[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5553":3,"related-5553":144},{"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":49,"search_phrases":116,"slug":119,"view_count":120,"doi":121,"paper":122,"created_at":143},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。",null,"ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","物联网","精准农业","气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":56},[55,67,74,81,87,95,103,110],{"code":32,"score":17,"supported":56,"title":57,"abstract":59,"tags":66},true,[58],"artificial intelligence",[60,61,62,63,64,65],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[27],{"code":68,"score":69,"supported":56,"title":70,"abstract":72,"tags":73},"I1",16,[71],"precision agriculture",[29,71],[26,29],{"code":45,"score":13,"supported":56,"title":75,"abstract":76,"tags":80},[],[28,77,78,79],"边缘计算","IoT","edge computing",[28],{"code":39,"score":20,"supported":56,"title":82,"abstract":83,"tags":86},[],[84,85,40],"灌溉","水资源",[],{"code":88,"score":89,"supported":56,"title":90,"abstract":91,"tags":94},"H7",7,[],[92,93],"气候变化","climate change",[30],{"code":96,"score":97,"supported":56,"title":98,"abstract":99,"tags":102},"H2",6,[],[100,101],"土壤","soil",[],{"code":104,"score":105,"supported":56,"title":106,"abstract":107,"tags":109},"A7",3,[],[108],"间作",[],{"code":111,"score":105,"supported":56,"title":112,"abstract":113,"tags":115},"F3",[],[114],"养分管理",[],[117,118],"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",0,"10.53982\u002Fajerd.2026.01.14-j",{"doi":121,"openalex_id":123,"authors":124,"venue":10,"cited_by_count":120,"oa_url":6,"card":135,"direction":141,"ingested_from":142},"W7223121456",[125,128,130,132],{"name":126,"orcid":127},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":129,"orcid":9},"Oluwadare OYEBODE",{"name":131,"orcid":9},"Iretiayo Adelaiye",{"name":133,"orcid":134},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":136,"method":137,"finding":138,"direction":139,"opportunity":140},"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。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:18.151376Z",{"total":97,"page":21,"page_size":97,"items":145},[146,226,305,410,480,556],{"id":147,"title":148,"url":149,"summary":150,"summary_zh":151,"content":9,"source_name":152,"source_url":149,"published_at":153,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":154,"score_detail":155,"sources":160,"tags":162,"primary_topic":164,"secondary_topics":165,"classification":168,"search_phrases":202,"slug":205,"view_count":206,"doi":207,"paper":208,"created_at":225},2926,"An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data","https:\u002F\u002Fdoi.org\u002F10.11591\u002Fijece.v16i5.pp2652-2663","Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON\u002FOFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.","高效的水资源管理对可持续农业生产至关重要，尤其是在水资源有限的干旱和半干旱地区。基于机器学习的灌溉系统可以利用环境和土壤相关传感器数据支持自动化水泵运行决策。然而，以往大多数研究集中于单一机器学习模型，而堆叠集成（stacked ensemble）在二元灌溉水泵状态预测中的应用仍相对有限。本研究提出了一种基于堆叠（stacking）的框架，用于在开\u002F关（ON\u002FOFF）分类场景下预测灌溉水泵运行状态。该框架使用环境和土壤相关变量，包括土壤湿度、温度、湿度和一个数值型时间相关特征。随机森林（RF）、极端梯度提升（XGBoost）和多层感知机模型被训练为基学习器，其折外（OOF）预测结果通过逻辑回归元学习器进行组合。模型在留出测试集上使用准确率、精确率、召回率、特异度和F1分数进行评估。单一模型的准确率范围为97.34%至99.95%，其中XGBoost的单一模型性能最佳。所提出的堆叠集成达到了99.97%的准确率、99.94%的精确率、100.00%的召回率、99.94%的特异度和99.97%的F1分数。与XGBoost相比，该集成进一步优化了预测性能，准确率提高了0.02个百分点，F1分数提高了0.01个百分点，同时实现了假阴性的完全消除（100.00%召回率）。这些结果表明，堆叠集成学习在改进二元水泵运行预测和支持水资源受限环境下的数据驱动灌溉管理方面具有潜力。","International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z",72,{"impact":156,"substance":157,"depth":158,"authority":19,"freshness":20,"relevant":21,"comment":159},12,21,17,"提出基于IoT传感器数据的堆叠集成模型实现灌溉水泵开关预测，准确率达99.97%，方法新颖、结论可靠，但属实验室验证阶段，产业影响有限。",[161],{"name":152,"url":149},[26,27,61,28,163],"智能灌溉",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[166,167],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":169},{"candidates":170,"agricultural_evidence":56},[171,175,179,183,187,192,198],{"code":39,"score":17,"supported":56,"title":172,"abstract":173,"tags":174},[40],[84,85,40],[163],{"code":32,"score":19,"supported":56,"title":176,"abstract":177,"tags":178},[58],[61],[27,61],{"code":45,"score":20,"supported":56,"title":180,"abstract":181,"tags":182},[78],[],[28],{"code":96,"score":97,"supported":56,"title":184,"abstract":185,"tags":186},[],[100,101],[],{"code":88,"score":105,"supported":56,"title":188,"abstract":189,"tags":191},[],[190],"干旱",[],{"code":193,"score":105,"supported":56,"title":194,"abstract":195,"tags":197},"H8",[],[196],"可持续农业",[],{"code":68,"score":21,"supported":14,"title":199,"abstract":200,"tags":201},[],[],[26],[203,204],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926",2,"10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":207,"openalex_id":209,"authors":210,"venue":152,"cited_by_count":120,"oa_url":149,"card":220,"direction":141,"ingested_from":142},"W7213546497",[211,214,217],{"name":212,"orcid":213},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":215,"orcid":216},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":218,"orcid":219},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":221,"method":222,"finding":223,"direction":141,"opportunity":224},"提出基于堆叠集成学习的灌溉泵ON\u002FOFF预测框架，用物联网传感器数据实现智能灌溉控制。","用RF、XGBoost、MLP作基学习器，逻辑回归元学习器融合OOF预测，基于土","堆叠集成达99.97%准确率和100%召回率，完全消除假阴性，优于单一XGBoost模型。","可探索多模态传感器融合与在线增量学习，提升堆叠集成在不同作物和气候区的泛化与实时部署能力。","2026-09-19T23:30:11.009076Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":9,"content":9,"source_name":231,"source_url":229,"published_at":232,"category":12,"cover_url":9,"hotness":233,"is_selected":14,"score":234,"score_detail":235,"sources":237,"tags":247,"primary_topic":248,"secondary_topics":249,"classification":252,"search_phrases":283,"slug":286,"view_count":287,"doi":288,"paper":289,"created_at":304},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",55,74,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":105,"relevant":21,"comment":236},"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。",[238,239,242,244],{"name":231,"url":229},{"name":240,"url":241},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":240,"url":243},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":245,"url":246},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[26,27,77,28,163],{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},[250,251],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":253},{"candidates":254,"agricultural_evidence":56},[255,261,265,269,275,279],{"code":45,"score":256,"supported":56,"title":257,"abstract":258,"tags":260},27,[78,79],[259,78,79],"internet of things",[28,77],{"code":39,"score":156,"supported":56,"title":262,"abstract":263,"tags":264},[40],[40],[163],{"code":32,"score":156,"supported":56,"title":266,"abstract":267,"tags":268},[63],[63],[27],{"code":270,"score":105,"supported":56,"title":271,"abstract":272,"tags":274},"A8",[],[273],"food security",[],{"code":96,"score":105,"supported":56,"title":276,"abstract":277,"tags":278},[],[101],[],{"code":68,"score":21,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[26],[284,285],"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":288,"openalex_id":290,"authors":291,"venue":231,"cited_by_count":120,"oa_url":298,"card":299,"direction":141,"ingested_from":142},"W7204189894",[292,295],{"name":293,"orcid":294},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":296,"orcid":297},"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":300,"method":301,"finding":302,"direction":141,"opportunity":303},"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":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":9,"source_name":311,"source_url":308,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":312,"score_detail":313,"sources":315,"tags":317,"primary_topic":321,"secondary_topics":322,"classification":325,"search_phrases":380,"slug":383,"view_count":120,"doi":384,"paper":385,"created_at":409},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",79,{"impact":17,"substance":18,"depth":17,"authority":156,"freshness":20,"relevant":21,"comment":314},"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。",[316],{"name":311,"url":308},[26,27,318,319,320],"遥感","作物水分胁迫","精准灌溉",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[323,324],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":326},{"candidates":327,"agricultural_evidence":56},[328,333,337,341,347,353,360,367,374],{"code":39,"score":329,"supported":56,"title":330,"abstract":331,"tags":332},15,[40],[84,40],[320],{"code":32,"score":13,"supported":56,"title":334,"abstract":335,"tags":336},[],[61,63,58],[27],{"code":45,"score":20,"supported":56,"title":338,"abstract":339,"tags":340},[],[28,259,78],[],{"code":68,"score":89,"supported":56,"title":342,"abstract":343,"tags":346},[],[344,345],"数字孪生","digital twin",[26],{"code":348,"score":89,"supported":56,"title":349,"abstract":350,"tags":352},"I3",[],[318,351],"remote sensing",[318],{"code":354,"score":97,"supported":56,"title":355,"abstract":356,"tags":359},"A2",[],[357,358],"小麦","wheat",[],{"code":361,"score":97,"supported":56,"title":362,"abstract":363,"tags":366},"A3",[],[364,365],"玉米","maize",[],{"code":368,"score":97,"supported":56,"title":369,"abstract":370,"tags":373},"A5",[],[371,372],"马铃薯","potato",[],{"code":375,"score":105,"supported":56,"title":376,"abstract":377,"tags":379},"B2",[],[378],"葡萄",[],[381,382],"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":384,"openalex_id":386,"authors":387,"venue":311,"cited_by_count":120,"oa_url":308,"card":403,"direction":141,"ingested_from":142},"W7223088921",[388,391,394,397,400],{"name":389,"orcid":390},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":392,"orcid":393},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":395,"orcid":396},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":398,"orcid":399},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":401,"orcid":402},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":404,"method":405,"finding":406,"direction":407,"opportunity":408},"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":411,"title":412,"url":413,"summary":414,"summary_zh":415,"content":9,"source_name":416,"source_url":413,"published_at":417,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":418,"score_detail":419,"sources":421,"tags":423,"primary_topic":426,"secondary_topics":427,"classification":430,"search_phrases":461,"slug":464,"view_count":287,"doi":9,"paper":465,"created_at":479},4076,"Automated System for Plant Health Monitoring and Stress Diagnosis Using IoT and Machine Learning","https:\u002F\u002Fwajes.isroset.org\u002Findex.php\u002Fj\u002Farticle\u002Fview\u002F208","Irrigation decisions on smallholder farms are still taken subjectively, and both root-zone dehydration and over-saturation measurably depress yield, making low-cost automated plant-stress diagnosis an urgent requirement in water-scarce agricultural regions. This study designs, implements and validates a decentralized system that closes the control loop between field sensing and irrigation actuation by combining Internet of Things (IoT) hardware with a cloud-hosted predictive Machine Learning (ML) model. The edge node is built on an ESP32 system-on-chip integrated with a calibrated capacitive soil-moisture probe and a DHT22 temperature and humidity array. Serialized telemetry is transmitted over a non-blocking, encrypted wireless channel to a Flask web service, where a 150-tree Random Forest classifier trained on 5,000 labelled multi-sensor records assigns the plant to one of three physiological states Optimal, Temperature Stress or Dehydration Stress and returns an actuator control bit that drives a micro-irrigation pump. An engineered Evapotranspiration Proxy Index couples ambient temperature and relative humidity into a single transpiration-sensitive feature. Capacitive calibration was validated against the gravimetric standard on ten soil cores, yielding a Pearson correlation coefficient of 0.948. Over a continuous forty-eight-hour open-air evaluation the classifier achieved an overall accuracy of 96.8%, with 98.2% precision and 97.9% recall on the critical Dehydration Stress class, and a mean end-to-end round-trip latency of 1.84 seconds. Deep-sleep duty cycling reduced the mean node current to 0.65 mA, corresponding to an estimated 159 days of unattended operation on a single 2,500 mAh cell. The results demonstrate that a low-cost edge–cloud architecture can deliver dependable, timely and energy-sustainable irrigation control for micro-precision agriculture without server-grade hardware.","小农户的灌溉决策仍依赖主观判断，而根区脱水和过度饱和均会显著降低产量，这使得低成本自动化植物胁迫诊断成为缺水农业地区的迫切需求。本研究设计、实现并验证了一种去中心化系统，通过将物联网（IoT）硬件与云端预测机器学习（ML）模型相结合，闭合了田间传感与灌溉执行之间的控制回路。边缘节点基于ESP32片上系统构建，集成了经过校准的电容式土壤水分探头和DHT22温湿度传感器阵列。序列化遥测数据通过非阻塞加密无线信道传输至Flask Web服务，在此处，一个基于5,000条带标签多传感器记录训练的150棵树的随机森林分类器将植物判定为三种生理状态之一——最优、温度胁迫或脱水胁迫——并返回驱动微灌泵的执行器控制位。所设计的蒸散代理指数将环境温度和相对湿度耦合为单一蒸腾敏感特征。电容校准在十个土芯上与重量法标准进行验证，得到皮尔逊相关系数为0.948。在连续四十八小时的户外评估中，分类器总体准确率达到96.8%，在关键的脱水胁迫类别上精确率为98.2%、召回率为97.9%，平均端到端往返延迟为1.84秒。深度睡眠占空比控制将节点平均电流降至0.65 mA，相当于在单节2,500 mAh电池上估计可无人值守运行159天。结果表明，低成本边缘—云架构无需服务器级硬件即可为微精准农业提供可靠、及时且能源可持续的灌溉控制。","World Academics Journal of Engineering Sciences","2026-09-30T00:00:00Z",75,{"impact":69,"substance":18,"depth":17,"authority":19,"freshness":97,"relevant":21,"comment":420},"低成本边缘-云架构实现植物胁迫自动诊断与灌溉闭环，数据扎实、结论可靠，对智慧农业节水灌溉有参考价值。",[422],{"name":416,"url":413},[26,27,424,320,425],"农业物联网","土壤墒情",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},[428,429],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":431},{"candidates":432,"agricultural_evidence":56},[433,437,441,446,450,454],{"code":45,"score":17,"supported":56,"title":434,"abstract":435,"tags":436},[78],[28,259,78],[424],{"code":32,"score":329,"supported":56,"title":438,"abstract":439,"tags":440},[63],[61,63],[27],{"code":39,"score":13,"supported":56,"title":442,"abstract":443,"tags":445},[],[84,40,444],"evapotranspiration",[320],{"code":96,"score":89,"supported":56,"title":447,"abstract":448,"tags":449},[],[100,101],[425],{"code":68,"score":89,"supported":56,"title":451,"abstract":452,"tags":453},[],[29,71],[26],{"code":455,"score":97,"supported":56,"title":456,"abstract":457,"tags":460},"G2",[],[458,459],"小农户","smallholder",[],[462,463],"ESP32 土壤湿度 灌溉 机器学习","随机森林 植物胁迫 诊断","ESP32土壤湿度灌溉机器学习-4076",{"doi":9,"openalex_id":466,"authors":467,"venue":416,"cited_by_count":120,"oa_url":413,"card":474,"direction":141,"ingested_from":142},"W7217836036",[468,470,472],{"name":469,"orcid":9},"Christian Athanas Mwakyusa",{"name":471,"orcid":9},"Allen Deogratius Mwalekwa",{"name":473,"orcid":9},"Gift Geofrey Mushi",{"tldr":475,"method":476,"finding":477,"direction":141,"opportunity":478},"设计并验证了基于ESP32与随机森林的物联网闭环灌溉系统，实现植物胁迫自动诊断。","ESP32、电容式土壤湿度传感器、DHT22、随机森林分类器、Flask云服务。","48小时测试分类准确率96.8%，脱水胁迫召回率97.9%，节点续航约159天。","可探索多作物泛化、极端环境鲁棒性及边缘端轻量化模型部署以降低云依赖。","2026-10-03T23:30:18.857006Z",{"id":481,"title":482,"url":483,"summary":484,"summary_zh":485,"content":9,"source_name":240,"source_url":483,"published_at":486,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":487,"sources":490,"tags":492,"primary_topic":494,"secondary_topics":500,"classification":503,"search_phrases":537,"slug":540,"view_count":21,"doi":541,"paper":542,"created_at":555},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":69,"substance":488,"depth":158,"authority":156,"freshness":20,"relevant":21,"comment":489},20,"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。",[491],{"name":240,"url":483},[26,27,28,29,493],"棉花病害",{"code":495,"slug":496,"name":497,"path":498,"parent_code":499},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[501,502],{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":504},{"candidates":505,"agricultural_evidence":56},[506,510,517,522,526,533],{"code":45,"score":329,"supported":56,"title":507,"abstract":508,"tags":509},[78],[28,78],[28],{"code":495,"score":511,"supported":56,"title":512,"abstract":514,"tags":516},14,[513],"cotton",[515,513],"棉花",[],{"code":68,"score":518,"supported":56,"title":519,"abstract":520,"tags":521},11,[],[26,29,71],[26,29],{"code":32,"score":13,"supported":56,"title":523,"abstract":524,"tags":525},[],[60,62,64,58,65],[27],{"code":527,"score":97,"supported":56,"title":528,"abstract":529,"tags":532},"F4",[],[530,531],"农药","pesticide",[],{"code":96,"score":97,"supported":56,"title":534,"abstract":535,"tags":536},[],[100,101],[],[538,539],"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":541,"openalex_id":543,"authors":544,"venue":240,"cited_by_count":120,"oa_url":483,"card":550,"direction":141,"ingested_from":142},"W7221340722",[545,547],{"name":546,"orcid":9},"Deepak R Khadse",{"name":548,"orcid":549},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":551,"method":552,"finding":553,"direction":141,"opportunity":554},"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":557,"title":558,"url":559,"summary":560,"summary_zh":561,"content":9,"source_name":562,"source_url":559,"published_at":563,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":564,"score_detail":565,"sources":567,"tags":569,"primary_topic":570,"secondary_topics":571,"classification":577,"search_phrases":597,"slug":600,"view_count":206,"doi":601,"paper":602,"created_at":614},4058,"Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100535","Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental intelligence. Conventional monitoring systems, which often rely on periodic measurements and isolated analytical tools, are no longer sufficient for managing highly dynamic environmental systems. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), edge computing, and Digital Twins provide unprecedented opportunities to transform sustainability management from reactive monitoring into intelligent, predictive, and autonomous decision-making. IoT enables continuous environmental sensing, AI extracts knowledge from large heterogeneous datasets, edge intelligence supports low-latency analytics, while Digital Twins create dynamic virtual representations of physical systems capable of simulation and optimization. This critical integrative review examines recent developments in AI-enabled sustainability across smart energy systems, intelligent water management, precision agriculture, transportation, smart buildings, circular economy, and climate resilience. Rather than reviewing these technologies independently, the paper analyzes their interactions within an integrated digital ecosystem and discusses emerging topics including Green AI, carbon-aware computing, explainable AI, federated learning, cybersecurity, and sustainability governance. The review method combines a structured multi-database search with targeted evidence and standards updates through June 2026. Building upon the reviewed literature, the paper introduces the Sustainability Intelligence Framework (SIF), a conceptual seven-layer architecture integrating IoT sensing, edge intelligence, AI analytics, Digital Twins, decision intelligence, and sustainability assessment within a continuous feedback cycle. The proposed framework provides a systems-level perspective for designing adaptive, resilient, and resource-efficient infrastructures capable of supporting future sustainable development. The synthesis identifies continuous, auditable sustainability assessment (Layer 7), rather than the underlying six-layer IoT\u002Fcyber–physical stack, as the framework’s distinct contribution. It also shows that reported benefits remain difficult to compare because field validation, system boundaries, rebound effects, and lifecycle digital burdens are inconsistently reported.","可持续性已成为二十一世纪具有决定性意义的科学与社会挑战之一。气候变化、快速城市化、人口增长、能源需求上升以及自然资源减少，要求决策必须持续、自适应，并由可靠的环境智能提供支撑。传统监测系统往往依赖周期性测量和孤立分析工具，已不足以管理高度动态的环境系统。人工智能（AI）、物联网（IoT）、边缘计算和数字孪生（Digital Twins）的最新进展，为将可持续性管理从被动监测转变为智能、预测性和自主决策提供了前所未有的机遇。物联网能够实现持续环境感知，人工智能可从大规模异构数据集中提取知识，边缘智能支持低时延分析，而数字孪生则创建物理系统的动态虚拟表示，以支持仿真与优化。这篇批判性综合综述考察了AI赋能可持续性在智能能源系统、智能水管理、精准农业、交通、智能建筑、循环经济和气候韧性等领域的最新进展。本文并未孤立地评述这些技术，而是分析它们在集成数字生态系统中的相互作用，并讨论绿色AI、碳感知计算、可解释AI、联邦学习、网络安全和可持续性治理等新兴议题。综述方法结合了结构化多数据库检索，并通过截至2026年6月的针对性证据与标准更新加以补充。在所评述文献的基础上，本文提出可持续性智能框架（Sustainability Intelligence Framework, SIF），这是一个概念性七层架构，在持续反馈循环中整合物联网感知、边缘智能、AI分析、数字孪生、决策智能和可持续性评估。所提出的框架为设计能够支持未来可持续发展的自适应、韧性和资源高效型基础设施提供了系统层面视角。该综合指出，框架的独特贡献在于持续、可审计的可持续性评估（第7层），而非底层六层物联网\u002F网络—物理技术栈。研究还表明，由于现场验证、","Next Sustainability","2026-10-02T00:00:00Z",78,{"impact":17,"substance":488,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":566},"该综述提出七层可持续智能框架，系统整合AI、物联网与数字孪生，对智慧农业与气候韧性基础设施具有较高参考价值，但偏概念性、缺乏实证数据。",[568],{"name":562,"url":559},[26,27,28,344,29],{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},[572,573],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":88,"slug":574,"name":575,"path":576,"parent_code":43},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":50,"locked":14,"version":51,"method":52,"evidence":578},{"candidates":579,"agricultural_evidence":56},[580,584,588,592],{"code":45,"score":17,"supported":56,"title":581,"abstract":582,"tags":583},[259],[28,77,259,78,79],[28],{"code":68,"score":156,"supported":56,"title":585,"abstract":586,"tags":587},[],[29,344,71],[344,26,29],{"code":32,"score":156,"supported":56,"title":589,"abstract":590,"tags":591},[58],[58],[27],{"code":88,"score":20,"supported":56,"title":593,"abstract":594,"tags":596},[],[92,93,595],"climate resilience",[],[598,599],"农业人工智能 数字孪生 智慧农业 精准农业","农业人工智能 数字孪生","农业人工智能数字孪生智慧农业精准农业-4058","10.1016\u002Fj.nxsust.2026.100535",{"doi":601,"openalex_id":603,"authors":604,"venue":562,"cited_by_count":120,"oa_url":559,"card":608,"direction":141,"ingested_from":142},"W7216004668",[605],{"name":606,"orcid":607},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":609,"method":610,"finding":611,"direction":612,"opportunity":613},"综述AI、物联网与数字孪生融合的可持续性智能，提出七层SIF框架。","多数据库结构化检索，整合IoT、边缘计算、AI与数字孪生文献。","持续可审计的可持续性评估是框架核心贡献，但实地验证仍难比较。","其他","农业场景下SIF七层框架的田间验证与可审计碳\u002F资源评估方法尚属空白。","2026-10-03T23:30:09.950820Z"]