[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1267":3,"related-1267":112},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":43,"search_phrases":89,"slug":92,"view_count":93,"doi":94,"paper":95,"created_at":111},1267,"Artificial Intelligence for Climate-Smart Agriculture: A Review of Applications in Yield Prediction, Weather Forecasting, Irrigation Management and Pest and Disease Detection","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug707","Climate change is increasingly disrupting agricultural systems worldwide through more frequent extreme weather events, shifting rainfall patterns, and rising temperatures. These changes contribute to reduced crop yields, unstable food supplies, and heightened production risks, especially in vulnerable developing regions. In response, ClimateSmart Agriculture (CSA) has been developed as a strategic framework to improve agricultural productivity while enhancing resilience and promoting environmentally sustainable farming practices. Within this framework, Artificial Intelligence (AI) is increasingly recognized as a transformative tool capable of supporting data-driven agricultural decision-making. This review systematically synthesizes recent literature on AI applications in CSA, focusing on four key thematic areas: crop yield prediction, weather and climate forecasting, irrigation and water management, and pest and disease detection under climate stress. Peer-reviewed articles published between 2020 and 2026 were retrieved from Google Scholar, Semantic Scholar, and Crossref. A total of 170 studies were initially identified, of which 58 highly relevant articles were selected following screening based on relevance, quality, and thematic alignment. Findings indicate that AIbased systems significantly improve predictive accuracy, optimize resource use, and enhance early warning capabilities across agricultural systems. Machine learning, deep learning, remote sensing, IoT, and big data analytics are widely applied to support precision agriculture and climate adaptation strategies. However, key challenges such as data scarcity in developing regions, high implementation costs, limited farmer adoption, and climate uncertainty continue to constrain widespread adoption. The review concludes that AI holds strong potential to transform CSA by improving productivity, resilience, and sustainability. Nonetheless, its effectiveness depends on the development of accessible, affordable, and context-specific solutions supported by strong policy frameworks and improved digital infrastructure.",null,"International Journal of Innovative Science and Research Technology (IJISRT)","2026-08-29T00:00:00Z","论文",10,false,67,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,20,8,3,1,"系统综述AI在气候智慧农业中的应用，覆盖产量预测、灌溉、病虫害等，信息量大，但来源权威性一般且时效性较低。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","综述","精准农业","气候变化",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":50},[49,57,65,70,77,83],{"code":31,"score":16,"supported":50,"title":51,"abstract":53,"tags":56},true,[52],"artificial intelligence",[54,55,52],"machine learning","deep learning",[26],{"code":58,"score":59,"supported":50,"title":60,"abstract":62,"tags":64},"I1",13,[61],"smart agriculture",[63],"precision agriculture",[25,28],{"code":38,"score":66,"supported":50,"title":67,"abstract":68,"tags":69},11,[39],[39],[],{"code":71,"score":72,"supported":50,"title":73,"abstract":74,"tags":76},"H7",4,[],[75],"climate change",[29],{"code":78,"score":19,"supported":50,"title":79,"abstract":80,"tags":82},"I3",[],[81],"remote sensing",[],{"code":84,"score":19,"supported":50,"title":85,"abstract":86,"tags":88},"I5",[],[87],"IoT",[],[90,91],"农业人工智能 智慧农业 气候变化 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业气候变化精准农业-1267",2,"10.38124\u002Fijisrt\u002F26aug707",{"doi":94,"openalex_id":96,"authors":97,"venue":9,"cited_by_count":101,"oa_url":102,"card":103,"direction":109,"ingested_from":110},"W7204643386",[98],{"name":99,"orcid":100},"Sonny Gad Attipoe","https:\u002F\u002Forcid.org\u002F0000-0002-8981-6756",0,"https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26AUG707.pdf",{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"综述AI在气候智慧农业中的应用，涵盖产量预测、天气预报、灌溉管理和病虫害检测。","系统综述2020-2026年文献，筛选58篇相关研究，分析ML、DL、遥感、Io","AI显著提升预测精度和资源利用，但面临数据稀缺、成本高、采纳率低等挑战。","农业人工智能与决策模型","可研究低成本、易用的AI解决方案，结合政策与数字基础设施，促进发展中国家CSA采纳。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T04:03:12.892133Z",{"total":113,"page":20,"page_size":113,"items":114},6,[115,216,285,385,441,542],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":8,"source_name":121,"source_url":118,"published_at":122,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":123,"score_detail":124,"sources":128,"tags":130,"primary_topic":133,"secondary_topics":134,"classification":140,"search_phrases":192,"slug":195,"view_count":101,"doi":196,"paper":197,"created_at":215},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":16,"substance":125,"depth":16,"authority":59,"freshness":126,"relevant":20,"comment":127},22,9,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[129],{"name":121,"url":118},[25,26,131,28,132],"物联网","气候韧性",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[135,139],{"code":84,"slug":136,"name":137,"path":138,"parent_code":35},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":141},{"candidates":142,"agricultural_evidence":50},[143,151,156,162,168,173,180,186],{"code":31,"score":16,"supported":50,"title":144,"abstract":145,"tags":150},[52],[146,147,148,54,55,149],"深度学习","机器学习","计算机视觉","computer vision",[26],{"code":58,"score":152,"supported":50,"title":153,"abstract":154,"tags":155},16,[63],[28,63],[25,28],{"code":84,"score":12,"supported":50,"title":157,"abstract":158,"tags":161},[],[131,159,87,160],"边缘计算","edge computing",[131],{"code":38,"score":126,"supported":50,"title":163,"abstract":164,"tags":167},[],[165,166,39],"灌溉","水资源",[],{"code":71,"score":169,"supported":50,"title":170,"abstract":171,"tags":172},7,[],[29,75],[132],{"code":174,"score":113,"supported":50,"title":175,"abstract":176,"tags":179},"H2",[],[177,178],"土壤","soil",[],{"code":181,"score":19,"supported":50,"title":182,"abstract":183,"tags":185},"A7",[],[184],"间作",[],{"code":187,"score":19,"supported":50,"title":188,"abstract":189,"tags":191},"F3",[],[190],"养分管理",[],[193,194],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":196,"openalex_id":198,"authors":199,"venue":121,"cited_by_count":101,"oa_url":118,"card":210,"direction":109,"ingested_from":110},"W7223121456",[200,203,205,207],{"name":201,"orcid":202},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":204,"orcid":8},"Oluwadare OYEBODE",{"name":206,"orcid":8},"Iretiayo Adelaiye",{"name":208,"orcid":209},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":211,"method":212,"finding":213,"direction":107,"opportunity":214},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":8,"source_name":222,"source_url":219,"published_at":223,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":224,"score_detail":225,"sources":230,"tags":232,"primary_topic":235,"secondary_topics":236,"classification":238,"search_phrases":268,"slug":271,"view_count":101,"doi":272,"paper":273,"created_at":284},4801,"CROPINTEL DATASET: A COMPREHENSIVE AGROENVIRONMENTAL RESOURCE FOR CROP CLASSIFICATION, IRRIGATION PLANNING, AND YIELD ANALYSIS","https:\u002F\u002Fdoi.org\u002F10.30546\u002F09090.2025.210.005","The growing demand for sustainable agricultural intensification necessitates advanced tools for optimizing crop selection and resource management.This study presents an integrated analytical framework using machine learning to derive agro-environmental intelligence from the comprehensive CropIntel dataset.We evaluated a suite of over fifteen machine learning algorithms for three critical precision agriculture tasks: crop classification, seasonal irrigation requirement prediction, and yield potential forecasting.The results demonstrate the exceptional capability of tree-based models in this domain.Notably, Decision Tree, Bagging, and Gaussian Naïve Bayes classifiers achieved near-perfect accuracy (Acc≈99.7%) in identifying suitable crops based on climatic and edaphic conditions.For regression tasks, LightGBM and Histogram Gradient Boosting models proved most effective, explaining approximately 84% of the variance in irrigation needs (R²≈0.84)and about 70% of the variance in yield potential (R²≈0.696).These findings underscore the potential of machine learning to create powerful decision support systems that can guide crop selection, optimize water allocation, and provide reliable yield forecasts.This research contributes to a holistic, data-driven methodology that can enhance the efficiency and sustainability of agricultural practices.","对可持续农业集约化的需求日益增长，亟需先进工具来优化作物选择与资源管理。本研究提出一个集成的分析框架，利用机器学习从综合性的CropIntel数据集中提取农业环境智能信息。我们针对三个关键精准农业任务评估了超过十五种机器学习算法：作物分类、季节性灌溉需求预测和产量潜力预报。结果表明，基于树的模型在该领域具有卓越能力。值得注意的是，决策树、装袋法和高斯朴素贝叶斯分类器在基于气候和土壤条件识别适宜作物方面达到了近乎完美的准确率（Acc≈99.7%）。在回归任务中，LightGBM和直方图梯度提升模型最为有效，分别解释了约84%的灌溉需求方差（R²≈0.84）和约70%的产量潜力方差（R²≈0.696）。这些发现凸显了机器学习在构建强大决策支持系统方面的潜力，可指导作物选择、优化水资源分配并提供可靠的产量预报。本研究为一种整体性、数据驱动的方法论做出了贡献，有助于提升农业实践的效率与可持续性。","JOURNAL OF BAKU ENGINEERING UNIVERSITY- MATHEMATICS AND COMPUTER SCIENCE","2026-10-12T00:00:00Z",72,{"impact":226,"substance":227,"depth":228,"authority":59,"freshness":126,"relevant":20,"comment":229},12,21,17,"构建CropIntel农业环境数据集并系统评测15种以上机器学习算法，在作物分类、灌溉需求与产量预测上给出可复现结论，方法扎实但属细分领域学术进展，产业影响有限。",[231],{"name":222,"url":219},[25,26,233,28,234],"产量预测","灌溉决策",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[237],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":239},{"candidates":240,"agricultural_evidence":50},[241,245,250,254,258,264],{"code":38,"score":16,"supported":50,"title":242,"abstract":243,"tags":244},[39],[165,166,39],[234],{"code":31,"score":12,"supported":50,"title":246,"abstract":247,"tags":249},[],[147,54,248],"decision support",[26],{"code":58,"score":18,"supported":50,"title":251,"abstract":252,"tags":253},[],[28,63],[25,28],{"code":174,"score":19,"supported":50,"title":255,"abstract":256,"tags":257},[],[177],[],{"code":259,"score":19,"supported":50,"title":260,"abstract":261,"tags":263},"H8",[],[262],"可持续农业",[],{"code":78,"score":20,"supported":13,"title":265,"abstract":266,"tags":267},[],[],[233],[269,270],"CropIntel 数据集 作物分类","机器学习 灌溉需求 预测","CropIntel数据集作物分类-4801","10.30546\u002F09090.2025.210.005",{"doi":272,"openalex_id":274,"authors":275,"venue":222,"cited_by_count":101,"oa_url":219,"card":279,"direction":109,"ingested_from":110},"W7221521521",[276],{"name":277,"orcid":278},"Artughrul Gayibov","https:\u002F\u002Forcid.org\u002F0009-0009-7349-0286",{"tldr":280,"method":281,"finding":282,"direction":107,"opportunity":283},"基于CropIntel数据集，用15+种机器学习算法实现作物分类、灌溉需求预测与产量潜力预测。","CropIntel农业环境数据集，对比15+种机器学习算法，含树模型与贝叶斯分类","树模型分类准确率近99.7%，LightGBM等回归模型对灌溉需求R²约0.84、产量约0.70。","可探索多任务联合建模与模型可解释性，并将预测结果嵌入实时灌溉决策系统。","2026-10-13T23:30:08.688669Z",{"id":286,"title":287,"url":288,"summary":289,"summary_zh":290,"content":8,"source_name":291,"source_url":288,"published_at":292,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":293,"score_detail":294,"sources":296,"tags":298,"primary_topic":300,"secondary_topics":301,"classification":307,"search_phrases":369,"slug":372,"view_count":93,"doi":373,"paper":374,"created_at":384},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research","2026-09-14T00:00:00Z",76,{"impact":16,"substance":17,"depth":228,"authority":59,"freshness":18,"relevant":20,"comment":295},"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[297],{"name":291,"url":288},[25,26,233,299,28],"可解释AI",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[302,303],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":259,"slug":304,"name":305,"path":306,"parent_code":42},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":44,"locked":13,"version":45,"method":46,"evidence":308},{"candidates":309,"agricultural_evidence":50},[310,315,319,324,331,337,341,346,351,355,361,365],{"code":31,"score":66,"supported":50,"title":311,"abstract":312,"tags":314},[],[313,147,148,54,52,149],"目标检测",[26,299],{"code":38,"score":126,"supported":50,"title":316,"abstract":317,"tags":318},[],[165,166,39],[],{"code":259,"score":18,"supported":50,"title":320,"abstract":322,"tags":323},[321],"sustainable agriculture",[],[],{"code":325,"score":113,"supported":50,"title":326,"abstract":327,"tags":330},"A8",[],[328,329],"粮食安全","food security",[],{"code":187,"score":113,"supported":50,"title":332,"abstract":333,"tags":336},[],[334,335],"肥料","fertilizer",[],{"code":174,"score":113,"supported":50,"title":338,"abstract":339,"tags":340},[],[177,178],[],{"code":84,"score":113,"supported":50,"title":342,"abstract":343,"tags":345},[],[131,344],"internet of things",[],{"code":78,"score":72,"supported":50,"title":347,"abstract":348,"tags":350},[],[349],"遥感",[233],{"code":181,"score":19,"supported":50,"title":352,"abstract":353,"tags":354},[],[184],[],{"code":356,"score":19,"supported":50,"title":357,"abstract":358,"tags":360},"F4",[],[359],"除草",[],{"code":71,"score":19,"supported":50,"title":362,"abstract":363,"tags":364},[],[29],[],{"code":58,"score":93,"supported":13,"title":366,"abstract":367,"tags":368},[],[],[25,28],[370,371],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能 产量预测","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":373,"openalex_id":375,"authors":376,"venue":291,"cited_by_count":101,"oa_url":288,"card":379,"direction":109,"ingested_from":110},"W7212616413",[377],{"name":378,"orcid":8},"Manish Maan",{"tldr":380,"method":381,"finding":382,"direction":107,"opportunity":383},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","2026-09-15T23:30:13.705421Z",{"id":386,"title":387,"url":388,"summary":389,"summary_zh":390,"content":8,"source_name":391,"source_url":388,"published_at":122,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":392,"score_detail":393,"sources":395,"tags":397,"primary_topic":400,"secondary_topics":401,"classification":403,"search_phrases":423,"slug":426,"view_count":101,"doi":427,"paper":428,"created_at":440},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","Zenodo (CERN European Organization for Nuclear Research)",69,{"impact":18,"substance":125,"depth":16,"authority":226,"freshness":126,"relevant":20,"comment":394},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[396],{"name":391,"url":388},[25,26,234,398,399],"安全保证","试验设计",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[402],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":404},{"candidates":405,"agricultural_evidence":50},[406,411,415,419],{"code":38,"score":407,"supported":50,"title":408,"abstract":409,"tags":410},15,[39],[165,39],[234],{"code":31,"score":226,"supported":50,"title":412,"abstract":413,"tags":414},[52],[248],[26],{"code":174,"score":113,"supported":50,"title":416,"abstract":417,"tags":418},[],[177,178],[],{"code":58,"score":20,"supported":13,"title":420,"abstract":421,"tags":422},[],[],[25],[424,425],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":427,"openalex_id":429,"authors":430,"venue":391,"cited_by_count":101,"oa_url":388,"card":435,"direction":107,"ingested_from":110},"W7223131205",[431,433],{"name":432,"orcid":8},"Nassima Benkhelifa",{"name":434,"orcid":8},"Yacine Aït Ahmed",{"tldr":436,"method":437,"finding":438,"direction":107,"opportunity":439},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":8,"source_name":447,"source_url":444,"published_at":122,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":448,"score_detail":449,"sources":451,"tags":453,"primary_topic":456,"secondary_topics":457,"classification":460,"search_phrases":512,"slug":515,"view_count":101,"doi":516,"paper":517,"created_at":541},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",79,{"impact":16,"substance":125,"depth":16,"authority":226,"freshness":126,"relevant":20,"comment":450},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[452],{"name":447,"url":444},[25,26,349,454,455],"作物水分胁迫","精准灌溉",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[458,459],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":84,"slug":136,"name":137,"path":138,"parent_code":35},{"status":44,"locked":13,"version":45,"method":46,"evidence":461},{"candidates":462,"agricultural_evidence":50},[463,467,471,475,481,485,492,499,506],{"code":38,"score":407,"supported":50,"title":464,"abstract":465,"tags":466},[39],[165,39],[455],{"code":31,"score":12,"supported":50,"title":468,"abstract":469,"tags":470},[],[147,54,52],[26],{"code":84,"score":126,"supported":50,"title":472,"abstract":473,"tags":474},[],[131,344,87],[],{"code":58,"score":169,"supported":50,"title":476,"abstract":477,"tags":480},[],[478,479],"数字孪生","digital twin",[25],{"code":78,"score":169,"supported":50,"title":482,"abstract":483,"tags":484},[],[349,81],[349],{"code":486,"score":113,"supported":50,"title":487,"abstract":488,"tags":491},"A2",[],[489,490],"小麦","wheat",[],{"code":493,"score":113,"supported":50,"title":494,"abstract":495,"tags":498},"A3",[],[496,497],"玉米","maize",[],{"code":500,"score":113,"supported":50,"title":501,"abstract":502,"tags":505},"A5",[],[503,504],"马铃薯","potato",[],{"code":507,"score":19,"supported":50,"title":508,"abstract":509,"tags":511},"B2",[],[510],"葡萄",[],[513,514],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":516,"openalex_id":518,"authors":519,"venue":447,"cited_by_count":101,"oa_url":444,"card":535,"direction":109,"ingested_from":110},"W7223088921",[520,523,526,529,532],{"name":521,"orcid":522},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":524,"orcid":525},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":527,"orcid":528},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":530,"orcid":531},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":533,"orcid":534},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":536,"method":537,"finding":538,"direction":539,"opportunity":540},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":543,"title":544,"url":545,"summary":546,"summary_zh":547,"content":8,"source_name":548,"source_url":545,"published_at":549,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":392,"score_detail":550,"sources":552,"tags":554,"primary_topic":556,"secondary_topics":557,"classification":559,"search_phrases":591,"slug":594,"view_count":101,"doi":595,"paper":596,"created_at":611},5496,"Multi-Crop Irrigation Need Classification Based on Machine Learning with an Indonesian-Language GenAI-UX Recommendation Layer","https:\u002F\u002Fdoi.org\u002F10.30871\u002Fjaic.v10i5.13774","Inefficient irrigation water use remains a critical challenge in Indonesian agriculture, and the complexity of raw sensor data hinders adoption by smallholder farmers who lack data-analysis skills. This study develops an intelligent irrigation Decision Support System that integrates interpretable Machine Learning with an Indonesian-language GenAI-UX recommendation layer, using a publicly available multi-crop irrigation scheduling dataset (501 records, nine crops) with an analytical focus on paddy and maize. Four components are combined into one pipeline: (i) irrigation-need classification compared across six algorithms; (ii) regression of a continuous water-requirement score; (iii) sensor-anomaly detection using Isolation Forest cross-validated with the Interquartile Range method; and (iv) a rule-based natural-language recommendation layer whose operating point is optimized by a Genetic Algorithm. Validity was safeguarded through stratified five-fold cross-validation and leakage-controlled preprocessing. Gradient Boosting achieved the best classification performance (F1-score 0.980; cross-validated F1 0.963), Random Forest Regressor reached R square of 0.807, Isolation Forest agreed with IQR outliers at 95.2%, and the Genetic Algorithm attained a confidence of 0.993 within realistic agronomic bounds. In an illustrative usability evaluation, the GenAI-UX interface obtained the highest System Usability Scale score (Good category). The results indicate that pairing accurate, interpretable models with a language-based delivery layer can transform complex sensor data into actionable irrigation recommendations, lowering the adoption barrier for non-technical users. Field validation with real respondents and edge deployment are identified as the next steps.","灌溉用水效率低下仍是印度尼西亚农业面临的一项关键挑战，而原始传感器数据的复杂性阻碍了缺乏数据分析技能的小农户采用相关技术。本研究开发了一种智能灌溉决策支持系统（Decision Support System），将可解释的机器学习与印度尼西亚语生成式人工智能用户体验（GenAI-UX）推荐层相结合，使用公开可用的多作物灌溉调度数据集（501条记录，九种作物），并以水稻和玉米为分析重点。四个组件被整合为一条流水线：（i）在六种算法之间比较灌溉需求分类；（ii）对连续需水量得分进行回归；（iii）使用孤立森林（Isolation Forest）进行传感器异常检测，并与四分位距（IQR）方法交叉验证；（iv）基于规则的自然语言推荐层，其操作点由遗传算法优化。通过分层五折交叉验证和防泄漏预处理保障了有效性。梯度提升（Gradient Boosting）取得了最佳分类性能（F1分数0.980；交叉验证F1为0.963），随机森林回归器（Random Forest Regressor）达到R平方0.807，孤立森林与IQR异常值的一致率为95.2%，遗传算法在现实农艺范围内达到0.993的置信度。在一项示例性可用性评估中，GenAI-UX界面获得了最高的系统可用性量表（System Usability Scale）得分（良好类别）。结果表明，将准确、可解释的模型与基于语言的交付层相结合，能够将复杂的传感器数据转化为可操作的灌溉建议，从而降低非技术用户的采用门槛。与真实受访者进行田间验证以及边缘部署被确定为下一步工作。","Journal of Applied Informatics and Computing","2026-10-19T00:00:00Z",{"impact":226,"substance":17,"depth":228,"authority":226,"freshness":18,"relevant":20,"comment":551},"方法组合完整、指标扎实的农业AI决策支持论文，但属印尼小农户场景的实验室级研究，公共影响有限，适合作为技术参考而非每日精选头条。",[553],{"name":548,"url":545},[25,26,555,496,234],"水稻",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[558],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":560},{"candidates":561,"agricultural_evidence":50},[562,566,570,576,580,587],{"code":31,"score":16,"supported":50,"title":563,"abstract":564,"tags":565},[54],[147,54,248],[26],{"code":38,"score":407,"supported":50,"title":567,"abstract":568,"tags":569},[39],[165,39],[234],{"code":571,"score":169,"supported":50,"title":572,"abstract":573,"tags":575},"A1",[],[555,574],"paddy",[555],{"code":493,"score":169,"supported":50,"title":577,"abstract":578,"tags":579},[],[496,497],[496],{"code":581,"score":113,"supported":50,"title":582,"abstract":583,"tags":586},"G2",[],[584,585],"小农户","smallholder",[],{"code":58,"score":20,"supported":13,"title":588,"abstract":589,"tags":590},[],[],[25],[592,593],"印尼 多作物 灌溉决策","GenAI 灌溉推荐","印尼多作物灌溉决策-5496","10.30871\u002Fjaic.v10i5.13774",{"doi":595,"openalex_id":597,"authors":598,"venue":548,"cited_by_count":101,"oa_url":545,"card":606,"direction":107,"ingested_from":110},"W7223016583",[599,602,604],{"name":600,"orcid":601},"Muhammad Rusdi","https:\u002F\u002Forcid.org\u002F0000-0002-1048-5000",{"name":603,"orcid":8},"Zaenuddin Zaenuddin",{"name":605,"orcid":8},"Hendra Sanjaya",{"tldr":607,"method":608,"finding":609,"direction":109,"opportunity":610},"构建融合可解释机器学习与印尼语GenAI推荐层的智能灌溉决策支持系统。","六种分类算法、回归、孤立森林异常检测与遗传算法优化规则推荐层。","梯度提升分类F1达0.980，GenAI界面可用性最佳，降低非技术用户使用门槛。","可探索多语言本地化GenAI推荐层与边缘部署，并开展真实农户田间验证。","2026-10-21T23:30:46.441864Z"]