[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3777":3,"related-3777":82},{"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":8,"secondary_topics":30,"classification":31,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":81},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",null,"Research Square","2026-09-28T00:00:00Z","论文",10,false,59,{"impact":16,"substance":17,"depth":18,"authority":12,"freshness":19,"relevant":20,"comment":21},12,14,15,8,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","农业物联网","智能灌溉","气候韧性",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":39,"reason":66},[37,44,50,56,61],{"code":38,"score":16,"supported":39,"title":40,"abstract":42,"tags":43},"H3",true,[41],"irrigation",[41],[28],{"code":45,"score":16,"supported":39,"title":46,"abstract":48,"tags":49},"I2",[47],"machine learning",[47],[26],{"code":51,"score":16,"supported":39,"title":52,"abstract":54,"tags":55},"I5",[53],"IoT",[53],[27],{"code":57,"score":20,"supported":13,"title":58,"abstract":59,"tags":60},"H7",[],[],[29],{"code":62,"score":20,"supported":13,"title":63,"abstract":64,"tags":65},"I1",[],[],[25],"多个产业证据相当，需要确认主归属",[68,69],"Research Square 智能灌溉 机器学习","农业人工智能 农业物联网 智慧农业 智能灌溉","ResearchSquare智能灌溉机器学习-3777",2,"10.21203\u002Frs.3.rs-10600972\u002Fv1",{"doi":72,"openalex_id":74,"authors":75,"venue":9,"cited_by_count":78,"oa_url":6,"card":8,"direction":79,"ingested_from":80},"W7214682755",[76],{"name":77,"orcid":8},"VISHNU N M",0,"智慧农业 \u002F 农业物联网","openalex","2026-09-29T23:30:10.752441Z",{"total":83,"page":20,"page_size":83,"items":84},6,[85,173,260,353,406,469],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":8,"source_name":91,"source_url":88,"published_at":92,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":93,"score_detail":94,"sources":99,"tags":101,"primary_topic":103,"secondary_topics":107,"classification":113,"search_phrases":150,"slug":153,"view_count":83,"doi":154,"paper":155,"created_at":172},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":95,"substance":96,"depth":97,"authority":17,"freshness":19,"relevant":20,"comment":98},16,21,18,"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。",[100],{"name":91,"url":88},[25,26,27,28,102],"节水农业",{"code":38,"slug":41,"name":104,"path":105,"parent_code":106},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[108],{"code":45,"slug":109,"name":110,"path":111,"parent_code":112},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":114,"locked":13,"version":33,"method":34,"evidence":115},"classified",{"candidates":116,"agricultural_evidence":39},[117,125,131,138,145],{"code":38,"score":118,"supported":39,"title":119,"abstract":120,"tags":124},19,[41],[121,122,41,123],"灌溉","水资源","evapotranspiration",[28,102],{"code":45,"score":18,"supported":39,"title":126,"abstract":128,"tags":130},[127],"deep learning",[129,127],"深度学习",[26],{"code":51,"score":132,"supported":39,"title":133,"abstract":134,"tags":137},7,[],[135,136],"物联网","internet of things",[27],{"code":139,"score":83,"supported":39,"title":140,"abstract":141,"tags":144},"H2",[],[142,143],"土壤","soil",[],{"code":62,"score":146,"supported":39,"title":147,"abstract":148,"tags":149},4,[],[25],[25],[151,152],"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":154,"openalex_id":156,"authors":157,"venue":91,"cited_by_count":78,"oa_url":166,"card":167,"direction":79,"ingested_from":80},"W7221040139",[158,160,162,164],{"name":159,"orcid":8},"Ze Luo",{"name":161,"orcid":8},"Zhuo Yang",{"name":163,"orcid":8},"Yi Hu",{"name":165,"orcid":8},"Jun Wang","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72706-5_reference.pdf",{"tldr":168,"method":169,"finding":170,"direction":79,"opportunity":171},"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":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":8,"source_name":179,"source_url":176,"published_at":180,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":181,"score_detail":182,"sources":186,"tags":188,"primary_topic":190,"secondary_topics":194,"classification":195,"search_phrases":236,"slug":239,"view_count":71,"doi":240,"paper":241,"created_at":259},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":19,"substance":16,"depth":183,"authority":83,"freshness":184,"relevant":20,"comment":185},11,9,"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。",[187],{"name":179,"url":176},[25,26,27,28,189],"精准农业",{"code":112,"slug":191,"name":192,"path":193,"parent_code":8},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":196,"locked":13,"version":33,"method":34,"evidence":197},"partial",{"candidates":198,"agricultural_evidence":39,"reason":235},[199,204,209,213,217,224,228],{"code":62,"score":118,"supported":39,"title":200,"abstract":202,"tags":203},[201],"smart agriculture",[25,189,201],[25,189],{"code":45,"score":18,"supported":39,"title":205,"abstract":206,"tags":208},[47],[207,47],"机器学习",[26],{"code":51,"score":18,"supported":39,"title":210,"abstract":211,"tags":212},[53],[135,53],[27],{"code":38,"score":12,"supported":39,"title":214,"abstract":215,"tags":216},[],[121,122,41],[28],{"code":218,"score":83,"supported":39,"title":219,"abstract":220,"tags":223},"A8",[],[221,222],"粮食安全","food security",[],{"code":139,"score":83,"supported":39,"title":225,"abstract":226,"tags":227},[],[142,143],[],{"code":229,"score":230,"supported":39,"title":231,"abstract":232,"tags":234},"H8",3,[],[233],"可持续农业",[],"一级领域明确，二级方向需要细分",[237,238],"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":240,"openalex_id":242,"authors":243,"venue":179,"cited_by_count":78,"oa_url":176,"card":254,"direction":79,"ingested_from":80},"W7220440757",[244,246,248,250,252],{"name":245,"orcid":8},"Priyanka Behera",{"name":247,"orcid":8},"Satyabrata Satapathy",{"name":249,"orcid":8},"Nakshytrajeet Kar",{"name":251,"orcid":8},"Sucharita NathSharma",{"name":253,"orcid":8},"Priyanka Shit",{"tldr":255,"method":256,"finding":257,"direction":79,"opportunity":258},"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":261,"title":262,"url":263,"summary":264,"summary_zh":265,"content":8,"source_name":266,"source_url":263,"published_at":267,"category":11,"cover_url":8,"hotness":268,"is_selected":13,"score":269,"score_detail":270,"sources":274,"tags":278,"primary_topic":281,"secondary_topics":282,"classification":283,"search_phrases":336,"slug":339,"view_count":340,"doi":341,"paper":342,"created_at":352},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":271,"substance":97,"depth":95,"authority":272,"freshness":78,"relevant":20,"comment":273},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。",[275,276],{"name":266,"url":263},{"name":266,"url":277},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[279,25,26,27,29,280],"数字乡村","遥感监测",{"code":112,"slug":191,"name":192,"path":193,"parent_code":8},[],{"status":196,"locked":13,"version":33,"method":34,"evidence":284},{"candidates":285,"agricultural_evidence":39,"reason":235},[286,292,296,301,308,312,316,320,325,331],{"code":45,"score":97,"supported":39,"title":287,"abstract":289,"tags":291},[288],"artificial intelligence",[207,290,47,288],"病虫害识别",[26],{"code":51,"score":97,"supported":39,"title":293,"abstract":294,"tags":295},[53],[135,136,53],[27],{"code":62,"score":18,"supported":39,"title":297,"abstract":299,"tags":300},[298],"precision 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智慧农业","印度精准农业AI-3357",5,"10.5281\u002Fzenodo.22914538",{"doi":341,"openalex_id":343,"authors":344,"venue":266,"cited_by_count":78,"oa_url":263,"card":347,"direction":79,"ingested_from":80},"W7214083098",[345],{"name":346,"orcid":8},"Twinkal Prakash Sawant",{"tldr":348,"method":349,"finding":350,"direction":79,"opportunity":351},"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":354,"title":355,"url":356,"summary":357,"summary_zh":8,"content":8,"source_name":358,"source_url":8,"published_at":359,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":360,"score_detail":361,"sources":363,"tags":365,"primary_topic":8,"secondary_topics":366,"classification":367,"search_phrases":394,"slug":396,"view_count":20,"doi":8,"paper":397,"created_at":405},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。","Biosystems Engineering","2026-09-13T01:00:00Z",83,{"impact":271,"substance":96,"depth":97,"authority":17,"freshness":19,"relevant":20,"comment":362},"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。",[364],{"name":358,"url":356},[25,26,27,28,280],[],{"status":32,"locked":13,"version":33,"method":34,"evidence":368},{"candidates":369,"agricultural_evidence":39,"reason":66},[370,374,378,382,386,390],{"code":38,"score":18,"supported":39,"title":371,"abstract":372,"tags":373},[121],[121,122],[28],{"code":51,"score":18,"supported":39,"title":375,"abstract":376,"tags":377},[135],[135,53],[27],{"code":302,"score":16,"supported":39,"title":379,"abstract":380,"tags":381},[306],[306],[280],{"code":139,"score":230,"supported":39,"title":383,"abstract":384,"tags":385},[],[142],[],{"code":62,"score":20,"supported":13,"title":387,"abstract":388,"tags":389},[],[],[25],{"code":45,"score":20,"supported":13,"title":391,"abstract":392,"tags":393},[],[],[26],[69,395],"农业人工智能 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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":97,"substance":414,"depth":415,"authority":17,"freshness":184,"relevant":20,"comment":416},20,17,"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。",[418],{"name":358,"url":409},[25,26,27,28,280],[],{"status":32,"locked":13,"version":33,"method":34,"evidence":422},{"candidates":423,"agricultural_evidence":13,"reason":444},[424,428,432,436,440],{"code":38,"score":16,"supported":39,"title":425,"abstract":426,"tags":427},[41],[41],[28],{"code":45,"score":16,"supported":39,"title":429,"abstract":430,"tags":431},[288],[288],[26],{"code":302,"score":16,"supported":39,"title":433,"abstract":434,"tags":435},[304],[304],[280],{"code":51,"score":16,"supported":39,"title":437,"abstract":438,"tags":439},[136],[136],[27],{"code":62,"score":20,"supported":13,"title":441,"abstract":442,"tags":443},[],[],[25],"农业相关性需要复核",[69,395],"农业人工智能农业物联网智慧农业智能灌溉-2024","10.1016\u002Fj.biosystemseng.2026.104589",{"doi":447,"openalex_id":449,"authors":450,"venue":358,"cited_by_count":78,"oa_url":8,"card":8,"direction":8,"ingested_from":80},"W7212048658",[451,454,456,458,460,462,465],{"name":452,"orcid":453},"Ehab H. Hegazi","https:\u002F\u002Forcid.org\u002F0000-0002-1468-1420",{"name":455,"orcid":8},"Jian Liu",{"name":457,"orcid":8},"Ruixia Ai",{"name":459,"orcid":8},"Lin Liu",{"name":461,"orcid":8},"Xuemei Liu",{"name":463,"orcid":464},"Jin Yuan","https:\u002F\u002Forcid.org\u002F0000-0002-5803-6626",{"name":466,"orcid":467},"G. Papadakis","https:\u002F\u002Forcid.org\u002F0000-0002-1805-5056","2026-09-10T23:30:05.849251Z",{"id":470,"title":471,"url":472,"summary":473,"summary_zh":474,"content":8,"source_name":475,"source_url":472,"published_at":476,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":477,"score_detail":478,"sources":480,"tags":482,"primary_topic":485,"secondary_topics":486,"classification":487,"search_phrases":525,"slug":528,"view_count":78,"doi":529,"paper":530,"created_at":549},5559,"Smart AI-Based Agriculture Decision Support System (AgriSens)","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85162","Traditional farming relies heavily on empirical guesswork and legacy ancestral knowledge, frequently leaving crops vulnerable to uncalibrated chemical fertilizer application, unpredictable micro-climates, and delayed foliar disease detection. This paper introduces AgriSens, a comprehensive, full-stack smart farming platform developed to empower farmers with datadriven precision agriculture. The system integrates a multi-parameter crop recommendation engine using a mathematical Gaussian-decay environmental fitness model alongside a multimodal leaf disease diagnostic system powered by Google Gemini 3.7 Flash computer vision to identify foliar pathogens from smartphone imagery. Furthermore, the architecture deploys a soiltexture-aware fertilizer optimization engine that calculates precise N-P-K (Nitrogen, Phosphorus, Potassium) nutrient deficits, a real-time agro-meteorological forecasting stream driven by the Open-Meteo API for chemical spray safety alerts, and a closedloop automated IoT irrigation telemetry simulator. To eliminate structural literacy barriers within rural farming communities, the platform features native voice synthesis and a 7-language localized translation module. Simulation benchmarks demonstrate that the platform achieves rapid visual diagnostic latencies under 2.5 seconds and optimizes water conservation by 25% to 35% compared to baseline scheduled flooding methods","传统农业严重依赖经验性猜测和祖辈传承的知识，作物常常面临化肥施用不当、微气候难以预测以及叶部病害发现滞后等问题。本文介绍AgriSens——一个综合性全栈智慧农业平台，旨在以数据驱动的精准农业赋能农民。该系统集成了基于高斯衰减环境适应度数学模型的多元参数作物推荐引擎，以及由Google Gemini 3.7 Flash计算机视觉驱动的多模态叶片病害诊断系统，可从智能手机图像中识别叶部病原体。此外，该架构还部署了土壤质地感知的肥料优化引擎，用于计算精确的氮磷钾（N-P-K）养分亏缺；由Open-Meteo API驱动的实时农业气象预报流，用于提供施药安全预警；以及闭环自动化物联网灌溉遥测模拟器。为消除农村农业社区中的结构性识字障碍，该平台具备原生语音合成功能和7种语言的本地化翻译模块。仿真基准测试表明，该平台实现了低于2.5秒的快速视觉诊断延迟，并且与基准定时漫灌方法相比，将节水效率优化了25%至35%。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":16,"substance":97,"depth":95,"authority":184,"freshness":12,"relevant":20,"comment":479},"论文提出集作物推荐、病害诊断、施肥优化与灌溉模拟于一体的全栈平台，方法有创新但尚属仿真验证阶段，具备一定参考价值。",[481],{"name":475,"url":472},[25,26,483,28,484],"精准施肥","病害诊断",{"code":45,"slug":109,"name":110,"path":111,"parent_code":112},[],{"status":114,"locked":13,"version":33,"method":34,"evidence":488},{"candidates":489,"agricultural_evidence":39},[490,497,501,508,512,516,520],{"code":45,"score":18,"supported":39,"title":491,"abstract":493,"tags":496},[492],"decision support",[494,495],"计算机视觉","computer vision",[26],{"code":62,"score":12,"supported":39,"title":498,"abstract":499,"tags":500},[],[25,189,298],[25],{"code":502,"score":132,"supported":39,"title":503,"abstract":504,"tags":507},"F3",[],[505,506],"肥料","fertilizer",[483],{"code":38,"score":132,"supported":39,"title":509,"abstract":510,"tags":511},[],[121,41],[28],{"code":51,"score":83,"supported":39,"title":513,"abstract":514,"tags":515},[],[135,53],[],{"code":139,"score":230,"supported":39,"title":517,"abstract":518,"tags":519},[],[142],[],{"code":57,"score":230,"supported":39,"title":521,"abstract":522,"tags":524},[],[523],"农业气象",[],[526,527],"AgriSens 智慧农业","Gemini 叶片病害诊断","AgriSens智慧农业-5559","10.22214\u002Fijraset.2026.85162",{"doi":529,"openalex_id":531,"authors":532,"venue":475,"cited_by_count":78,"oa_url":472,"card":543,"direction":79,"ingested_from":80},"W7223158459",[533,535,537,539,541],{"name":534,"orcid":8},"Harshini G. K.",{"name":536,"orcid":8},"Monisha B. P.",{"name":538,"orcid":8},"Manoj G.",{"name":540,"orcid":8},"Chandana K. M.",{"name":542,"orcid":8},"Prof. Sushma S.",{"tldr":544,"method":545,"finding":546,"direction":547,"opportunity":548},"构建AgriSens全栈智慧农业决策支持平台，集成作物推荐、病害诊断、施肥优化与灌溉模拟。","高斯衰减环境适应度模型、Gemini视觉诊断、Open-Meteo API与Io","视觉诊断延迟低于2.5秒，节水25%–35%，并支持7语言语音以降低识字门槛。","农业人工智能与决策模型","可验证多模态病害诊断在真实田间的准确率，并研究语音多语言交互对农户采纳行为的影响。","2026-10-22T11:20:18.985947Z"]