[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3548":3,"related-3548":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":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":68},3548,"Cybersecurity and Privacy in AI-Enabled Agricultural IoT Ecosystems: A Systematic Review of Threats, Safeguards, and Resilience Gaps","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fa19100827","Agricultural Internet of Things (IoT) ecosystems increasingly connect sensors, drones, edge devices, and cloud platforms to support precision farming, yet cybersecurity, privacy, and the real-world readiness of proposed safeguards remain fragmented across the literature. This study systematically reviewed cybersecurity threats, privacy concerns, AI-driven and traditional safeguards, and evidence gaps in agricultural IoT research published between 2015 and 2025. Following the Kitchenham and Charters methodology, 103 studies were selected from 2535 records retrieved across five databases. STRIDE and LINDDUN were retrospectively applied as complementary frameworks for threat and privacy classification. Because the coding scheme was multi-label, reliability was assessed at the category level using presence\u002Fabsence decisions on a 20-study sample and observed agreement ranged from 75% to 95% for STRIDE and 95% to 100% for LINDDUN, with interpretable Cohen’s κ values ranging from 0.348 to 0.794 and 0.875 to 1.000, respectively. All included studies also underwent quality appraisal and a supplementary ecological-validity assessment. Denial-of-service, tampering, and spoofing were the most frequently reported threats, concentrated at the device, network, and cloud layers, while the edge layer remained underexamined. AI- and machine-learning-based intrusion detection and privacy-preserving methods such as federated learning emerged as prominent safeguards, but adversarial manipulation of agricultural AI models received limited attention. Privacy research remained oriented toward confidentiality, with 90.3% of studies referencing no applicable regulatory framework. Most importantly, only 8 of 103 studies (7.8%) received a High ecological-validity rating, showing how rarely the evidence base is grounded in real agricultural field conditions. The review identifies field-grounded evaluation, adversarially robust AI, privacy governance, and cyber resilience as priorities for future agricultural IoT security research.","农业物联网（IoT）生态系统日益将传感器、无人机、边缘设备和云平台连接起来，以支持精准农业，然而网络安全、隐私以及所提出保障措施的现实适用性在文献中仍呈现碎片化状态。本研究系统综述了2015年至2025年间发表的农业物联网研究中的网络安全威胁、隐私问题、人工智能驱动及传统保障措施以及证据缺口。遵循Kitchenham和Charters方法论，从五个数据库检索到的2535条记录中筛选出103项研究。STRIDE和LINDDUN被回溯性应用为威胁与隐私分类的互补框架。由于编码方案为多标签，可靠性在类别层面通过20项研究样本的存在\u002F缺失判定进行评估，STRIDE的观察一致率为75%至95%，LINDDUN为95%至100%，可解释的Cohen's κ值分别为0.348至0.794和0.875至1.000。所有纳入研究还接受了质量评价和补充性生态效度评估。拒绝服务、篡改和欺骗是报告最频繁的威胁，集中在设备层、网络层和云层，而边缘层仍未被充分考察。基于人工智能和机器学习的入侵检测以及联邦学习等隐私保护方法成为突出的保障措施，但农业人工智能模型的对抗性操纵受到的关注有限。隐私研究仍以保密性为导向，90.3%的研究未引用任何适用的监管框架。最重要的是，103项研究中仅有8项（7.8%）获得高生态效度评级，表明证据基础鲜有扎根于真实农业田间条件。本综述将田间实证评估、对抗鲁棒人工智能、隐私治理和网络韧性确定为未来农业物联网安全研究的优先事项。",null,"Algorithms","2026-09-25T00:00:00Z","论文",10,true,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,23,19,13,9,1,"系统综述103项研究，揭示农业物联网安全证据多脱离田间实际，对智慧农业安全研究有较高参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","农业物联网","隐私保护","数据安全",[33,34],"农业物联网 网络安全","农业AI 隐私保护 联邦学习","农业物联网网络安全-3548",0,"10.3390\u002Fa19100827",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":61,"direction":65,"ingested_from":67},"W7214403401",[41,44,47,50,53,56,58],{"name":42,"orcid":43},"Emmanuel Kojo Gyamfi","https:\u002F\u002Forcid.org\u002F0009-0002-0441-2830",{"name":45,"orcid":46},"Jess Kropczynski","https:\u002F\u002Forcid.org\u002F0000-0002-7458-6003",{"name":48,"orcid":49},"Jacques Bou Abdo","https:\u002F\u002Forcid.org\u002F0000-0002-3482-9154",{"name":51,"orcid":52},"Joseph Samuel Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-2555-8142",{"name":54,"orcid":55},"Mustapha Awinsongya Yakubu","https:\u002F\u002Forcid.org\u002F0009-0005-6623-0858",{"name":57,"orcid":9},"Anthony Tsetse",{"name":59,"orcid":60},"Gertrude Kaneah Abagale","https:\u002F\u002Forcid.org\u002F0009-0009-1502-3188",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"系统综述2015-2025年农业物联网的网络安全、隐私威胁与防护措施及证据缺口。","Kitchenham系统综述法，筛选103项研究，用STRIDE与LINDDUN","拒绝服务、篡改、欺骗威胁最多，边缘层研究不足，仅7.8%研究具高生态效度。","智慧农业 \u002F 农业物联网","农业AI模型的对抗鲁棒性、边缘层安全、隐私治理与真实田间条件下的韧性评估是明显空白。","openalex","2026-09-26T23:30:15.415998Z",{"total":70,"page":22,"page_size":70,"items":71},6,[72,125,162,215,249,273],{"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":80,"score":81,"score_detail":82,"sources":87,"tags":89,"search_phrases":92,"slug":95,"view_count":36,"doi":96,"paper":97,"created_at":124},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",false,50,{"impact":70,"substance":83,"depth":84,"authority":85,"freshness":21,"relevant":22,"comment":86},16,15,4,"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。",[88],{"name":78,"url":75},[27,28,29,90,91],"精准农业","农业教育",[93,94],"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":96,"openalex_id":98,"authors":99,"venue":78,"cited_by_count":36,"oa_url":75,"card":118,"direction":65,"ingested_from":67},"W7214071608",[100,103,106,109,112,115],{"name":101,"orcid":102},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":104,"orcid":105},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":107,"orcid":108},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":110,"orcid":111},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":113,"orcid":114},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":116,"orcid":117},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":119,"method":120,"finding":121,"direction":122,"opportunity":123},"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":126,"title":127,"url":128,"summary":129,"summary_zh":130,"content":9,"source_name":131,"source_url":128,"published_at":132,"category":12,"cover_url":9,"hotness":133,"is_selected":80,"score":134,"score_detail":135,"sources":138,"tags":142,"search_phrases":146,"slug":149,"view_count":36,"doi":150,"paper":151,"created_at":161},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":136,"substance":17,"depth":83,"authority":20,"freshness":36,"relevant":22,"comment":137},22,"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。",[139,140],{"name":131,"url":128},{"name":131,"url":141},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[143,27,28,29,144,145],"数字乡村","气候韧性","遥感监测",[147,148],"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":150,"openalex_id":152,"authors":153,"venue":131,"cited_by_count":36,"oa_url":128,"card":156,"direction":65,"ingested_from":67},"W7214083098",[154],{"name":155,"orcid":9},"Twinkal Prakash Sawant",{"tldr":157,"method":158,"finding":159,"direction":65,"opportunity":160},"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":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":9,"source_name":168,"source_url":165,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":80,"score":170,"score_detail":171,"sources":174,"tags":176,"search_phrases":179,"slug":182,"view_count":36,"doi":183,"paper":184,"created_at":214},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。","Journal of Applied Business and Technology","2026-09-24T00:00:00Z",62,{"impact":172,"substance":17,"depth":83,"authority":13,"freshness":13,"relevant":22,"comment":173},8,"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。",[175],{"name":168,"url":165},[177,27,28,29,178],"数字农业","畜牧养殖",[180,181],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347","10.35145\u002F6e5wnv18",{"doi":183,"openalex_id":185,"authors":186,"venue":168,"cited_by_count":36,"oa_url":165,"card":209,"direction":65,"ingested_from":67},"W7214075234",[187,189,191,193,195,197,200,203,205,207],{"name":188,"orcid":9},"Nicholas Renaldo",{"name":190,"orcid":9},"Sulaiman Musa",{"name":192,"orcid":9},"Jaswar Koto",{"name":194,"orcid":9},"Kristy Veronica",{"name":196,"orcid":9},"Umar Faruq",{"name":198,"orcid":199},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":201,"orcid":202},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":204,"orcid":9},"Wilda Susanti",{"name":206,"orcid":9},"Achmad Tavip Junaedi",{"name":208,"orcid":9},"Nabila Wahid",{"tldr":210,"method":211,"finding":212,"direction":65,"opportunity":213},"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。","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。","2026-09-24T23:30:09.863218Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":9,"source_name":221,"source_url":218,"published_at":222,"category":12,"cover_url":9,"hotness":13,"is_selected":80,"score":223,"score_detail":224,"sources":228,"tags":230,"search_phrases":232,"slug":235,"view_count":36,"doi":236,"paper":237,"created_at":248},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":225,"substance":226,"depth":84,"authority":13,"freshness":172,"relevant":22,"comment":227},12,14,"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。",[229],{"name":221,"url":218},[27,28,29,90,231],"植物病害检测",[233,234],"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":236,"openalex_id":238,"authors":239,"venue":221,"cited_by_count":36,"oa_url":9,"card":242,"direction":65,"ingested_from":67},"W7213950095",[240],{"name":241,"orcid":9},"Jamuna Ratcha",{"tldr":243,"method":244,"finding":245,"direction":246,"opportunity":247},"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":250,"title":251,"url":252,"summary":253,"summary_zh":9,"content":254,"source_name":255,"source_url":9,"published_at":256,"category":257,"cover_url":9,"hotness":13,"is_selected":14,"score":258,"score_detail":259,"sources":263,"tags":265,"search_phrases":268,"slug":271,"view_count":36,"doi":9,"paper":9,"created_at":272},3022,"农业农村部党组召开会议强调：大力推进\"人工智能+\"农业 拓展无人机、物联网等应用场景","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202609\u002Ft20260920_8872540.htm","农业农村部党组9月20日召开会议，传达学习习近平总书记关于山东青岛市北海造船厂一货轮火灾事故的重要指示精神，部署农业安全生产、农机装备产业发展等工作。会议强调，要大力推进\"人工智能+\"农业，拓展无人机、物联网等应用场景，让新质生产力更好赋能现代农业发展；要全力抓好\"三秋\"生产，分区域分作物指导抓细秋粮田管，强化农业防灾减灾救灾，精心组织开展秋收，压茬推进秋冬种，确保秋粮丰收到手、冬小麦冬油菜种足种好。","[![Image 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9月20日，农业农村部党组召开会议。部党组书记、部长张柱主持会议。\n\n会议传达学习习近平总书记关于山东青岛市北海造船厂一货轮火灾事故的重要指示精神，强调要深入学习领会，抓好贯彻落实，按照李强总理批示要求和国务院常务会议部署，时刻绷紧安全生产这根弦，树牢极限思维、底线思维，严格按照“三管三必须”要求，进一步压紧压实责任，全面加强农业安全生产监管，紧盯海洋渔船、农机、有限空间等重点领域，深入排查整治风险隐患，逐项整改落实到位，细化防范救援措施，坚决遏制重特大事故发生，以高水平安全保障农业农村高质量发展。\n\n会议学习贯彻习近平总书记关于发展先进制造业的重要指示精神，强调要把培育壮大农机装备产业作为农业现代化的关键支撑，坚持智能化、绿色化、融合化发展方向，聚焦高端智能、丘陵山区适用农机等突出短板，集中优势资源力量，攻关突破一批标志性整机和关键共性技术，加快中试验证 和 熟化 应用，让农业生产更多领域“有机可用、有好机用”。要大力推进“人工智能+”农业，拓展无人机、物联网等应用场景，让新质生产力更好赋能现代农业发展。\n\n会议强调，要全力抓好“三秋”生产，分区域分作物指导抓细秋粮田管，强化农业防灾减灾救灾，精心组织开展秋收，压茬推进秋冬种，确保秋粮丰收到手，冬小麦冬油菜种足种好。要着力强化农业科技装备支撑，系统谋划推进高标准农田建设，加快农业科技成果集成推广，加强优良品种选育，大力推广水肥一体化技术模式，加力推进粮油作物大面积单产提升。要积极稳妥深化农村改革，有序推进第二轮土地承包到期后再延长30年试点，推动农业社会化服务扩面提质，促进小农户和现代农业发展有机衔接。\n\n会议还研究了其他事项。\n\n![Image 3](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fnxw_back.png)\n返回顶部\n\n*   [外交部](https:\u002F\u002Fwww.fmprc.gov.cn\u002Fweb\u002F)\n*   [国防部](http:\u002F\u002Fwww.mod.gov.cn\u002F)\n*   [国家发展和改革委员会](https:\u002F\u002Fwww.ndrc.gov.cn\u002F)\n*   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人工智能","农业无人机 物联网 应用场景","农业农村部人工智能-3022","2026-09-21T00:04:31.528736Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":9,"source_name":279,"source_url":276,"published_at":280,"category":12,"cover_url":9,"hotness":13,"is_selected":80,"score":281,"score_detail":282,"sources":284,"tags":286,"search_phrases":289,"slug":292,"view_count":36,"doi":293,"paper":294,"created_at":308},3010,"A Multi-Task Stacked Ensemble and IoT-Enabled Decision Support System for Precision Fertigation in Smallholder Agriculture","https:\u002F\u002Fdoi.org\u002F10.5120\u002Fijca9a6e56b81235","Nigerian agriculture's fixed-schedule fertigation causes low efficiency and nutrient leaching.A stacked-ensemble model is developed for precision fertigation that jointly predicts fertigation need, rate (kg\u002Fha) and timing (Early\u002FOptimal\u002FLate).To train and evaluate the ensemble, a unified dataset was integrated, comprising 12,840 records and 42 variables from a Nigerian soil-weather-yield dataset, a locally sourced Nigerian IoT sensor series and historical weather\u002FNDVI feeds.An LSTM soil-dynamics model, an XGBoost rate regressor and Random Forest need\u002Ftiming classifiers are fused through an XGBoost meta-learner trained on out-of-fold predictions.On held-out partitions, the ensemble reduced rate MAE from 0.55 to 0.49 kg\u002Fha (-10.9%) and RMSE from 0.68 to 0.61 kg\u002Fha (-10.3%;R² 0.88→0.92),raised need F1 from 0.83 to 0.86 (accuracy 0.87→0.89;AUC 0.89→0.93)and timing macro-F1 from 0.84 to 0.86, with well-calibrated probabilities (Brier 0.082).The trained ensemble was deployed through a RESTful API and responsive dashboard; under concurrent load, the system recorded 0% request errors with 1.88s median API latency, demonstrating practical deployability for Nigerian smallholder agriculture.","尼日利亚农业的固定日程水肥一体化导致效率低下和养分淋失。本研究开发了一种堆叠集成模型用于精准水肥管理，可联合预测灌溉施肥需求、施用量（kg\u002Fha）和时机（早\u002F最佳\u002F晚）。为训练和评估该集成模型，整合了一个统一数据集，包含来自尼日利亚土壤-天气-产量数据集、本地尼日利亚物联网传感器序列及历史天气\u002FNDVI数据的12,840条记录和42个变量。通过基于折外预测训练的XGBoost元学习器，将LSTM土壤动力学模型、XGBoost施用量回归器和随机森林需求\u002F时机分类器进行融合。在留出集上，该集成模型将施用量MAE从0.55降至0.49 kg\u002Fha（-10.9%），RMSE从0.68降至0.61 kg\u002Fha（-10.3%；R² 0.88→0.92），需求F1从0.83提升至0.86（准确率0.87→0.89；AUC 0.89→0.93），时机宏平均F1从0.84提升至0.86，且概率校准良好（Brier 0.082）。训练后的集成模型通过RESTful API和响应式仪表板部署；在并发负载下，系统录得0%请求错误，API延迟中位数为1.88秒，展示了在尼日利亚小农农业中的实际可部署性。","International Journal of Computer Applications","2026-09-18T00:00:00Z",79,{"impact":17,"substance":136,"depth":17,"authority":20,"freshness":172,"relevant":22,"comment":283},"面向小农户的精准水肥一体化多任务集成模型与物联网决策支持系统，数据规模与方法验证扎实，对智慧农业落地有参考价值。",[285],{"name":279,"url":276},[27,28,29,287,288],"小农户","精准灌溉",[290,291],"尼日利亚 精准灌溉 物联网","堆叠集成 施肥决策 小农户","尼日利亚精准灌溉物联网-3010","10.5120\u002Fijca9a6e56b81235",{"doi":293,"openalex_id":295,"authors":296,"venue":279,"cited_by_count":36,"oa_url":276,"card":303,"direction":65,"ingested_from":67},"W7213663876",[297,299,301],{"name":298,"orcid":9},"Awojide S.",{"name":300,"orcid":9},"Ikpotokin F.O.",{"name":302,"orcid":9},"Sadiq F.I.",{"tldr":304,"method":305,"finding":306,"direction":65,"opportunity":307},"构建多任务堆叠集成模型与物联网决策支持系统，实现小农户精准水肥一体化。","LSTM、XGBoost、随机森林堆叠集成，融合尼日利亚土壤气象、IoT与NDV","集成模型将施肥量MAE降低10.9%，需求与时机分类F1提升，系统部署零错误。","可探索多任务集成模型在非洲小农户不同作物与气候区的迁移能力及低成本IoT部署。","2026-09-20T23:30:09.003454Z"]