[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4997":3,"related-4997":100},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":73,"slug":76,"view_count":64,"doi":77,"paper":78,"created_at":99},4997,"Adaptive Reinforcement Learning Models for Autonomous Resource Optimization","https:\u002F\u002Fdoi.org\u002F10.17148\u002Fijarcce.2026.15960","Adaptive Reinforcement Learning (ARL) has become a paradigm shift of autonomous optimization of resources in dynamic and uncertain settings.The conventional management methods of resources are based on inflexible models and rule-based decision making systems which are not responsive to the environmental changes, system needs or operational limitations.Adaptive reinforcement learning models deal with such constraints by allowing systems to adopt optimal resource allocation policies by a continuous interaction with the environment, adaptation through feedback and long run optimization of rewards.The models are especially useful when working with such systems as smart grids, autonomous vehicles, cloud computing, smart agriculture, and intelligent infrastructure systems.ARL ensures the efficient use of the computational, energy, and physical resources through the introduction of adaptive learning mechanisms, multi-agent coordination, and real-time optimization of decisions.Recent progress in deep reinforcement learning, multi-agent reinforcement learning as well as hybrid intelligent frameworks has greatly contributed in increasing the scale, convergence rate and accuracy of decisions.Besides, the incorporation with edge computing, digital twins, and Internet of Things (IoT) technologies allows managing the resources in a decentralized and autonomous way.This paper provides an in-depth review of adaptive reinforcement learning frameworks in the context of autonomous resource optimization, their approaches, functionality, and industrial use.Experimental evidence has shown that learning about adaptive reinforcement has great efficacy in streamlining processes and operating at a lower cost, the system remains stable and autonomous decision making with complex resource management settings is successful.","自适应强化学习（Adaptive Reinforcement Learning, ARL）已成为动态不确定环境下自主资源优化的一种范式转变。传统的资源管理方法基于刚性模型和规则驱动的决策系统，无法对环境变化、系统需求或运行限制做出响应。自适应强化学习模型通过允许系统与环境持续交互、借助反馈进行自适应调整以及长期优化奖励，从而获得最优资源配置策略，以此应对上述约束。此类模型在处理智能电网、自动驾驶车辆、云计算、智慧农业和智能基础设施系统等场景时尤为有效。ARL通过引入自适应学习机制、多智能体协调以及实时决策优化，确保计算资源、能源和物理资源的高效利用。深度强化学习、多智能体强化学习以及混合智能框架方面的最新进展，极大地推动了决策规模的扩大、收敛速度的提升和决策精度的提高。此外，与边缘计算、数字孪生和物联网（IoT）技术的融合，使得以去中心化和自主方式管理资源成为可能。本文对面向自主资源优化的自适应强化学习框架进行了深入综述，涵盖其方法、功能及工业应用。实验证据表明，自适应强化学习在简化流程和降低运行成本方面具有显著功效，系统保持稳定，并能在复杂资源管理场景中成功实现自主决策。",null,"IJARCCE","2026-10-13T00:00:00Z","论文",10,false,58,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,14,15,7,1,"综述性论文，方法层面有参考价值，但缺乏具体农业场景数据与实证，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","智慧农业","农业人工智能","强化学习","资源优化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,62,68],{"code":45,"score":13,"supported":46,"title":47,"abstract":48,"tags":51},"I1",true,[],[27,49,50],"数字孪生","smart agriculture",[27],{"code":32,"score":53,"supported":46,"title":54,"abstract":55,"tags":61},9,[],[56,57,58,59,60],"物联网","边缘计算","internet of things","IoT","edge computing",[],{"code":63,"score":64,"supported":14,"title":65,"abstract":66,"tags":67},"I2",2,[],[],[28,29],{"code":69,"score":21,"supported":14,"title":70,"abstract":71,"tags":72},"I6",[],[],[26],[74,75],"自适应强化学习 资源优化","智慧农业 强化学习","自适应强化学习资源优化-4997","10.17148\u002Fijarcce.2026.15960",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":90,"oa_url":6,"card":91,"direction":97,"ingested_from":98},"W7221832990",[81,84,87],{"name":82,"orcid":83},"Prateek Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-0521-8124",{"name":85,"orcid":86},"Sohit Agarwal","https:\u002F\u002Forcid.org\u002F0000-0002-1280-7907",{"name":88,"orcid":89},"Jitesh Rajendra Neve","https:\u002F\u002Forcid.org\u002F0009-0009-2358-1147",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"综述自适应强化学习在动态不确定环境下自主资源优化的框架、方法与应用。","综述深度强化学习、多智能体强化学习及边缘计算、数字孪生、物联网融合方法。","自适应强化学习能高效分配计算、能源与物理资源，降低成本并保持系统稳定自主决策。","农业人工智能与决策模型","可将ARL用于智慧农业中水肥、农机与能源的实时协同调度，弥补农业场景多智能体与数字孪生结合的空白。","智慧农业 \u002F 农业物联网","openalex","2026-10-15T23:30:12.824701Z",{"total":101,"page":21,"page_size":101,"items":102},6,[103,174,257,365,454,526],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":116,"tags":118,"primary_topic":120,"secondary_topics":121,"classification":126,"search_phrases":160,"slug":163,"view_count":90,"doi":164,"paper":165,"created_at":173},4658,"Energy-Efficient Smart Agriculture Management Framework Using IoT and Ensemble Machine Learning for Intelligent Crop Health Monitoring and Resource Optimization","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.62s.32","Context: Smart agriculture plays an important role in addressing the challenges of food security, environmental sustainability, and sustainable farming through IoT.Farmers are facing many issues in traditional farming, such as inefficient monitoring and poor crop health detection.Traditional farming has a negative impact on crop production and sustainability.In this regard, the development of intelligent and energy-efficient agricultural monitoring systems has become crucial for contemporary precision farming applications.Objective: This study aims to monitor crop health and optimize resource use using machine learning techniques.Our proposed system intends to monitor crop conditions in real time, enhance agricultural management, and minimize unnecessary sensor utilization and communication overhead.Material\u002Fmethods: In this paper, we used five machine learning classifiers to monitor crop health using sensor data.The agricultural sensor data (soil moisture, soil temperature, soil pH, humidity, air temperature, solar radiation, wind speed, NDVI index, and 5G latency) were used for crop health monitoring.The models, such as RF, XGBoost, LightGBM, and CatBoost, and soft voting were used for crop health classification into the Healthy, High_Stress, and Moderate_Stress categories.The experimental observation revealed that soft voting achieved the highest accuracy of 94.5%, macro-F1 score of 0.945, weighted F1 score of 0.945, and AUC-ROC score of 0.988 compared to the other models.The cross-validation results further validated the robustness and stability of the proposed framework.The IoTenabled intelligent agriculture management system offers an efficient, scalable solution for real-time crop health monitoring, energy-efficient resource optimization, and sustainable smart farming.","背景：智慧农业在借助物联网（IoT）应对粮食安全、环境可持续性和可持续耕作等挑战方面发挥着重要作用。农民在传统耕作中面临诸多问题，例如监测效率低下和作物健康检测不佳。传统耕作对作物产量和可持续性产生负面影响。在此背景下，开发智能且节能的农业监测系统对于当代精准农业应用已变得至关重要。目标：本研究旨在利用机器学习技术监测作物健康并优化资源利用。我们提出的系统旨在实时监测作物状况、增强农业管理并最大限度减少不必要的传感器使用和通信开销。材料\u002F方法：在本文中，我们使用了五种机器学习分类器，利用传感器数据监测作物健康。农业传感器数据（土壤湿度、土壤温度、土壤pH值、湿度、空气温度、太阳辐射、风速、NDVI指数和5G延迟）被用于作物健康监测。RF、XGBoost、LightGBM和CatBoost等模型以及软投票被用于将作物健康分类为健康、高胁迫和中等胁迫类别。实验观察表明，与其他模型相比，软投票达到了最高的准确率94.5%、宏F1分数0.945、加权F1分数0.945和AUC-ROC分数0.988。交叉验证结果进一步验证了所提出框架的鲁棒性和稳定性。该物联网赋能的智能农业管理系统为实时作物健康监测、节能资源优化和可持续智慧农业提供了一种高效、可扩展的解决方案。","International Journal of Drug Delivery Technology","2026-10-10T00:00:00Z",52,{"impact":113,"substance":114,"depth":18,"authority":101,"freshness":113,"relevant":21,"comment":115},8,16,"方法组合与多源传感器数据有一定新意，但期刊权威性弱、属实验室验证，公共价值有限，不宜进入每日精选。",[117],{"name":109,"url":106},[27,28,56,119,30],"作物健康监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[122],{"code":63,"slug":123,"name":124,"path":125,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":127},{"candidates":128,"agricultural_evidence":46},[129,135,140,146,153],{"code":45,"score":130,"supported":46,"title":131,"abstract":132,"tags":134},18,[50],[27,133,50],"精准农业",[27],{"code":32,"score":130,"supported":46,"title":136,"abstract":137,"tags":139},[59],[56,138,59],"农业传感",[56],{"code":63,"score":19,"supported":46,"title":141,"abstract":143,"tags":145},[142],"machine learning",[144,142],"机器学习",[28],{"code":147,"score":101,"supported":46,"title":148,"abstract":149,"tags":152},"A8",[],[150,151],"粮食安全","food security",[],{"code":154,"score":101,"supported":46,"title":155,"abstract":156,"tags":159},"H2",[],[157,158],"土壤","soil",[],[161,162],"IoT 作物健康监测","集成学习 精准农业","IoT作物健康监测-4658","10.25258\u002Fijddt.16.62s.32",{"doi":164,"openalex_id":166,"authors":167,"venue":109,"cited_by_count":90,"oa_url":106,"card":168,"direction":97,"ingested_from":98},"W7221334384",[],{"tldr":169,"method":170,"finding":171,"direction":97,"opportunity":172},"用IoT传感器数据与集成机器学习实现作物健康分类与资源优化。","五种分类器（RF、XGBoost、LightGBM、CatBoost、软投票）结","软投票集成模型准确率达94.5%，AUC-ROC为0.988，优于单一模型。","可探索边缘端轻量化集成模型与能耗-精度联合优化，降低传感器通信开销。","2026-10-11T23:30:07.636015Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":182,"is_selected":14,"score":183,"score_detail":184,"sources":189,"tags":193,"primary_topic":197,"secondary_topics":198,"classification":199,"search_phrases":241,"slug":244,"view_count":64,"doi":245,"paper":246,"created_at":256},2312,"MridAI: Autonomous Edge-to-Conversational IoT for Democratising Precision Agriculture via Neuro-Symbolic Telemetry","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724805","While sensor-guided precision agriculture improves water efficiency and curtails chemical run-off, adoption across smallholder farm land in the Global South remains under 1%. Commercial telemetry systems are constrained by high capital acquisition costs (>$300) and complex, dashboard-centric mobile applications that impose heavy cognitive burdens on low-literacy farmers. This paper proposes MridAI, a low-cost (\u003C$45 \u002F ₹3,420 COGS) autonomous in-ground agro-telemetry node coupled with a cloud-based neuro-symbolic artificial intelligence advisory pipeline. The physical layer integrates a multi-parameter Modbus RS485 sensor, an ESP32-C3 micro-controller, an Indian-band 4G LTE Cat-1 modem, and an energy harvesting subsystem within an IP67-rated solar stem. To eliminate measurement errors in high-clay tropical soils (Vertisols), an on-device digital signal-processing pipeline applies an adapted Topp dielectric correction alongside a one-dimensional discrete Kalman filter. To overcome rural telecommunication instability, the firmware implements an asynchronous, non-volatile store-and-forward ring buffer. At the cloud layer, an analytical solver calculates deterministic FAO-56 evapotranspiration deficits and enforces Indian Council of Agricultural Research (ICAR) chemical boundaries, strictly isolating numerical computation from an instruction-tuned Large Language Model (LLM). The generative model functions solely as a linguistic translator, delivering actionable, dialect-adapted recommendations directly via the Meta WhatsApp Cloud API without requiring third-party application downloads. Empirical power-budget modelling confirms indefinite operational autonomy (>240 days without solar irradiance), establishing a scalable paradigm for digital agriculture.","尽管传感器引导的精准农业提高了用水效率并减少了化学品径流，但在全球南方小农农田中的采用率仍不足1%。商业遥测系统受制于高昂的资本购置成本（超过300美元）以及复杂的、以仪表盘为中心的移动应用程序，后者给低识字率农民带来了沉重的认知负担。本文提出MridAI，一种低成本（物料清单成本低于45美元\u002F3，420印度卢比）的自主地下农业遥测节点，并耦合基于云的神经符号人工智能咨询流水线。物理层将多参数Modbus RS485传感器、ESP32-C3微控制器、印度频段4G LTE Cat-1调制解调器以及能量收集子系统集成于IP67防护等级的太阳能杆体内。为消除高黏土热带土壤（变性土）中的测量误差，设备端数字信号处理流水线采用适配的Topp介电校正与一维离散卡尔曼滤波器。为克服农村电信不稳定性，固件实现了异步、非易失性的存储转发环形缓冲区。在云层，分析求解器计算确定性的FAO-56蒸散亏缺，并执行印度农业研究理事会（ICAR）的化学品边界，将数值计算与指令微调的大语言模型（LLM）严格隔离。生成模型仅充当语言翻译器，通过Meta WhatsApp Cloud API直接提供可操作的、适配方言的建议，无需下载第三方应用程序。实证功率预算建模证实了无限期运行自主性（无太阳辐照下超过240天），为数字农业建立了一种可扩展的范式。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-12T00:00:00Z",25,85,{"impact":185,"substance":186,"depth":130,"authority":187,"freshness":53,"relevant":21,"comment":188},22,23,13,"面向全球南方小农户的低成本自主土壤遥测节点与神经符号AI咨询管线，方法新颖、成本与能耗数据具体，对农业信息化与普惠数字农业有实质参考价值。",[190,191],{"name":180,"url":177},{"name":180,"url":192},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724804",[26,27,28,194,195,196],"农业物联网","小农户","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":200},{"candidates":201,"agricultural_evidence":46},[202,209,213,219,226,232,237],{"code":45,"score":130,"supported":46,"title":203,"abstract":205,"tags":208},[204],"precision agriculture",[133,206,204,207],"数字农业","digital agriculture",[27],{"code":32,"score":53,"supported":46,"title":210,"abstract":211,"tags":212},[59],[],[194],{"code":63,"score":20,"supported":46,"title":214,"abstract":215,"tags":218},[],[216,217],"artificial intelligence","large language model",[28],{"code":220,"score":221,"supported":46,"title":222,"abstract":223,"tags":225},"G2",4,[],[224],"smallholder",[195],{"code":227,"score":221,"supported":46,"title":228,"abstract":229,"tags":231},"H3",[],[230],"evapotranspiration",[196],{"code":154,"score":233,"supported":46,"title":234,"abstract":235,"tags":236},3,[],[157],[],{"code":69,"score":21,"supported":14,"title":238,"abstract":239,"tags":240},[],[],[26],[242,243],"农业人工智能 农业物联网 数字乡村 智慧农业","农业人工智能 农业物联网","农业人工智能农业物联网数字乡村智慧农业-2312","10.5281\u002Fzenodo.22724805",{"doi":245,"openalex_id":247,"authors":248,"venue":180,"cited_by_count":90,"oa_url":177,"card":251,"direction":97,"ingested_from":98},"W7212357737",[249],{"name":250,"orcid":9},"Pranit Kamble",{"tldr":252,"method":253,"finding":254,"direction":97,"opportunity":255},"提出低成本自主地下物联网节点与神经符号AI咨询管道，让小农户用WhatsApp获取精准农业建议。","ESP32-C3与Modbus传感器、Topp校正和卡尔曼滤波、FAO-56与L","系统成本低于45美元，可独立运行超240天，无需下载应用即可通过WhatsApp获得方言化建议。","可探索低资源语言与方言适配的LLM农业咨询，以及神经符号系统在更多作物和土壤类型中的泛化验证。","2026-09-13T23:30:14.323767Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":263,"source_url":260,"published_at":264,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":266,"sources":268,"tags":270,"primary_topic":273,"secondary_topics":278,"classification":281,"search_phrases":335,"slug":338,"view_count":90,"doi":339,"paper":340,"created_at":364},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","2026-10-21T00:00:00Z",79,{"impact":130,"substance":185,"depth":130,"authority":17,"freshness":53,"relevant":21,"comment":267},"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。",[269],{"name":263,"url":260},[27,28,271,272,196],"遥感","作物水分胁迫",{"code":227,"slug":274,"name":275,"path":276,"parent_code":277},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[279,280],{"code":63,"slug":123,"name":124,"path":125,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":282},{"candidates":283,"agricultural_evidence":46},[284,289,293,297,302,308,315,322,329],{"code":227,"score":19,"supported":46,"title":285,"abstract":286,"tags":288},[274],[287,274],"灌溉",[196],{"code":63,"score":13,"supported":46,"title":290,"abstract":291,"tags":292},[],[144,142,216],[28],{"code":32,"score":53,"supported":46,"title":294,"abstract":295,"tags":296},[],[56,58,59],[],{"code":45,"score":20,"supported":46,"title":298,"abstract":299,"tags":301},[],[49,300],"digital twin",[27],{"code":303,"score":20,"supported":46,"title":304,"abstract":305,"tags":307},"I3",[],[271,306],"remote sensing",[271],{"code":309,"score":101,"supported":46,"title":310,"abstract":311,"tags":314},"A2",[],[312,313],"小麦","wheat",[],{"code":316,"score":101,"supported":46,"title":317,"abstract":318,"tags":321},"A3",[],[319,320],"玉米","maize",[],{"code":323,"score":101,"supported":46,"title":324,"abstract":325,"tags":328},"A5",[],[326,327],"马铃薯","potato",[],{"code":330,"score":233,"supported":46,"title":331,"abstract":332,"tags":334},"B2",[],[333],"葡萄",[],[336,337],"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":339,"openalex_id":341,"authors":342,"venue":263,"cited_by_count":90,"oa_url":260,"card":358,"direction":97,"ingested_from":98},"W7223088921",[343,346,349,352,355],{"name":344,"orcid":345},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":347,"orcid":348},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":350,"orcid":351},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":353,"orcid":354},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":356,"orcid":357},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":359,"method":360,"finding":361,"direction":362,"opportunity":363},"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":366,"title":367,"url":368,"summary":369,"summary_zh":370,"content":9,"source_name":371,"source_url":368,"published_at":264,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":372,"score_detail":373,"sources":375,"tags":377,"primary_topic":379,"secondary_topics":380,"classification":383,"search_phrases":430,"slug":433,"view_count":90,"doi":434,"paper":435,"created_at":453},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)",80,{"impact":130,"substance":185,"depth":130,"authority":187,"freshness":53,"relevant":21,"comment":374},"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。",[376],{"name":371,"url":368},[27,28,56,133,378],"气候韧性",{"code":63,"slug":123,"name":124,"path":125,"parent_code":36},[381,382],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":227,"slug":274,"name":275,"path":276,"parent_code":277},{"status":39,"locked":14,"version":40,"method":41,"evidence":384},{"candidates":385,"agricultural_evidence":46},[386,394,398,402,407,414,418,424],{"code":63,"score":130,"supported":46,"title":387,"abstract":388,"tags":393},[216],[389,144,390,142,391,392],"深度学习","计算机视觉","deep learning","computer vision",[28],{"code":45,"score":114,"supported":46,"title":395,"abstract":396,"tags":397},[204],[133,204],[27,133],{"code":32,"score":13,"supported":46,"title":399,"abstract":400,"tags":401},[],[56,57,59,60],[56],{"code":227,"score":53,"supported":46,"title":403,"abstract":404,"tags":406},[],[287,405,274],"水资源",[],{"code":408,"score":20,"supported":46,"title":409,"abstract":410,"tags":413},"H7",[],[411,412],"气候变化","climate change",[378],{"code":154,"score":101,"supported":46,"title":415,"abstract":416,"tags":417},[],[157,158],[],{"code":419,"score":233,"supported":46,"title":420,"abstract":421,"tags":423},"A7",[],[422],"间作",[],{"code":425,"score":233,"supported":46,"title":426,"abstract":427,"tags":429},"F3",[],[428],"养分管理",[],[431,432],"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":434,"openalex_id":436,"authors":437,"venue":371,"cited_by_count":90,"oa_url":368,"card":448,"direction":97,"ingested_from":98},"W7223121456",[438,441,443,445],{"name":439,"orcid":440},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":442,"orcid":9},"Oluwadare OYEBODE",{"name":444,"orcid":9},"Iretiayo Adelaiye",{"name":446,"orcid":447},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":449,"method":450,"finding":451,"direction":95,"opportunity":452},"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":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":180,"source_url":457,"published_at":460,"category":12,"cover_url":9,"hotness":182,"is_selected":14,"score":461,"score_detail":462,"sources":466,"tags":470,"primary_topic":472,"secondary_topics":473,"classification":475,"search_phrases":503,"slug":506,"view_count":90,"doi":507,"paper":508,"created_at":525},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","2026-11-25T00:00:00Z",73,{"impact":185,"substance":463,"depth":464,"authority":187,"freshness":90,"relevant":21,"comment":465},21,17,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[467,468],{"name":180,"url":457},{"name":180,"url":469},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,28,194,471,196],"区块链溯源",{"code":227,"slug":274,"name":275,"path":276,"parent_code":277},[474],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":476},{"candidates":477,"agricultural_evidence":46},[478,482,486,490,494,499],{"code":227,"score":19,"supported":46,"title":479,"abstract":480,"tags":481},[274],[287,274],[196],{"code":32,"score":13,"supported":46,"title":483,"abstract":484,"tags":485},[],[56,58,59],[194],{"code":63,"score":221,"supported":46,"title":487,"abstract":488,"tags":489},[],[216],[28],{"code":220,"score":233,"supported":46,"title":491,"abstract":492,"tags":493},[],[195],[],{"code":495,"score":21,"supported":14,"title":496,"abstract":497,"tags":498},"E4",[],[],[471],{"code":45,"score":21,"supported":14,"title":500,"abstract":501,"tags":502},[],[],[27],[504,505],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":507,"openalex_id":509,"authors":510,"venue":180,"cited_by_count":90,"oa_url":457,"card":519,"direction":97,"ingested_from":98},"W7223034914",[511,514,516],{"name":512,"orcid":513},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":515,"orcid":9},"Dr.T.Baskar",{"name":517,"orcid":518},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":520,"method":521,"finding":522,"direction":523,"opportunity":524},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":527,"title":528,"url":529,"summary":530,"summary_zh":531,"content":9,"source_name":532,"source_url":529,"published_at":533,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":534,"score_detail":535,"sources":537,"tags":539,"primary_topic":542,"secondary_topics":543,"classification":545,"search_phrases":561,"slug":564,"view_count":90,"doi":565,"paper":566,"created_at":586},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",76,{"impact":114,"substance":463,"depth":130,"authority":17,"freshness":53,"relevant":21,"comment":536},"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[538],{"name":532,"url":529},[27,28,194,540,541],"大语言模型","传感器标准化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[544],{"code":63,"slug":123,"name":124,"path":125,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":546},{"candidates":547,"agricultural_evidence":46},[548,552,557],{"code":32,"score":130,"supported":46,"title":549,"abstract":550,"tags":551},[59],[56,58,59],[194],{"code":63,"score":114,"supported":46,"title":553,"abstract":555,"tags":556},[554],"decision support",[217,554],[28,540],{"code":45,"score":20,"supported":46,"title":558,"abstract":559,"tags":560},[],[27,50],[27],[562,563],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":565,"openalex_id":567,"authors":568,"venue":532,"cited_by_count":90,"oa_url":529,"card":581,"direction":97,"ingested_from":98},"W7222920424",[569,571,573,575,577,579],{"name":570,"orcid":9},"Leqi Lai",{"name":572,"orcid":9},"Langtao Duan",{"name":574,"orcid":9},"Yuhao Chen",{"name":576,"orcid":9},"Yuhang Zheng",{"name":578,"orcid":9},"Jinyu Yang",{"name":580,"orcid":9},"Mingyao Wen",{"tldr":582,"method":583,"finding":584,"direction":97,"opportunity":585},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z"]