精准农业概念框架，旨在改善气候变化条件下作物的韧性。人工智能（AI）与精准农业、物联网（IoT）、遥感、卫星影像、无人机、机器学习、机器人技术及数字公共基础设施相结合，为改造印度农业提供了重大机遇。AI可支持作物与产量预测、病虫害识别、基于天气的农事建议、灌溉优化、土壤管理、作物保险、市场情报及预警系统。本文还讨论了与数字包容、数据治理、可负担性、AI可靠性、农民技能、隐私及机构协调相关的挑战。文章认为，印度的目标不应仅仅是农业数字化，而应是创建一个智能、包容且有韧性的农业生态系统，使技术增强农民的知识与决策能力。到2047年，印度有望建立具有全球竞争力的农业，以更少资源生产更多产品，抵御气候冲击，创造更高且更稳定的农业收入，并确保可持续的粮食与营养安全。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,69,{"impact":174,"substance":18,"depth":19,"authority":175,"freshness":34,"relevant":20,"comment":176},22,13,"概念性框架论文，系统梳理AI、IoT与遥感在印度气候韧性农业中的应用与挑战，有参考价值但无实证数据，且发布日期在未来、时效性缺失，暂不宜进入每日精选。",[178,179],{"name":169,"url":166},{"name":169,"url":180},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[182,26,27,28,183,184],"数字乡村","气候韧性","遥感监测",[186,187],"印度 精准农业 AI","农业人工智能 农业物联网 数字乡村 智慧农业","印度精准农业AI-3357","10.5281\u002Fzenodo.22914538",{"doi":189,"openalex_id":191,"authors":192,"venue":169,"cited_by_count":34,"oa_url":166,"card":195,"direction":65,"ingested_from":67},"W7214083098",[193],{"name":194,"orcid":9},"Twinkal Prakash Sawant",{"tldr":196,"method":197,"finding":198,"direction":65,"opportunity":199},"提出AI+物联网+遥感驱动的精准农业概念框架，提升印度气候韧性作物生产。","概念框架分析，整合AI、IoT、遥感、卫星、无人机、机器学习与数字公共基础设施。","印度农业应构建智能、包容、有韧性的生态系统，而非仅数字化，以应对气候与资源挑战。","可实证检验小农户场景下AI+IoT+遥感集成对作物韧性与收入的实际效果及数字包容机制。","2026-09-24T23:30:13.211525Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":9,"content":206,"source_name":207,"source_url":9,"published_at":79,"category":208,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":210,"sources":214,"tags":216,"search_phrases":219,"slug":222,"view_count":34,"doi":9,"paper":9,"created_at":223},3215,"秋分逢丰收节 机器人成主角！浙江田野正被AI\"接管\"——浙江农科院数字农业研究所研发AI眼镜+害虫识别小程序","https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714","9-22 新蓝网专题报道：在湖州德清县农博家庭农场，种植大户王菊仙戴上一副AI眼镜，对着诱杀害虫的黄板轻轻一扫，\"镜片上、手机端，种类、数量、位置等数据瞬间显现\"。这套由浙江省农科院数字农业研究所研发的设备，正将虫害防控从\"事后补救\"推向\"提前预警\"。在湖州吴兴丰盛湾水产种业，\"云眸\"沼虾养殖AI系统能在3-5秒内捕捉沼虾触须末端细微影像，自动生成比对图谱，一旦发现活动异常即刻标记预警，自动投料机器人与水下传感器联动精准计算投喂量，饲料利用率提升12%-15%、巡塘人力节省六成、养殖效益整体提高10%以上。在杭州余杭区径山镇，无人驾驶拖拉机搭载北斗导航系统自主作业。在杭州临平区田立方未来农场，450亩无人智慧农场示范区配套200余个田间传感器和4个物联网微基站，可根据土壤饱和度和实时水位自动确定浇灌量，一亩地一季油菜花可节水约1000吨。浙江省农业农村厅数据显示，截至目前浙江已累计建成数字农业工厂729家、未来农场63家。","![Image 2](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FloginBg-OXMHhVd9.png)\n\n验证码登录\n\n获取验证码\n\n 一键登录 \n\n- [x]  \n\n登录代表同意 《用户协议》及 《隐私政策》\n\n扫码登录\n\n![Image 3](https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714)\n\n鼠标悬浮刷新二维码\n\n打开Z视介扫码登录\n\n![Image 4](blob:http:\u002F\u002Flocalhost\u002F08f361b6a75bd12fbead3ead9d0ae42d)\n\n[![Image 5](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Flogo-DNzgtwnp.png)](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[首页](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[新闻](https:\u002F\u002Fwww.cztv.com\u002Fheadlines)\n\n[文化](https:\u002F\u002Fwww.cztv.com\u002Fculture)\n\n 电视 \n\n 广播 \n\n[专区](https:\u002F\u002Fwww.cztv.com\u002Fzone)\n\n![Image 6](blob:http:\u002F\u002Flocalhost\u002F0062bfa43494c79fb356b384bfc51bc8)\n\n![Image 7: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faibtn_icon-Dqb11ejT.png)\n\n![Image 8](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode1-C1Z3XA1v.png)\n\n![Image 9: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_lxw-BTIOt7tT.png)更多精彩 中国蓝新闻\n\n![Image 10](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode2-CU2IzmPJ.png)\n\n![Image 11: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_zsj-C7ZTF9AF.png)更多精彩 下载Z视介\n\n[![Image 12](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_more_btn-DzzjA4z1.png)](https:\u002F\u002Fzmtv.cztv.com\u002Fcmsh5-share\u002Fprod\u002FcommonDownload\u002Findex.html)\n\n登录\n\n![Image 13: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FcreateCenter_btn-CP1dJyCT.png)\n\n![Image 14: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faiblue-BYHqI38h.png)\n\nNaN-NaN-NaN NaN:NaN\n\n编辑：\n\n作者：\n\n[](javascript:; \"分享到微信\") \n\n![Image 15](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Flogo-DNzgtwnp.png)\n\nCopyright 2009-2024 cztv.com [浙ICP备05052141号-1](https:\u002F\u002Fbeian.miit.gov.cn\u002F#\u002FIntegrated\u002Findex) | [浙公网安备 33010602002235号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=33010602002235) | 信息网络传播视听节目许可证号：1107197\n\n[互联网新闻信息服务许可证号：33120170002](https:\u002F\u002Fwww.cztv.com\u002Fxuke) | 网络文化经营许可证号：浙网文(2023)1495-044号\n\n 地址：浙江省杭州市莫干山路111号 邮政编码：310005 邮箱：cztv@zmg.com.cn\n\n 网上有害信息举报专区 12321网络不良与垃圾信息举报受理中心 网络违法犯罪举报网站 网络举报APP下载 \n\n 违法和不良信息公开举报电话：12377、0571-81089789 有害信息举报邮箱：jubao@12377.cn 涉未成年人有害信息举报电话: 0571-81089789 \n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fvideo)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveTV)\n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fradio)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveRadio)","新蓝网","报道",60,{"impact":19,"substance":211,"depth":212,"authority":123,"freshness":123,"relevant":20,"comment":213},14,12,"省级官媒报道浙江农科院数字农业研究所AI眼镜与害虫识别小程序落地，属智慧农业细分进展，时效性强但正文信息量有限。",[215],{"name":207,"url":204},[25,26,27,217,218],"智能农机","害虫识别",[220,221],"浙江农科院 数字农业研究所 AI眼镜","浙江 害虫识别 小程序","浙江农科院数字农业研究所AI眼镜-3215","2026-09-23T00:04:29.218317Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":229,"content":9,"source_name":230,"source_url":227,"published_at":231,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":232,"score_detail":233,"sources":235,"tags":237,"search_phrases":239,"slug":242,"view_count":34,"doi":243,"paper":244,"created_at":255},3161,"Artificial Intelligence in Plant Disease Detection: An Introduction to Intelligent and Automated Crop Health Monitoring","https:\u002F\u002Fdoi.org\u002F10.59256\u002Fijire.20260705005","Plant diseases are a major challenge in modern agriculture, as they can significantly reduce crop yield, crop quality, and economic productivity. Traditional plant disease detection methods mainly depend on visual inspection and expert knowledge, which can be time-consuming, subjective, and difficult to apply across large agricultural fields. The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), and Computer Vision, has created new opportunities for automated and efficient crop disease detection and crop health monitoring. AI-based plant disease detection systems can analyze plant and leaf images to identify disease-related characteristics such as leaf discoloration, spots, lesions, texture variations, and abnormal growth patterns. Advanced techniques, including Convolutional Neural Networks (CNNs), transfer learning, image processing, image segmentation, and object detection, can be used for plant disease classification and identification of affected regions with high accuracy. This chapter introduces the fundamental concepts of AI-based plant disease detection, covering image acquisition, image preprocessing, feature extraction, model development, disease classification, and performance evaluation. It also examines the applications of AI in precision agriculture, smart agriculture, mobile-based plant disease diagnosis, drone-assisted crop monitoring, IoT-enabled farming, and edge-based agricultural systems. Furthermore, the chapter discusses important challenges such as limited and imbalanced datasets, environmental variations, similar disease symptoms, model generalization, computational requirements, and the need for explainable AI in agricultural applications. Finally, emerging trends and future opportunities are discussed, with emphasis on integrating AI with IoT, remote sensing, agricultural robotics, and multimodal agricultural data. The chapter provides a foundation for understanding how Artificial Intelligence for plant disease detection can support early disease identification, reduce crop losses, optimize agricultural resources, and contribute to sustainable and intelligent farming practices.","植物病害是现代农业面临的一项重大挑战，因为它们会显著降低作物产量、作物品质和经济生产力。传统的植物病害检测方法主要依赖视觉检查和专家知识，这种方式耗时、主观性强，且难以在大规模农田中应用。人工智能（AI）的快速发展，尤其是机器学习（ML）、深度学习（DL）和计算机视觉，为自动化、高效的作物病害检测和作物健康监测创造了新的机遇。基于AI的植物病害检测系统可以分析植物和叶片图像，以识别与病害相关的特征，如叶片变色、斑点、病斑、纹理变化和异常生长模式。包括卷积神经网络（CNN）、迁移学习、图像处理、图像分割和目标检测在内的先进技术，可用于植物病害分类和受影响区域的高精度识别。本章介绍了基于AI的植物病害检测的基本概念，涵盖图像采集、图像预处理、特征提取、模型开发、病害分类和性能评估。本章还探讨了AI在精准农业、智慧农业、基于移动端的植物病害诊断、无人机辅助作物监测、物联网（IoT）赋能农业和边缘农业系统中的应用。此外，本章讨论了重要挑战，如数据集有限且不平衡、环境变化、相似病害症状、模型泛化、计算需求，以及农业应用中可解释AI的需求。最后，讨论了新兴趋势和未来机遇，重点强调将AI与物联网、遥感、农业机器人和多模态农业数据相结合。本章为理解人工智能用于植物病害检测如何支持早期病害识别、减少作物损失、优化农业资源，并促进可持续和智能农业实践提供了基础。","International Journal of Innovative Research in Engineering","2026-09-21T00:00:00Z",59,{"impact":212,"substance":211,"depth":121,"authority":13,"freshness":17,"relevant":20,"comment":234},"系统综述AI在植物病害检测中的应用，内容全面但属教科书式介绍，方法新颖性与数据规模有限，可作为智慧农业主题聚合素材。",[236],{"name":230,"url":227},[26,27,28,128,238],"植物病害检测",[240,241],"AI 植物病害检测","无人机 作物健康监测","AI植物病害检测-3161","10.59256\u002Fijire.20260705005",{"doi":243,"openalex_id":245,"authors":246,"venue":230,"cited_by_count":34,"oa_url":9,"card":249,"direction":65,"ingested_from":67},"W7213950095",[247],{"name":248,"orcid":9},"Jamuna Ratcha",{"tldr":250,"method":251,"finding":252,"direction":253,"opportunity":254},"综述AI在植物病害检测中的应用，涵盖图像采集到模型评估全流程及未来趋势。","综述CNN、迁移学习、图像分割与目标检测在叶片病害识别中的应用。","AI可高精度识别病害，但受限于数据集不足、环境变化与模型泛化能力。","农业人工智能与决策模型","可探索多模态数据融合与可解释AI，提升复杂田间环境下病害检测的泛化能力。","2026-09-22T23:30:11.209653Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":9,"source_name":262,"source_url":259,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":267,"tags":269,"search_phrases":272,"slug":275,"view_count":34,"doi":276,"paper":277,"created_at":295},3065,"A MULTIMODAL FRAMEWORK FOR LIVESTOCK DETECTION AND EPIZOOTIC MONITORING","https:\u002F\u002Fdoi.org\u002F10.67048\u002Fqxju2026ai128iss7m1055","This study proposes an automated computer vision framework to detect and monitor mixed herds of cattle and sheep. By integrating unmanned aerial vehicle (UAV) imagery with ground-level perspectives, a multimodal dataset was curated. Using this data, a lightweight, single-stage object detector (YOLOv8n), tailored for resource-constrained edge devices, was trained and optimised. The integration of computer vision and UAV technology offers agricultural agencies a scalable tool for proactive epizootic surveillance.","本研究提出了一种自动化计算机视觉框架，用于检测和监测牛与羊的混合畜群。通过将无人机（UAV）影像与地面视角相结合，构建了一个多模态数据集。利用该数据，训练并优化了一个轻量级单阶段目标检测器（YOLOv8n），专为资源受限的边缘设备而设计。计算机视觉与无人机技术的融合为农业机构提供了一种可扩展的工具，用于主动进行动物流行病监测。","Agro ilm","2026-09-18T00:00:00Z",71,{"impact":18,"substance":82,"depth":83,"authority":17,"freshness":17,"relevant":20,"comment":266},"多模态无人机视觉框架用于牛羊混群检测与疫病预警，方法具体、面向边缘设备，对智慧畜牧有实用参考价值。",[268],{"name":262,"url":259},[26,27,270,29,271],"无人机遥感","疫病监测",[273,274],"YOLOv8n 牲畜检测","无人机 畜牧 疫病监测","YOLOv8n牲畜检测-3065","10.67048\u002Fqxju2026ai128iss7m1055",{"doi":276,"openalex_id":278,"authors":279,"venue":262,"cited_by_count":34,"oa_url":289,"card":290,"direction":65,"ingested_from":67},"W7213668410",[280,283,286],{"name":281,"orcid":282},"R. N. Khadzhaev","https:\u002F\u002Forcid.org\u002F0009-0001-8124-2399",{"name":284,"orcid":285},"Sa’dulla Avezbayev","https:\u002F\u002Forcid.org\u002F0009-0008-8896-7078",{"name":287,"orcid":288},"Sayfuddin Sharipov","https:\u002F\u002Forcid.org\u002F0000-0002-4232-1369","https:\u002F\u002Fqxjurnal.uz\u002Findex.php\u002Fai\u002Farticle\u002Fdownload\u002F1055\u002F936",{"tldr":291,"method":292,"finding":293,"direction":65,"opportunity":294},"提出多模态计算机视觉框架，用无人机和地面图像检测牛羊并监测疫情。","融合无人机与地面视角构建多模态数据集，训练轻量级YOLOv8n边缘检测器。","轻量级模型可在资源受限设备上实现混合畜群检测，支持主动疫情监测。","可探索多物种、复杂地形下的实时边缘检测与疫情早期预警模型优化。","2026-09-21T23:30:13.271856Z"]