[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2610":3,"related-2610":94},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":79,"slug":82,"view_count":21,"doi":8,"paper":83,"created_at":93},2610,"整合人工智能、物联网与遥感技术的大田作物智能灌溉管理 综述","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260913082658847.htm","发表于Biosystems Engineering。对人工智能(AI)、物联网(IoT)和遥感(RS)技术在灌溉管理中的应用进行全面且结构化分析，特别是在优化基于天气、土壤和作物的灌溉调度方面。智能灌溉系统实现了水资源节约(用水量减少高达20-60%)、降低能源消耗和提高作物生产力。未来研究应优先考虑成本效益高的传感器开发和用户友好的AI界面。",null,"Biosystems Engineering","2026-09-13T01:00:00Z","论文",10,false,83,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,21,18,14,8,1,"核心期刊综述，系统梳理AI、物联网与遥感在大田灌溉调度中的融合应用，给出节水20-60%等量化结论，对智慧农业技术路线有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业物联网","智能灌溉","遥感监测",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":78},[38,47,54,61,68,73],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":46},"H3",15,true,[43],"灌溉",[43,45],"水资源",[29],{"code":48,"score":40,"supported":41,"title":49,"abstract":51,"tags":53},"I5",[50],"物联网",[50,52],"IoT",[28],{"code":55,"score":56,"supported":41,"title":57,"abstract":59,"tags":60},"I3",12,[58],"遥感",[58],[30],{"code":62,"score":63,"supported":41,"title":64,"abstract":65,"tags":67},"H2",3,[],[66],"土壤",[],{"code":69,"score":21,"supported":13,"title":70,"abstract":71,"tags":72},"I1",[],[],[26],{"code":74,"score":21,"supported":13,"title":75,"abstract":76,"tags":77},"I2",[],[],[27],"多个产业证据相当，需要确认主归属",[80,81],"农业人工智能 农业物联网 智慧农业 智能灌溉","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业智能灌溉-2610",{"doi":8,"openalex_id":8,"authors":84,"venue":8,"cited_by_count":85,"oa_url":8,"card":86,"direction":90,"ingested_from":92},[],0,{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"综述AI、物联网与遥感在大田作物智能灌溉调度中的应用与成效。","结构化综述AI、IoT、RS在基于天气、土壤和作物的灌溉调度中的应用。","智能灌溉可节水20-60%，降低能耗并提高作物生产力。","智慧农业 \u002F 农业物联网","低成本传感器与用户友好AI界面是落地瓶颈，可研究轻量化模型与低成本感知方案。","agent","2026-09-16T00:03:52.381950Z",{"total":95,"page":21,"page_size":95,"items":96},6,[97,167,241,313,395,444],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":8,"content":8,"source_name":9,"source_url":100,"published_at":102,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":103,"score_detail":104,"sources":109,"tags":111,"primary_topic":8,"secondary_topics":112,"classification":113,"search_phrases":141,"slug":142,"view_count":143,"doi":144,"paper":145,"created_at":166},2024,"Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104589","Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review。Biosystems Engineering","2026-09-09T00:00:00Z",78,{"impact":18,"substance":105,"depth":106,"authority":19,"freshness":107,"relevant":21,"comment":108},20,17,9,"核心期刊综述，系统梳理AI、物联网与遥感融合的田间作物智能灌溉研究进展，对智慧农业技术路线有参考价值，但属综述类成果、非突破性原创，适合进入每日精选。",[110],{"name":9,"url":100},[26,27,28,29,30],[],{"status":33,"locked":13,"version":34,"method":35,"evidence":114},{"candidates":115,"agricultural_evidence":13,"reason":140},[116,121,126,131,136],{"code":39,"score":56,"supported":41,"title":117,"abstract":119,"tags":120},[118],"irrigation",[118],[29],{"code":74,"score":56,"supported":41,"title":122,"abstract":124,"tags":125},[123],"artificial intelligence",[123],[27],{"code":55,"score":56,"supported":41,"title":127,"abstract":129,"tags":130},[128],"remote sensing",[128],[30],{"code":48,"score":56,"supported":41,"title":132,"abstract":134,"tags":135},[133],"internet of things",[133],[28],{"code":69,"score":21,"supported":13,"title":137,"abstract":138,"tags":139},[],[],[26],"农业相关性需要复核",[80,81],"农业人工智能农业物联网智慧农业智能灌溉-2024",2,"10.1016\u002Fj.biosystemseng.2026.104589",{"doi":144,"openalex_id":146,"authors":147,"venue":9,"cited_by_count":85,"oa_url":8,"card":8,"direction":8,"ingested_from":165},"W7212048658",[148,151,153,155,157,159,162],{"name":149,"orcid":150},"Ehab H. Hegazi","https:\u002F\u002Forcid.org\u002F0000-0002-1468-1420",{"name":152,"orcid":8},"Jian Liu",{"name":154,"orcid":8},"Ruixia Ai",{"name":156,"orcid":8},"Lin Liu",{"name":158,"orcid":8},"Xuemei Liu",{"name":160,"orcid":161},"Jin Yuan","https:\u002F\u002Forcid.org\u002F0000-0002-5803-6626",{"name":163,"orcid":164},"G. Papadakis","https:\u002F\u002Forcid.org\u002F0000-0002-1805-5056","openalex","2026-09-10T23:30:05.849251Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":8,"source_name":173,"source_url":170,"published_at":174,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":175,"score_detail":176,"sources":180,"tags":182,"primary_topic":184,"secondary_topics":189,"classification":194,"search_phrases":223,"slug":226,"view_count":63,"doi":227,"paper":228,"created_at":240},4809,"Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02299-3","Abstract Precision agriculture is one of the major breakthroughs which has been adopted to counteract some of the major challenges facing contemporary agriculture, such as maximizing crop yield, maximizing resource efficiency, and sustainability. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies can go a long way in enhancing crop yield prediction through data obtained from multiple sources, including satellite images, ground sensors, and weather stations. This research paper proposes a next-generation crop yield prediction system called Geo-Optimized Deep Learning (GODL) algorithm. The GODL algorithm integrates geospatial information with deep learning algorithms for crop yield analysis and prediction. This combination of deep learning and geospatial data allows accurate and scalable crop yield predictions using geospatial and sensor information. In order to ensure model optimization in terms of accuracy and efficiency, the GODL Algorithm makes use of advanced hyperparameter optimization methods that include Adam and Stochastic Gradient Descent (SGD). The research paper highlights the efficiency of the GODL Algorithm by conducting extensive experiments that compare the algorithm’s performance with other crop yield prediction models like Recurrent Neural Network (RNN) and other machine learning techniques. The evaluation takes into consideration important metrics such as accuracy, Jaccard Index, Fowlkes-Mallows Index (FMI), and G-mean, as well as IoT data delay and its effects on the prediction accuracy. In future research, the use of blockchain technology to facilitate data sharing and edge computing to process the data will be considered.","摘要 精准农业是为应对当代农业面临的若干重大挑战而采用的主要突破之一，这些挑战包括最大化作物产量、最大化资源效率以及可持续性。人工智能（AI）与物联网（IoT）技术的应用，能够通过从卫星图像、地面传感器和气象站等多源获取的数据，大幅提升作物产量预测能力。本文提出了一种下一代作物产量预测系统，称为地理优化深度学习（GODL）算法。GODL算法将地理空间信息与深度学习算法相结合，用于作物产量分析与预测。深度学习与地理空间数据的这种结合，使得利用地理空间和传感器信息进行准确且可扩展的作物产量预测成为可能。为确保模型在准确性和效率方面实现优化，GODL算法采用了先进的超参数优化方法，包括Adam和随机梯度下降（SGD）。本文通过开展大量实验，将该算法的性能与其他作物产量预测模型（如循环神经网络（RNN）及其他机器学习技术）进行比较，从而突出GODL算法的效率。评估考虑了准确率、Jaccard指数、Fowlkes-Mallows指数（FMI）和G均值等重要指标，以及物联网数据延迟及其对预测精度的影响。在未来的研究中，将考虑使用区块链技术促进数据共享，并利用边缘计算处理数据。","Peer-to-Peer Networking and Applications","2026-10-12T00:00:00Z",68,{"impact":56,"substance":18,"depth":177,"authority":178,"freshness":107,"relevant":21,"comment":179},16,13,"提出GODL地理优化深度学习算法融合AI与IoT进行作物估产，方法有创新且对比实验充分，但属学术论文，产业落地影响有限。",[181],{"name":173,"url":170},[26,27,28,30,183],"作物估产",{"code":48,"slug":185,"name":186,"path":187,"parent_code":188},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[190],{"code":74,"slug":191,"name":192,"path":193,"parent_code":188},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":195,"locked":13,"version":34,"method":35,"evidence":196},"classified",{"candidates":197,"agricultural_evidence":41},[198,204,211,219],{"code":48,"score":18,"supported":41,"title":199,"abstract":200,"tags":203},[52],[50,201,133,52,202],"边缘计算","edge computing",[28],{"code":69,"score":40,"supported":41,"title":205,"abstract":207,"tags":210},[206],"smart agriculture",[208,209],"精准农业","precision agriculture",[26],{"code":74,"score":12,"supported":41,"title":212,"abstract":213,"tags":218},[],[214,215,216,217,123],"深度学习","机器学习","machine learning","deep learning",[27],{"code":55,"score":143,"supported":13,"title":220,"abstract":221,"tags":222},[],[],[183,30],[224,225],"GODL 作物估产 算法","AI IoT 智慧农业 产量预测","GODL作物估产算法-4809","10.1007\u002Fs12083-026-02299-3",{"doi":227,"openalex_id":229,"authors":230,"venue":173,"cited_by_count":85,"oa_url":170,"card":235,"direction":90,"ingested_from":165},"W7221560555",[231,233],{"name":232,"orcid":8},"Keerthana T",{"name":234,"orcid":8},"Janarthanan R",{"tldr":236,"method":237,"finding":238,"direction":90,"opportunity":239},"提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。","GODL算法，融合地理空间数据与深度学习，采用Adam和SGD超参数优化。","GODL在准确率、Jaccard指数、FMI和G-mean上优于RNN等模型，且IoT数据延迟影响预","可探索区块链保障数据共享安全、边缘计算降低IoT延迟，以及多源异构数据融合的轻量化模型。","2026-10-13T23:30:10.304344Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":8,"source_name":247,"source_url":244,"published_at":248,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":249,"score_detail":250,"sources":252,"tags":254,"primary_topic":256,"secondary_topics":260,"classification":262,"search_phrases":290,"slug":293,"view_count":276,"doi":294,"paper":295,"created_at":312},4567,"An embedded deep learning–fuzzy logic framework for intelligent and automated irrigation in smart farming","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72706-5","Water scarcity and the need to improve resource efficiency have made intelligent and automated irrigation management an important research challenge in smart farming. This study proposes IrrigaDL-FL, a low-cost automated smart irrigation framework based on the internet of things, integrating intelligent data routing with adaptive irrigation decision-making. The proposed framework consists of two complementary components. First, a Deep Learning–Fuzzy Logic (DL-FL) framework is integrated with the Open Shortest Path First (OSPF) protocol to enhance data routing efficiency in the network. The DL-FL framework combines these heterogeneous characteristics to identify suitable next-hop nodes and establish energy-efficient routing paths. Second, irrigation decision-making is performed using a lightweight Convolutional Neural Network–Fuzzy Logic Controller (CNN-FLC). The CNN-FLC processes sensor-derived and crop-related information, including air temperature, relative humidity, soil moisture, evapotranspiration rate, crop growth coefficient, crop water requirement, planting date, cumulative rainfall, recent rainfall, irrigation history, crop growth stage, and the difference between actual and target soil moisture. These inputs are used to characterize crop hydration stress and dynamically determine the appropriate irrigation program and sprinkler activation duration. The proposed IrrigaDL-FL framework is evaluated through simulations with different numbers of sensor nodes over predefined routing rounds and is compared with several existing approaches. The results demonstrate that IrrigaDL-FL achieves superior performance in terms of network lifetime, residual energy, energy variance, number of transmitted packets, and average path length.","水资源短缺与提高资源效率的需求，使智能化和自动化灌溉管理成为智慧农业中的重要研究挑战。本研究提出IrrigaDL-FL，一种基于物联网的低成本自动化智能灌溉框架，将智能数据路由与自适应灌溉决策相结合。该框架由两个互补部分组成。首先，将深度学习-模糊逻辑（DL-FL）框架与开放最短路径优先（OSPF）协议集成，以提高网络中的数据路由效率。DL-FL框架结合这些异构特征来识别合适的下一跳节点并建立节能路由路径。其次，灌溉决策通过轻量级卷积神经网络-模糊逻辑控制器（CNN-FLC）执行。CNN-FLC处理来自传感器的信息和作物相关信息，包括空气温度、相对湿度、土壤湿度、蒸散速率、作物生长系数、作物需水量、种植日期、累计降雨量、近期降雨量、灌溉历史、作物生长阶段以及实际土壤湿度与目标土壤湿度之间的差值。这些输入用于表征作物水分胁迫，并动态确定合适的灌溉方案和喷灌器启动时长。所提出的IrrigaDL-FL框架通过在预定义路由轮次下对不同数量传感器节点进行仿真评估，并与若干现有方法进行比较。结果表明，IrrigaDL-FL在网络寿命、剩余能量、能量方差、传输数据包数量和平均路径长度方面均取得了更优性能。","Scientific Reports","2026-10-08T00:00:00Z",77,{"impact":177,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":251},"提出低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由与灌溉决策，方法新颖、仿真验证充分，对智慧农业节水研究有参考价值。",[253],{"name":247,"url":244},[26,27,28,29,255],"节水农业",{"code":39,"slug":118,"name":257,"path":258,"parent_code":259},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[261],{"code":74,"slug":191,"name":192,"path":193,"parent_code":188},{"status":195,"locked":13,"version":34,"method":35,"evidence":263},{"candidates":264,"agricultural_evidence":41},[265,271,275,280,285],{"code":39,"score":266,"supported":41,"title":267,"abstract":268,"tags":270},19,[118],[43,45,118,269],"evapotranspiration",[29,255],{"code":74,"score":40,"supported":41,"title":272,"abstract":273,"tags":274},[217],[214,217],[27],{"code":48,"score":276,"supported":41,"title":277,"abstract":278,"tags":279},7,[],[50,133],[28],{"code":62,"score":95,"supported":41,"title":281,"abstract":282,"tags":284},[],[66,283],"soil",[],{"code":69,"score":286,"supported":41,"title":287,"abstract":288,"tags":289},4,[],[26],[26],[291,292],"IrrigaDL-FL 智能灌溉","CNN-FLC 灌溉决策","IrrigaDL-FL智能灌溉-4567","10.1038\u002Fs41598-026-72706-5",{"doi":294,"openalex_id":296,"authors":297,"venue":247,"cited_by_count":85,"oa_url":306,"card":307,"direction":90,"ingested_from":165},"W7221040139",[298,300,302,304],{"name":299,"orcid":8},"Ze Luo",{"name":301,"orcid":8},"Zhuo Yang",{"name":303,"orcid":8},"Yi Hu",{"name":305,"orcid":8},"Jun Wang","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72706-5_reference.pdf",{"tldr":308,"method":309,"finding":310,"direction":90,"opportunity":311},"提出IrrigaDL-FL低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由和灌溉决策。","用CNN-模糊逻辑控制器处理多源传感器与作物数据，结合OSPF协议做节能路由。","在仿真中网络寿命、剩余能量、能耗方差、传输包数和平均路径长度均优于现有方法。","可探索真实农田部署下多作物、多气候场景的泛化能力与边缘计算资源约束优化。","2026-10-10T23:30:11.306685Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":8,"source_name":319,"source_url":316,"published_at":320,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":321,"score_detail":322,"sources":325,"tags":327,"primary_topic":328,"secondary_topics":332,"classification":333,"search_phrases":371,"slug":374,"view_count":143,"doi":375,"paper":376,"created_at":394},4297,"IoT -Based Smart Agriculture Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.47392\u002Firjaeh.2026.0711","Agriculture plays a crucial role in economic development and food security. Traditional farming methods often face challenges such as inefficient water usage, unpredictable environmental conditions, and limited real-time monitoring. This paper proposes an IoT-Based Smart Agriculture system integrated with Machine Learning to enhance farm productivity and resource management. The system employs IoT sensors to collect data related to soil moisture, temperature, humidity, and light intensity. These data are transmitted to a cloud platform where machine learning algorithms analyze environmental conditions, predict crop requirements, and generate intelligent recommendations for irrigation and crop management. The proposed solution enables continuous monitoring, reduces water wastage, improves crop health assessment, and supports data-driven decision-making. By combining IoT and Machine Learning, the system offers a cost-effective and scalable approach to precision farming, helping farmers increase yield, reduce operational costs, and promote sustainable agricultural practices.","农业在经济发展和粮食安全中发挥着至关重要的作用。传统耕作方式往往面临水资源利用效率低、环境条件不可预测以及实时监测能力有限等挑战。本文提出了一种基于物联网（IoT）并与机器学习相结合的智慧农业系统，以提升农业生产率和资源管理水平。该系统采用物联网传感器采集与土壤湿度、温度、湿度和光照强度相关的数据。这些数据被传输至云平台，由机器学习算法分析环境条件、预测作物需求，并生成灌溉和作物管理的智能建议。所提出的解决方案能够实现持续监测、减少水资源浪费、改进作物健康评估，并支持数据驱动的决策。通过将物联网与机器学习相结合，该系统为精准农业提供了一种具有成本效益且可扩展的方法，帮助农民提高产量、降低运营成本并促进可持续农业实践。","International Research Journal on Advanced Engineering Hub (IRJAEH)","2026-10-05T00:00:00Z",46,{"impact":20,"substance":56,"depth":323,"authority":95,"freshness":107,"relevant":21,"comment":324},11,"论文提出IoT与机器学习结合的智慧农业系统，思路常规、缺乏实测数据与规模验证，学术权威性一般，仅具参考价值。",[326],{"name":319,"url":316},[26,27,28,29,208],{"code":188,"slug":329,"name":330,"path":331,"parent_code":8},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":334,"locked":13,"version":34,"method":35,"evidence":335},"partial",{"candidates":336,"agricultural_evidence":41,"reason":370},[337,341,345,349,353,360,364],{"code":69,"score":266,"supported":41,"title":338,"abstract":339,"tags":340},[206],[26,208,206],[26,208],{"code":74,"score":40,"supported":41,"title":342,"abstract":343,"tags":344},[216],[215,216],[27],{"code":48,"score":40,"supported":41,"title":346,"abstract":347,"tags":348},[52],[50,52],[28],{"code":39,"score":12,"supported":41,"title":350,"abstract":351,"tags":352},[],[43,45,118],[29],{"code":354,"score":95,"supported":41,"title":355,"abstract":356,"tags":359},"A8",[],[357,358],"粮食安全","food security",[],{"code":62,"score":95,"supported":41,"title":361,"abstract":362,"tags":363},[],[66,283],[],{"code":365,"score":63,"supported":41,"title":366,"abstract":367,"tags":369},"H8",[],[368],"可持续农业",[],"一级领域明确，二级方向需要细分",[372,373],"IoT 智慧农业 机器学习","土壤墒情 智能灌溉 云平台","IoT智慧农业机器学习-4297","10.47392\u002Firjaeh.2026.0711",{"doi":375,"openalex_id":377,"authors":378,"venue":319,"cited_by_count":85,"oa_url":316,"card":389,"direction":90,"ingested_from":165},"W7220440757",[379,381,383,385,387],{"name":380,"orcid":8},"Priyanka Behera",{"name":382,"orcid":8},"Satyabrata Satapathy",{"name":384,"orcid":8},"Nakshytrajeet Kar",{"name":386,"orcid":8},"Sucharita NathSharma",{"name":388,"orcid":8},"Priyanka Shit",{"tldr":390,"method":391,"finding":392,"direction":90,"opportunity":393},"提出物联网结合机器学习的智慧农业系统，实现环境监测与智能灌溉决策。","部署土壤湿度、温湿度、光照等IoT传感器，云端机器学习分析预测作物需求。","系统可连续监测、减少水资源浪费、改善作物健康评估并支持数据驱动决策。","可探索边缘计算与轻量模型在田间实时决策中的应用，降低云端依赖与延迟。","2026-10-06T23:30:08.015155Z",{"id":396,"title":397,"url":398,"summary":399,"summary_zh":8,"content":8,"source_name":400,"source_url":398,"published_at":401,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":402,"score_detail":403,"sources":405,"tags":407,"primary_topic":8,"secondary_topics":409,"classification":410,"search_phrases":434,"slug":436,"view_count":143,"doi":437,"paper":438,"created_at":443},3777,"An Intelligent IoT-Enabled Machine Learning Framework for Smart Irrigation in Climate-Resilient Agriculture","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10600972\u002Fv1","An Intelligent IoT-Enabled Machine Learning Framework for Smart Irrigation in Climate-Resilient Agriculture。Research Square","Research Square","2026-09-28T00:00:00Z",59,{"impact":56,"substance":19,"depth":40,"authority":12,"freshness":20,"relevant":21,"comment":404},"预印本提出IoT+机器学习智能灌溉框架，契合智慧农业与气候韧性方向，但尚属方法探索、缺乏落地数据，可作为主题聚合素材而非每日精选。",[406],{"name":400,"url":398},[26,27,28,29,408],"气候韧性",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":411},{"candidates":412,"agricultural_evidence":41,"reason":78},[413,417,421,425,430],{"code":39,"score":56,"supported":41,"title":414,"abstract":415,"tags":416},[118],[118],[29],{"code":74,"score":56,"supported":41,"title":418,"abstract":419,"tags":420},[216],[216],[27],{"code":48,"score":56,"supported":41,"title":422,"abstract":423,"tags":424},[52],[52],[28],{"code":426,"score":21,"supported":13,"title":427,"abstract":428,"tags":429},"H7",[],[],[408],{"code":69,"score":21,"supported":13,"title":431,"abstract":432,"tags":433},[],[],[26],[435,80],"Research Square 智能灌溉 机器学习","ResearchSquare智能灌溉机器学习-3777","10.21203\u002Frs.3.rs-10600972\u002Fv1",{"doi":437,"openalex_id":439,"authors":440,"venue":400,"cited_by_count":85,"oa_url":398,"card":8,"direction":90,"ingested_from":165},"W7214682755",[441],{"name":442,"orcid":8},"VISHNU N M","2026-09-29T23:30:10.752441Z",{"id":445,"title":446,"url":447,"summary":448,"summary_zh":449,"content":8,"source_name":450,"source_url":447,"published_at":451,"category":11,"cover_url":8,"hotness":452,"is_selected":13,"score":453,"score_detail":454,"sources":456,"tags":460,"primary_topic":462,"secondary_topics":463,"classification":464,"search_phrases":512,"slug":515,"view_count":516,"doi":517,"paper":518,"created_at":528},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":16,"substance":18,"depth":177,"authority":178,"freshness":85,"relevant":21,"comment":455},"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。",[457,458],{"name":450,"url":447},{"name":450,"url":459},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[461,26,27,28,408,30],"数字乡村",{"code":188,"slug":329,"name":330,"path":331,"parent_code":8},[],{"status":334,"locked":13,"version":34,"method":35,"evidence":465},{"candidates":466,"agricultural_evidence":41,"reason":370},[467,472,476,480,484,488,492,496,501,507],{"code":74,"score":18,"supported":41,"title":468,"abstract":469,"tags":471},[123],[215,470,216,123],"病虫害识别",[27],{"code":48,"score":18,"supported":41,"title":473,"abstract":474,"tags":475},[52],[50,133,52],[28],{"code":69,"score":40,"supported":41,"title":477,"abstract":478,"tags":479},[209],[208,209],[26],{"code":55,"score":40,"supported":41,"title":481,"abstract":482,"tags":483},[128],[58,128],[30],{"code":39,"score":107,"supported":41,"title":485,"abstract":486,"tags":487},[],[43,45,118],[],{"code":354,"score":95,"supported":41,"title":489,"abstract":490,"tags":491},[],[357,358],[],{"code":62,"score":95,"supported":41,"title":493,"abstract":494,"tags":495},[],[66,283],[],{"code":426,"score":286,"supported":41,"title":497,"abstract":498,"tags":500},[],[499],"气候变化",[408],{"code":502,"score":63,"supported":41,"title":503,"abstract":504,"tags":506},"H6",[],[505],"农业生态",[],{"code":508,"score":21,"supported":13,"title":509,"abstract":510,"tags":511},"I6",[],[],[461],[513,514],"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",5,"10.5281\u002Fzenodo.22914538",{"doi":517,"openalex_id":519,"authors":520,"venue":450,"cited_by_count":85,"oa_url":447,"card":523,"direction":90,"ingested_from":165},"W7214083098",[521],{"name":522,"orcid":8},"Twinkal Prakash Sawant",{"tldr":524,"method":525,"finding":526,"direction":90,"opportunity":527},"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"]