[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4420":3,"related-4420":112},{"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":43,"search_phrases":88,"slug":91,"view_count":21,"doi":92,"paper":93,"created_at":111},4420,"AI-Enabled Sustainable Precision Farming Using Raspberry Pi and Coral Edge TPU with Cloud-Based Model Training","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85019","Precision Agriculture requires intelligent, adaptable and efficient technology that is capable of making decisions in agriculture at the right time, based on the condition of the plants and the environment. This paper proposes the development of an AI-based sustainable precision farming system based on computer vision, cloud based model training, Raspberry Pi, Coral Edge TPU and the Internet of Things (IoT) sensing. The proposed architecture's sensing layer, edge processing layer, cloud intelligence layer, and application layer enables a complete data processing pipeline from the field to real time decision support. Crop photos are collected for AI condition classification and agricultural conditions such as soil moisture, temperature, humidity and light intensity are collected by built-in sensors. After the above preprocessing, normalisation, image resizing and augmentation, the dataset is divided into training, validation and test sets. The trained deep learning models in a cloud environment are then quantized and converted to be optimized for the edge, following this step. Following this step, the trained deep learning models are quantized and converted for optimization on edge. The optimized model achieves an exemplary accuracy of 94.82%, precision of 94.47%, recall of 94.05% and F1-score of 94.26%. Deploying with the Coral Edge TPU reduces inference latency to approximately 38ms compared to Raspberry Pi-only processing, increases processing throughput to 26.3 pictures per second and reduces CPU usage. The edge-assisted architecture reduces the need for constant connection to the cloud, giving local decision making a leg up. Illustrative sustainability study estimates that condition based monitoring can save 22% of the irrigation water used every day. The findings indicated that the introduction of the edge AI and cloud intelligence in precision agricultural systems that are responsive, scalable, and resource-efficient is technically possible.","精准农业需要智能、自适应且高效的技术，能够根据植物和环境状况在恰当时机做出农业决策。本文提出开发一种基于人工智能的可持续精准农业系统，该系统依托计算机视觉、基于云端的模型训练、树莓派（Raspberry Pi）、Coral边缘TPU（Edge TPU）以及物联网（IoT）传感技术。所提出的架构包含感知层、边缘处理层、云智能层和应用层，实现了从田间到实时决策支持的完整数据处理流水线。系统采集作物照片用于AI状态分类，并通过内置传感器采集土壤湿度、温度、空气湿度和光照强度等农业环境条件。经过上述预处理、归一化、图像缩放和数据增强后，数据集被划分为训练集、验证集和测试集。随后，在云环境中训练好的深度学习模型经过量化和转换，针对边缘设备进行优化。优化后的模型达到了94.82%的准确率、94.47%的精确率、94.05%的召回率和94.26%的F1分数，表现优异。与仅使用树莓派处理相比，部署Coral边缘TPU将推理延迟降低至约38毫秒，将处理吞吐量提升至每秒26.3张图片，并降低了CPU占用率。该边缘辅助架构减少了对云端持续连接的需求，提升了本地决策能力。一项示例性可持续性研究估计，基于状态的监测可节省22%的每日灌溉用水量。研究结果表明，在精准农业系统中引入边缘AI和云智能，构建响应迅速、可扩展且资源高效的农业系统，在技术上是可行的。",null,"International Journal for Research in Applied Science and Engineering Technology","2026-10-07T00:00:00Z","论文",10,false,55,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,15,6,1,"技术路线完整、指标具体，但属单篇学术论文，产业影响有限，可作为智慧农业技术参考而非每日精选头条。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","边缘计算","物联网","精准灌溉",{"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",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,57,66,74,81],{"code":32,"score":50,"supported":51,"title":52,"abstract":53,"tags":56},11,true,[],[29,54,55],"internet of things","IoT",[29,28],{"code":39,"score":13,"supported":51,"title":58,"abstract":59,"tags":65},[],[60,61,62,63,64],"深度学习","计算机视觉","deep learning","computer vision","decision support",[27],{"code":67,"score":68,"supported":51,"title":69,"abstract":70,"tags":73},"H3",7,[],[71,72],"灌溉","irrigation",[30],{"code":75,"score":68,"supported":51,"title":76,"abstract":77,"tags":80},"I1",[],[78,79],"精准农业","precision agriculture",[26],{"code":82,"score":20,"supported":51,"title":83,"abstract":84,"tags":87},"H2",[],[85,86],"土壤","soil",[],[89,90],"Raspberry Pi Coral Edge TPU 精准农业","边缘AI 作物分类 灌溉节水","RaspberryPiCoralEdgeTPU精准农业-4420","10.22214\u002Fijraset.2026.85019",{"doi":92,"openalex_id":94,"authors":95,"venue":10,"cited_by_count":103,"oa_url":6,"card":104,"direction":108,"ingested_from":110},"W7220855887",[96,99,101],{"name":97,"orcid":98},"Prakash Kanade","https:\u002F\u002Forcid.org\u002F0000-0001-6296-9368",{"name":100,"orcid":9},"Shetty Chirag",{"name":102,"orcid":9},"Sunay Kanade",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"提出基于树莓派与Coral Edge TPU的边缘AI精准农业系统，实现作物状态分类与实时决策。","计算机视觉、云端模型训练与量化、边缘TPU部署、IoT多传感器融合。","模型准确率94.82%，推理延迟约38ms，条件监测可节水22%。","智慧农业 \u002F 农业物联网","可探索多作物泛化、边缘模型自适应更新及水肥药协同优化，并量化长期可持续效益。","openalex","2026-10-08T23:30:09.663393Z",{"total":20,"page":21,"page_size":20,"items":113},[114,201,308,395,471,529],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":9,"content":9,"source_name":119,"source_url":117,"published_at":120,"category":12,"cover_url":9,"hotness":15,"is_selected":14,"score":121,"score_detail":122,"sources":127,"tags":137,"primary_topic":139,"secondary_topics":140,"classification":146,"search_phrases":179,"slug":182,"view_count":183,"doi":184,"paper":185,"created_at":200},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",74,{"impact":18,"substance":123,"depth":18,"authority":124,"freshness":125,"relevant":21,"comment":126},22,13,3,"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[128,129,132,134],{"name":119,"url":117},{"name":130,"url":131},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":130,"url":133},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":135,"url":136},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[26,27,28,29,138],"智能灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[141,145],{"code":67,"slug":72,"name":142,"path":143,"parent_code":144},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":147},{"candidates":148,"agricultural_evidence":51},[149,155,160,165,171,175],{"code":32,"score":150,"supported":51,"title":151,"abstract":153,"tags":154},27,[55,152],"edge computing",[54,55,152],[29,28],{"code":67,"score":156,"supported":51,"title":157,"abstract":158,"tags":159},12,[72],[72],[138],{"code":39,"score":156,"supported":51,"title":161,"abstract":163,"tags":164},[162],"machine learning",[162],[27],{"code":166,"score":125,"supported":51,"title":167,"abstract":168,"tags":170},"A8",[],[169],"food security",[],{"code":82,"score":125,"supported":51,"title":172,"abstract":173,"tags":174},[],[86],[],{"code":75,"score":21,"supported":14,"title":176,"abstract":177,"tags":178},[],[],[26],[180,181],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业智能灌溉边缘计算-1249",4,"10.3389\u002Ffsufs.2026.1827416",{"doi":184,"openalex_id":186,"authors":187,"venue":119,"cited_by_count":103,"oa_url":194,"card":195,"direction":108,"ingested_from":110},"W7204189894",[188,191],{"name":189,"orcid":190},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":192,"orcid":193},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":196,"method":197,"finding":198,"direction":108,"opportunity":199},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":208,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":210,"sources":213,"tags":215,"primary_topic":218,"secondary_topics":219,"classification":222,"search_phrases":278,"slug":281,"view_count":103,"doi":282,"paper":283,"created_at":307},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":18,"substance":123,"depth":18,"authority":156,"freshness":211,"relevant":21,"comment":212},9,"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。",[214],{"name":207,"url":204},[26,27,216,217,30],"遥感","作物水分胁迫",{"code":67,"slug":72,"name":142,"path":143,"parent_code":144},[220,221],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":223},{"candidates":224,"agricultural_evidence":51},[225,229,235,239,245,251,258,265,272],{"code":67,"score":19,"supported":51,"title":226,"abstract":227,"tags":228},[72],[71,72],[30],{"code":39,"score":13,"supported":51,"title":230,"abstract":231,"tags":234},[],[232,162,233],"机器学习","artificial intelligence",[27],{"code":32,"score":211,"supported":51,"title":236,"abstract":237,"tags":238},[],[29,54,55],[],{"code":75,"score":68,"supported":51,"title":240,"abstract":241,"tags":244},[],[242,243],"数字孪生","digital twin",[26],{"code":246,"score":68,"supported":51,"title":247,"abstract":248,"tags":250},"I3",[],[216,249],"remote sensing",[216],{"code":252,"score":20,"supported":51,"title":253,"abstract":254,"tags":257},"A2",[],[255,256],"小麦","wheat",[],{"code":259,"score":20,"supported":51,"title":260,"abstract":261,"tags":264},"A3",[],[262,263],"玉米","maize",[],{"code":266,"score":20,"supported":51,"title":267,"abstract":268,"tags":271},"A5",[],[269,270],"马铃薯","potato",[],{"code":273,"score":125,"supported":51,"title":274,"abstract":275,"tags":277},"B2",[],[276],"葡萄",[],[279,280],"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":282,"openalex_id":284,"authors":285,"venue":207,"cited_by_count":103,"oa_url":204,"card":301,"direction":108,"ingested_from":110},"W7223088921",[286,289,292,295,298],{"name":287,"orcid":288},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":290,"orcid":291},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":293,"orcid":294},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":296,"orcid":297},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":299,"orcid":300},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":302,"method":303,"finding":304,"direction":305,"opportunity":306},"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":309,"title":310,"url":311,"summary":312,"summary_zh":313,"content":9,"source_name":314,"source_url":311,"published_at":208,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":318,"tags":320,"primary_topic":322,"secondary_topics":323,"classification":326,"search_phrases":370,"slug":373,"view_count":103,"doi":374,"paper":375,"created_at":394},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":18,"substance":123,"depth":18,"authority":124,"freshness":211,"relevant":21,"comment":317},"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。",[319],{"name":314,"url":311},[26,27,29,78,321],"气候韧性",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[324,325],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":67,"slug":72,"name":142,"path":143,"parent_code":144},{"status":44,"locked":14,"version":45,"method":46,"evidence":327},{"candidates":328,"agricultural_evidence":51},[329,333,338,342,347,354,358,364],{"code":39,"score":18,"supported":51,"title":330,"abstract":331,"tags":332},[233],[60,232,61,162,62,63],[27],{"code":75,"score":334,"supported":51,"title":335,"abstract":336,"tags":337},16,[79],[78,79],[26,78],{"code":32,"score":13,"supported":51,"title":339,"abstract":340,"tags":341},[],[29,28,55,152],[29],{"code":67,"score":211,"supported":51,"title":343,"abstract":344,"tags":346},[],[71,345,72],"水资源",[],{"code":348,"score":68,"supported":51,"title":349,"abstract":350,"tags":353},"H7",[],[351,352],"气候变化","climate change",[321],{"code":82,"score":20,"supported":51,"title":355,"abstract":356,"tags":357},[],[85,86],[],{"code":359,"score":125,"supported":51,"title":360,"abstract":361,"tags":363},"A7",[],[362],"间作",[],{"code":365,"score":125,"supported":51,"title":366,"abstract":367,"tags":369},"F3",[],[368],"养分管理",[],[371,372],"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":374,"openalex_id":376,"authors":377,"venue":314,"cited_by_count":103,"oa_url":311,"card":388,"direction":108,"ingested_from":110},"W7223121456",[378,381,383,385],{"name":379,"orcid":380},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":382,"orcid":9},"Oluwadare OYEBODE",{"name":384,"orcid":9},"Iretiayo Adelaiye",{"name":386,"orcid":387},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":389,"method":390,"finding":391,"direction":392,"opportunity":393},"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。","农业人工智能与决策模型","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":396,"title":397,"url":398,"summary":399,"summary_zh":400,"content":9,"source_name":130,"source_url":398,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":402,"sources":406,"tags":408,"primary_topic":410,"secondary_topics":416,"classification":419,"search_phrases":452,"slug":455,"view_count":21,"doi":456,"paper":457,"created_at":470},4660,"IoT-Based Automatic Cotton Leaf Disease Detection, Infection Percentage Estimation, and Treatment Recommendation Using Raspberry PI","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280692","Cotton is one of the most important commercial crops worldwide, and its productivity is significantly affected by various leaf diseases such as bacterial blight, alter aria leaf spot, Cercospora leaf spot, powdery mildew, and leaf curl disease. Early and accurate identification of these diseases is essential to minimize crop losses and improve yield. Traditional disease diagnosis relies on manual field inspection by experts, which is time-consuming, labour-intensive, and often impractical for large agricultural fields. The proposed system integrates a Raspberry Pi5, a high-resolution camera module, environmental sensors, and wireless communication technologies to create an intelligent crop monitoring platform. Images of cotton leaves are captured using the Raspberry Pi camera and processed through computer vision techniques for leaf segmentation and feature extraction. A lightweight deep learning model based on MobileNetV2 is employed to automatically identify different cotton leaf diseases. In addition to disease classification, image segmentation techniques are used to calculate the percentage of infected leaf area, enabling the system to determine the severity level of infection. The system also collects environmental parameters such as temperature, humidity, soil moisture, and light intensity using IoT sensors. These parameters, along with disease diagnosis results, are transmitted to a cloud-based dashboard for real-time monitoring and analysis. Automated alerts can be sent to farmers through SMS or mobile notifications when severe infections are detected. Experimental evaluation is expected to achieve disease classification accuracy above 93%, infection area estimation error below 5%, and real-time inference within one second per image on the Raspberry Pi platform. The proposed solution offers a cost-effective, portable, and scalable approach for precision agriculture by combining artificial intelligence, image processing, and IoT technologies. The system enables early disease detection, accurate infection assessment, timely treatment recommendations, reduced pesticide usage, and improved cotton crop productivity, making it a valuable tool for smart farming applications.","棉花是全球最重要的经济作物之一，其产量受到多种叶部病害的显著影响，如细菌性枯萎病（bacterial blight）、链格孢叶斑病（alternaria leaf spot）、尾孢叶斑病（Cercospora leaf spot）、白粉病（powdery mildew）和卷叶病（leaf curl disease）。早期准确识别这些病害对于减少作物损失和提高产量至关重要。传统的病害诊断依赖专家进行人工田间巡检，这种方式耗时、费力，且对于大面积农田往往不切实际。所提出的系统集成了Raspberry Pi5、高分辨率摄像头模块、环境传感器和无线通信技术，构建了一个智能作物监测平台。利用Raspberry Pi摄像头采集棉花叶片图像，并通过计算机视觉技术进行叶片分割和特征提取。采用基于MobileNetV2的轻量级深度学习模型自动识别不同的棉花叶部病害。除病害分类外，还利用图像分割技术计算受感染叶面积百分比，使系统能够确定感染的严重程度。该系统还通过物联网传感器采集温度、湿度、土壤水分和光照强度等环境参数。这些参数连同病害诊断结果一起传输至基于云端的仪表板，用于实时监测和分析。当检测到严重感染时，可通过短信或移动通知向农民发送自动警报。实验评估预期在Raspberry Pi平台上实现高于93%的病害分类准确率、低于5%的感染面积估计误差，以及每张图像一秒以内的实时推理。所提出的解决方案结合了人工智能、图像处理和物联网技术，为精准农业提供了一种经济高效、便携且可扩展的方法。该系统能够实现病害早期检测、准确感染评估、及时治疗建议、减少农药使用并提高棉花作物产量，使其成为智慧农业应用中的一种有价值的工具。","2026-10-10T00:00:00Z",{"impact":334,"substance":403,"depth":404,"authority":156,"freshness":211,"relevant":21,"comment":405},20,17,"基于树莓派与轻量深度学习的棉花叶病检测与物联网监测方案，方法具体、指标明确，对智慧植保有参考价值。",[407],{"name":130,"url":398},[26,27,29,78,409],"棉花病害",{"code":411,"slug":412,"name":413,"path":414,"parent_code":415},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[417,418],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":420},{"candidates":421,"agricultural_evidence":51},[422,426,433,437,441,448],{"code":32,"score":19,"supported":51,"title":423,"abstract":424,"tags":425},[55],[29,55],[29],{"code":411,"score":427,"supported":51,"title":428,"abstract":430,"tags":432},14,[429],"cotton",[431,429],"棉花",[],{"code":75,"score":50,"supported":51,"title":434,"abstract":435,"tags":436},[],[26,78,79],[26,78],{"code":39,"score":13,"supported":51,"title":438,"abstract":439,"tags":440},[],[60,61,62,233,63],[27],{"code":442,"score":20,"supported":51,"title":443,"abstract":444,"tags":447},"F4",[],[445,446],"农药","pesticide",[],{"code":82,"score":20,"supported":51,"title":449,"abstract":450,"tags":451},[],[85,86],[],[453,454],"Raspberry Pi 棉花病害检测","MobileNetV2 棉花叶病识别","RaspberryPi棉花病害检测-4660","10.5281\u002Fzenodo.23280692",{"doi":456,"openalex_id":458,"authors":459,"venue":130,"cited_by_count":103,"oa_url":398,"card":465,"direction":108,"ingested_from":110},"W7221340722",[460,462],{"name":461,"orcid":9},"Deepak R Khadse",{"name":463,"orcid":464},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":466,"method":467,"finding":468,"direction":108,"opportunity":469},"基于树莓派与物联网，实现棉花叶病自动检测、感染率估算与防治推荐。","树莓派5+摄像头+环境传感器，MobileNetV2分类与图像分割，云端看板。","预期分类准确率超93%，感染面积误差低于5%，单图推理1秒内。","可探索边缘端轻量模型与多病害严重度分级、精准施药决策的闭环验证。","2026-10-11T23:30:07.876353Z",{"id":472,"title":473,"url":474,"summary":475,"summary_zh":476,"content":9,"source_name":477,"source_url":474,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":478,"score_detail":479,"sources":481,"tags":483,"primary_topic":486,"secondary_topics":487,"classification":489,"search_phrases":515,"slug":518,"view_count":103,"doi":519,"paper":520,"created_at":528},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",52,{"impact":17,"substance":334,"depth":427,"authority":20,"freshness":17,"relevant":21,"comment":480},"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。",[482],{"name":477,"url":474},[26,27,29,484,485],"作物健康监测","资源优化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[488],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":490},{"candidates":491,"agricultural_evidence":51},[492,497,502,506,511],{"code":75,"score":18,"supported":51,"title":493,"abstract":495,"tags":496},[494],"smart agriculture",[26,78,494],[26],{"code":32,"score":18,"supported":51,"title":498,"abstract":499,"tags":501},[55],[29,500,55],"农业传感",[29],{"code":39,"score":19,"supported":51,"title":503,"abstract":504,"tags":505},[162],[232,162],[27],{"code":166,"score":20,"supported":51,"title":507,"abstract":508,"tags":510},[],[509,169],"粮食安全",[],{"code":82,"score":20,"supported":51,"title":512,"abstract":513,"tags":514},[],[85,86],[],[516,517],"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":519,"openalex_id":521,"authors":522,"venue":477,"cited_by_count":103,"oa_url":474,"card":523,"direction":108,"ingested_from":110},"W7221334384",[],{"tldr":524,"method":525,"finding":526,"direction":108,"opportunity":527},"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":530,"title":531,"url":532,"summary":533,"summary_zh":534,"content":9,"source_name":535,"source_url":532,"published_at":536,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":537,"sources":539,"tags":541,"primary_topic":544,"secondary_topics":545,"classification":547,"search_phrases":578,"slug":581,"view_count":103,"doi":582,"paper":583,"created_at":615},4489,"Artificial Intelligence and Internet of Things in Smart Agriculture: A Bibliometric and Thematic Review of Data Infrastructures and Sustainability Implications","https:\u002F\u002Fdoi.org\u002F10.54175\u002Fhsustain5040036","Artificial intelligence (AI) is reshaping smart agriculture through prediction, automation, resource optimization, and data-driven decision-making. However, its effective and sustainable deployment depends on reliable Internet of Things (IoT) data infrastructures supporting sensing, connectivity, data acquisition, transmission, and distributed processing. This review maps research at the intersection of AI, IoT, and smart agriculture and examines its data-infrastructure requirements and sustainability implications. Drawing on 441 Scopus-indexed, peer-reviewed journal articles published between 2017 and 2025, the study combines bibliometric performance analysis, science mapping, and content-based thematic interpretation to identify publication trends, intellectual foundations, influential research fronts, emerging themes, and deployment challenges. The bibliometric analyses characterize AI–IoT research in smart agriculture, while IoT data infrastructure provides the principal analytical lens, and sustainability and responsible deployment are examined through interpretive synthesis. The findings reveal rapid publication growth and prominent themes involving connected sensing, predictive analytics, edge intelligence, remote sensing, smart irrigation, cybersecurity, blockchain, and edge–cloud processing. Persistent barriers include limited interoperability, inconsistent data quality, energy requirements, scalability constraints, cybersecurity and governance risks, affordability, and unequal adoption in resource-constrained contexts. The review concludes that sustainable AI-enabled agriculture requires reliable, secure, interoperable, energy-efficient, inclusive, and context-appropriate digital infrastructures.","人工智能（AI）正通过预测、自动化、资源优化和数据驱动决策重塑智慧农业。然而，其有效且可持续的部署依赖于可靠的物联网（IoT）数据基础设施，以支持感知、连接、数据采集、传输和分布式处理。本综述梳理了人工智能、物联网与智慧农业交叉领域的研究，并考察其数据基础设施需求及可持续性影响。基于2017年至2025年间发表的441篇Scopus索引同行评审期刊论文，本研究结合文献计量绩效分析、科学知识图谱和基于内容的主题解读，以识别发文趋势、知识基础、有影响力的研究前沿、新兴主题及部署挑战。文献计量分析刻画了智慧农业中人工智能—物联网研究的特征，物联网数据基础设施则作为主要分析视角，可持续性与负责任部署则通过解释性综合加以考察。研究发现发文量快速增长，突出主题涉及互联感知、预测分析、边缘智能、遥感、智能灌溉、网络安全、区块链及边云处理。持续存在的障碍包括互操作性有限、数据质量不一致、能源需求、可扩展性约束、网络安全与治理风险、可负担性以及在资源受限情境中的不均衡采用。本综述得出结论：可持续的人工智能赋能农业需要可靠、安全、可互操作、节能、包容且情境适宜的数字基础设施。","Highlights of Sustainability","2026-10-08T00:00:00Z",{"impact":18,"substance":123,"depth":18,"authority":124,"freshness":17,"relevant":21,"comment":538},"基于441篇文献的AI-IoT智慧农业文献计量综述，系统梳理数据基础设施与可持续部署瓶颈，学术参考价值较高。",[540],{"name":535,"url":532},[26,27,542,28,543],"农业物联网","数据基础设施",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[546],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":548},{"candidates":549,"agricultural_evidence":51},[550,555,559,566,570,574],{"code":32,"score":551,"supported":51,"title":552,"abstract":553,"tags":554},19,[54],[29,54,55],[542,28],{"code":75,"score":19,"supported":51,"title":556,"abstract":557,"tags":558},[494],[26,494],[26],{"code":560,"score":427,"supported":51,"title":561,"abstract":563,"tags":565},"I7",[562],"bibliometric",[564,562],"文献计量",[],{"code":39,"score":156,"supported":51,"title":567,"abstract":568,"tags":569},[233],[233],[27],{"code":67,"score":20,"supported":51,"title":571,"abstract":572,"tags":573},[],[71,72],[],{"code":246,"score":20,"supported":51,"title":575,"abstract":576,"tags":577},[],[216,249],[],[579,580],"AI IoT 智慧农业 文献计量","农业物联网 数据基础设施","AIIoT智慧农业文献计量-4489","10.54175\u002Fhsustain5040036",{"doi":582,"openalex_id":584,"authors":585,"venue":535,"cited_by_count":103,"oa_url":609,"card":610,"direction":108,"ingested_from":110},"W7220924310",[586,589,592,594,597,600,603,606],{"name":587,"orcid":588},"Altyeb Ali Abaker Omer","https:\u002F\u002Forcid.org\u002F0000-0001-9420-9910",{"name":590,"orcid":591},"Yajie Dong","https:\u002F\u002Forcid.org\u002F0009-0001-2843-6135",{"name":593,"orcid":9},"Ibrahim Issa Mohamed Issa",{"name":595,"orcid":596},"Yousif Yahia Ahmed Abuker","https:\u002F\u002Forcid.org\u002F0009-0002-8111-9283",{"name":598,"orcid":599},"Mohamed Ibrahim Abdallh Babeker","https:\u002F\u002Forcid.org\u002F0009-0008-0904-9501",{"name":601,"orcid":602},"Alfadil Aboud Alfadil Hassan","https:\u002F\u002Forcid.org\u002F0009-0005-0656-1903",{"name":604,"orcid":605},"Zhiguo Shan","https:\u002F\u002Forcid.org\u002F0000-0002-6165-8258",{"name":607,"orcid":608},"Yinxue Shi","https:\u002F\u002Forcid.org\u002F0009-0002-1465-8666","https:\u002F\u002Fwww.hos.pub\u002Farticles\u002Fhsustain5040036\u002Fpdf",{"tldr":611,"method":612,"finding":613,"direction":108,"opportunity":614},"综述2017-2025年441篇文献，梳理AI与IoT在智慧农业中的数据基础设施与可持续性。","Scopus文献计量、科学映射与主题内容分析，以IoT数据基础设施为分析视角。","研究快速增长，但互操作性、数据质量、能耗、安全与公平采用仍是主要障碍。","可针对资源受限小农户场景，研究低能耗、可互操作且包容的农业IoT数据基础设施与治理机制。","2026-10-09T23:30:07.811940Z"]