[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1877":3,"related-1877":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":61,"slug":64,"view_count":65,"doi":8,"paper":66,"created_at":76},1877,"IoT-Enabled Evidence-Fusing Graph Neural Network With Attention-Based Feature Engineering For Uncertainty-Aware Crop Recommendation（IJAIML Vol.6 No.9s 1668-1689）","https:\u002F\u002Fwww.svedbergopen.com\u002Findex.php\u002Fijaiml\u002Farticle\u002Fview\u002F1623","Kanuprasad与Balaji提出IoT-AM-EFGNN框架，结合IoT传感、AMTNet启发的多尺度注意力特征工程、Evidence Fusing Graph Neural Network (EFGNN)、酶作用优化器(EAO)和SHapley Additive exPlanations (SHAP)；实验准确率达99.18%，精度99.12%，召回率99.08%，F1-score 99.10%，ROC-AUC 99.42%；交叉验证平均99.18%±0.16%。",null,"International Journal of Artificial Intelligence and Machine Learning 2026, 6(9s): 1668-1689","2026-09-05T00:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,12,7,1,"提出IoT-AM-EFGNN框架，结合多尺度注意力与图神经网络，作物推荐准确率超99%，方法新颖且数据可靠，对精准农业有实质推进。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","精准农业","作物推荐","图神经网络",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,56],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},11,true,[47],"IoT",[47],[],{"code":51,"score":52,"supported":13,"title":53,"abstract":54,"tags":55},"I1",2,[],[],[25,27],{"code":57,"score":20,"supported":13,"title":58,"abstract":59,"tags":60},"I2",[],[],[26],[62,63],"农业人工智能 图神经网络 作物推荐 智慧农业","农业人工智能 图神经网络","农业人工智能图神经网络作物推荐智慧农业-1877",3,{"doi":8,"openalex_id":8,"authors":67,"venue":8,"cited_by_count":68,"oa_url":8,"card":69,"direction":73,"ingested_from":75},[],0,{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"提出IoT-AM-EFGNN框架，结合IoT传感与图神经网络实现高精度作物推荐。","IoT传感、多尺度注意力特征工程、证据融合图神经网络、酶作用优化器、SHAP解释","准确率99.18%，精度99.12%，召回率99.08%，F1 99.10%，AUC 99.42%。","农业人工智能与决策模型","可探索将证据融合图神经网络与不确定性量化结合，提升模型在数据稀缺或噪声环境下的鲁棒性。","agent","2026-09-08T00:07:15.686003Z",{"total":78,"page":20,"page_size":78,"items":79},6,[80,164,230,322,414,485],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":8,"source_name":86,"source_url":83,"published_at":87,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":89,"sources":95,"tags":97,"primary_topic":99,"secondary_topics":103,"classification":105,"search_phrases":144,"slug":147,"view_count":20,"doi":148,"paper":149,"created_at":163},3877,"Machine learning classification techniques for smart agriculture: a review of disease detection and crop recommendation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1878006","The adoption of Internet of Things (IoT) and Machine Learning (ML) in agriculture presents a revolutionary step toward efficient agricultural practices using data to address global issues surrounding food security. This review conducted a systematic search of the Scopus, IEEE Xplore, ScienceDirect, and SpringerLink databases for articles published between January 2023 and January 2026. It used keyword combinations like “IoT and machine learning and agriculture”, “crop recommendation”, “plant disease detection”, and “precision farming”. From the initial pool of articles found, 30 articles met the criteria for empirical methodology, quantitative performance reporting, and clear dataset descriptions. These were chosen for detailed analysis. It was discovered that the ensemble, as well as the traditional machine learning methods, such as Random Forest, Support Vector Machine (SVM), and Decision Trees, produce highly accurate predictions of the crop yields with an accuracy of 99.9%. Meanwhile, Convolutional Neural Networks (CNN) and ResNet-based deep learning models detect diseases with an accuracy above 98% while Gated Recurrent Units (GRU) and Long-Short Term Memory (LSTM) time-series models provide forecasts of weather conditions with an accuracy ranging from 94 to 98%, due to the complex nature of the environment dynamics. The application of IoT systems for precision agriculture produced a significant improvement in terms of resource utilization, resulting in 60–96% of water savings, 25% increased yield, and 36–38% reduced herbicide use. Although these advancements have been made, wider application is still hindered by high costs of implementation, lack of technological skills among farmers, data privacy concerns, issues of scalability within different agricultural settings, and consistent internet connection in rural areas. The current paper evaluates the use of IoT and ML in the agriculture sector from diverse data sources and studies to reveal the immense benefits offered by the two systems in enhancing farming activities.","物联网（IoT）与机器学习（ML）在农业中的应用标志着向高效农业实践迈出了革命性的一步，即利用数据来解决全球粮食安全相关问题。本综述对Scopus、IEEE Xplore、ScienceDirect和SpringerLink数据库中2023年1月至2026年1月间发表的文章进行了系统性检索，使用了“物联网与机器学习与农业”“作物推荐”“植物病害检测”和“精准农业”等关键词组合。从初步检索到的文章池中，30篇文章符合实证方法、定量性能报告和清晰数据集描述的标准，被选入进行详细分析。研究发现，集成方法以及随机森林、支持向量机（SVM）和决策树等传统机器学习方法对作物产量的预测精度高达99.9%。同时，卷积神经网络（CNN）和基于ResNet的深度学习模型检测病害的准确率超过98%，而门控循环单元（GRU）和长短期记忆（LSTM）时间序列模型由于环境动态的复杂性，对天气状况的预测准确率在94%至98%之间。物联网系统在精准农业中的应用在资源利用方面产生了显著改善，实现了60%至96%的节水、25%的产量提升以及36%至38%的除草剂用量减少。尽管取得了这些进展，更广泛的应用仍受到实施成本高昂、农民缺乏技术技能、数据隐私顾虑、不同农业场景下的可扩展性问题以及农村地区互联网连接不稳定等因素的制约。本文通过来自不同数据来源和研究，评估了物联网与机器学习在农业领域的应用，揭示了这两个系统在提升农业活动方面所带来的巨大效益。","Frontiers in Artificial Intelligence","2026-09-30T00:00:00Z",76,{"impact":16,"substance":90,"depth":91,"authority":92,"freshness":93,"relevant":20,"comment":94},20,17,13,8,"系统综述整合30篇实证研究，量化了IoT与机器学习在作物推荐、病害检测和精准灌溉上的性能与节水增产效益，对智慧农业技术选型有参考价值。",[96],{"name":86,"url":83},[25,26,98,27,28],"病虫害识别",{"code":57,"slug":100,"name":101,"path":102,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[104],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":106},{"candidates":107,"agricultural_evidence":45},[108,117,124,131,138],{"code":57,"score":109,"supported":45,"title":110,"abstract":112,"tags":116},19,[111],"machine learning",[113,114,111,115],"深度学习","机器学习","deep learning",[26,98],{"code":51,"score":118,"supported":45,"title":119,"abstract":121,"tags":123},16,[120],"smart agriculture",[27,122],"precision agriculture",[25,27],{"code":31,"score":125,"supported":45,"title":126,"abstract":127,"tags":130},9,[],[128,129,47],"物联网","internet of things",[],{"code":132,"score":78,"supported":45,"title":133,"abstract":134,"tags":137},"A8",[],[135,136],"粮食安全","food security",[],{"code":139,"score":65,"supported":45,"title":140,"abstract":141,"tags":143},"F4",[],[142],"除草",[],[145,146],"农业人工智能 病虫害识别 作物推荐 智慧农业","农业人工智能 病虫害识别","农业人工智能病虫害识别作物推荐智慧农业-3877","10.3389\u002Ffrai.2026.1878006",{"doi":148,"openalex_id":150,"authors":151,"venue":86,"cited_by_count":68,"oa_url":83,"card":156,"direction":161,"ingested_from":162},"W7214889993",[152,154],{"name":153,"orcid":8},"M. Umamaheswari",{"name":155,"orcid":8},"Kavitha D",{"tldr":157,"method":158,"finding":159,"direction":73,"opportunity":160},"综述2023-2026年30篇文献，评估物联网与机器学习在作物病害检测和推荐中的应用。","系统检索Scopus等四个数据库，筛选30篇实证研究进行性能与数据集分析。","集成学习与SVM等预测产量准确率达99.9%，CNN病害检测超98%，IoT节水60-96%。","可研究低成本、低连接依赖的边缘ML模型，解决小农户部署与数据隐私问题。","智慧农业 \u002F 农业物联网","openalex","2026-10-01T23:30:09.158196Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":8,"source_name":170,"source_url":167,"published_at":171,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":172,"score_detail":173,"sources":175,"tags":177,"primary_topic":179,"secondary_topics":180,"classification":188,"search_phrases":211,"slug":214,"view_count":52,"doi":215,"paper":216,"created_at":229},2509,"Artificial Intelligence-Based Crop Recommendation Using Soil and Climate Data: A Comprehensive Review of Machine Learning, Deep Learning, and Smart Agriculture Approaches","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722922","Crop recommendation systems that integrate soil and climate data with artificial intelligence (AI) have expanded rapidly since 2020, spanning classical machine learning (ML), ensemble methods, deep learning, explainable AI (XAI), and Internet of Things (IoT)-enabled sensing. This review critically synthesizes 35 sources — 33 peer-reviewed journal articles and conference papers plus 2 preprints retained only for background context — comprising 12 studies verified in full against their primary text and 23 studies verified at the bibliographic level, to examine what has been attempted, which data and algorithms have been used, and how reliable the reported results are. The reviewed literature shows convergent use of a narrow feature set (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and recurring near-ceiling accuracy, including cases at or above 98% [1], [5], [6] and, in one case, a reported 1.00 across accuracy, precision, recall, and F1-score following class-balancing [7]. Cross-examination of dataset descriptions across studies reveals inconsistent provenance and documentation: structurally similar seven-feature datasets are described with different national contexts and reported sample sizes ranging from 2,100 to 3,000 records [5], [6], [9]. Validation practice is dominated by random hold-out or k-fold cross-validation; among the studies examined in full, only one tested spatial cross-validation, reporting a substantial performance decline (AUC 0.89 to 0.55–0.62) relative to random-split results [4]. Explainable AI and uncertainty quantification remain minority practices in the reviewed literature. A prior conference proceedings paper self-described as a","自2020年以来，将土壤和气候数据与人工智能（AI）相结合的作物推荐系统迅速扩展，涵盖经典机器学习（ML）、集成方法、深度学习、可解释人工智能（XAI）以及物联网（IoT）赋能的传感技术。本综述批判性地综合了35个来源——33篇同行评审期刊论文和会议论文，以及2篇仅用于背景参考的预印本——其中包括12项经全文核实的研究和23项经书目层面核实的研究，以考察已尝试的研究方向、所使用的数据和算法，以及所报告结果的可靠性。所综述的文献显示，特征集使用趋同且范围狭窄（氮、磷、钾、温度、湿度、pH和降雨量），准确率反复接近上限，包括达到或超过98%的案例[1], [5], [6]，以及一例在类别平衡后准确率、精确率、召回率和F1分数均报告为1.00的研究[7]。对各项研究中数据集描述的交叉审查揭示了来源和文档记录的不一致：结构相似的七特征数据集被描述为不同的国家背景，报告的样本量从2,100到3,000条记录不等[5], [6], [9]。验证实践以随机留出法或k折交叉验证为主；在经全文审查的研究中，仅有一项测试了空间交叉验证，报告称相对于随机划分结果，性能显著下降（AUC从0.89降至0.55–0.62）[4]。可解释人工智能和不确定性量化在所综述文献中仍属少数实践。一篇先前会议论文集中自述为","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",61,{"impact":18,"substance":16,"depth":118,"authority":78,"freshness":125,"relevant":20,"comment":174},"系统综述揭示作物推荐模型普遍存在数据集来源混乱与验证方法单一（仅一项空间交叉验证即大幅掉点）的问题，对智慧农业AI落地有实质警示价值，但期刊影响力有限。",[176],{"name":170,"url":167},[25,26,27,28,178],"土壤数据",{"code":57,"slug":100,"name":101,"path":102,"parent_code":35},[181,187],{"code":182,"slug":183,"name":184,"path":185,"parent_code":186},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":189},{"candidates":190,"agricultural_evidence":45},[191,197,203,207],{"code":57,"score":192,"supported":45,"title":193,"abstract":195,"tags":196},34,[111,115,194],"artificial intelligence",[113,114,111,115,194],[26],{"code":182,"score":198,"supported":45,"title":199,"abstract":200,"tags":202},15,[183],[201,183],"土壤",[178],{"code":51,"score":12,"supported":45,"title":204,"abstract":205,"tags":206},[120],[],[25,27],{"code":31,"score":125,"supported":45,"title":208,"abstract":209,"tags":210},[],[128,129,47],[],[212,213],"农业人工智能 作物推荐 土壤数据 智慧农业","农业人工智能 作物推荐","农业人工智能作物推荐土壤数据智慧农业-2509","10.64388\u002Firev10i3-1722922",{"doi":215,"openalex_id":217,"authors":218,"venue":170,"cited_by_count":68,"oa_url":223,"card":224,"direction":161,"ingested_from":162},"W7212594050",[219,221],{"name":220,"orcid":8},"Snehal Sanjay Raut",{"name":222,"orcid":8},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722922.pdf",{"tldr":225,"method":226,"finding":227,"direction":73,"opportunity":228},"综述AI作物推荐研究，指出高准确率多源于数据与验证缺陷。","系统综述35篇文献，对比ML、DL、XAI与IoT方法及验证方式。","常用7特征数据集来源不一，随机验证致准确率虚高，空间验证性能骤降。","需建立标准化数据集并推广空间交叉验证与不确定性量化，提升模型真实泛化能力。","2026-09-15T23:30:08.397298Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":8,"source_name":236,"source_url":233,"published_at":237,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":238,"sources":241,"tags":243,"primary_topic":244,"secondary_topics":245,"classification":247,"search_phrases":292,"slug":294,"view_count":65,"doi":295,"paper":296,"created_at":321},2302,"AgriSphere: A Smart Agriculture Framework Integrating IoT and Artificial Intelligence for Adaptive Crop Selection","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315503187260909160504","Introduction Rapid climate change, soil degradation, and changing environmental conditions make crop selection difficult for farmers. This study proposes an IoT- and AI-based framework to recommend suitable crops using current soil conditions and future weather forecasts. It also identifies the key environmental factors influencing crop selection. Methods A three-layer architecture was designed with data collection, communication, and data processing modules. Real-time data on nitrogen, phosphorus, potassium, pH, temperature, and humidity were collected through IoT sensors and combined with rainfall and historical agricultural data. A dataset containing multiple environmental features and 22 crop classes was used for model development. Machine learning and deep learning methods, including Random Forest, XGBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Decision Trees (DT), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM), were applied for classification and forecasting. Performance was evaluated using accuracy, F1-Score, MAE, RMSE, and R 2 . Pareto analysis was also performed to identify the most influential parameters. Results Random Forest and CNN achieved the highest classification accuracy of 99.54% with an F1-Score of 0.995, while XGBoost also performed strongly with 99.32% accuracy. Regression analysis showed that ensemble models outperformed linear models. Pareto analysis revealed that rainfall, humidity, and potassium were the most influential factors in crop recommendation. In a real-time case study, the framework recommended rice as the most suitable crop for the given input conditions. Discussion The results show that integrating IoT sensing with AI-based forecasting supports proactive crop planning before sowing and improves sustainable farming decisions under changing climate conditions. Conclusion The proposed framework effectively combines real-time monitoring, predictive analytics, and intelligent crop recommendation, offering a practical foundation for scalable precision agriculture systems.","引言 快速的气候变化、土壤退化以及不断变化的环境条件使农民难以进行作物选择。本研究提出了一种基于物联网（IoT）和人工智能（AI）的框架，利用当前土壤条件和未来天气预报来推荐适宜的作物。研究还识别了影响作物选择的关键环境因素。方法 设计了一个三层架构，包括数据采集、通信和数据处理模块。通过物联网传感器采集氮、磷、钾、pH值、温度和湿度的实时数据，并结合降雨量和历史农业数据。使用包含多个环境特征和22种作物类别的数据集进行模型开发。应用机器学习和深度学习方法进行分类和预测，包括随机森林（Random Forest）、XGBoost、K近邻（KNN）、支持向量机（SVM）、卷积神经网络（CNN）、决策树（DT）、深度神经网络（DNN）和长短期记忆网络（LSTM）。采用准确率、F1分数、MAE、RMSE和R²评估性能。同时进行帕累托分析以识别最具影响力的参数。结果 随机森林和CNN取得了最高的分类准确率99.54%，F1分数为0.995，XGBoost也表现强劲，准确率为99.32%。回归分析表明，集成模型优于线性模型。帕累托分析显示，降雨量、湿度和钾是作物推荐中影响最大的因素。在实时案例研究中，该框架推荐水稻为给定输入条件下最适宜的作物。讨论 结果表明，将物联网感知与基于人工智能的预测相结合，有助于在播种前进行主动的作物规划，并在气候变化条件下改善可持续农业决策。结论 所提出的框架有效结合了实时监测、预测分析和智能作物推荐，为可扩展的精准农业系统提供了实用基础。","The Open Agriculture Journal","2026-09-11T00:00:00Z",{"impact":16,"substance":239,"depth":91,"authority":18,"freshness":93,"relevant":20,"comment":240},21,"该论文提出IoT与AI融合的智能选种框架，多模型对比与实时案例验证充分，方法新颖且结论可靠，对智慧农业精准种植有较高参考价值。",[242],{"name":236,"url":233},[25,26,128,27,28],{"code":57,"slug":100,"name":101,"path":102,"parent_code":35},[246],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":248},{"candidates":249,"agricultural_evidence":45},[250,254,258,262,269,273,280,286],{"code":57,"score":16,"supported":45,"title":251,"abstract":252,"tags":253},[194],[113,114,111,115],[26],{"code":51,"score":118,"supported":45,"title":255,"abstract":256,"tags":257},[120],[27,122],[25,27],{"code":31,"score":198,"supported":45,"title":259,"abstract":260,"tags":261},[47],[128,47],[128],{"code":263,"score":78,"supported":45,"title":264,"abstract":265,"tags":268},"A1",[],[266,267],"水稻","rice",[],{"code":182,"score":78,"supported":45,"title":270,"abstract":271,"tags":272},[],[201,183],[],{"code":274,"score":78,"supported":45,"title":275,"abstract":276,"tags":279},"H7",[],[277,278],"气候变化","climate change",[],{"code":281,"score":65,"supported":45,"title":282,"abstract":283,"tags":285},"H8",[],[284],"可持续农业",[],{"code":287,"score":65,"supported":45,"title":288,"abstract":289,"tags":291},"I6",[],[290],"agricultural data",[],[293,213],"农业人工智能 作物推荐 智慧农业 精准农业","农业人工智能作物推荐智慧农业精准农业-2302","10.2174\u002F0118743315503187260909160504",{"doi":295,"openalex_id":297,"authors":298,"venue":236,"cited_by_count":68,"oa_url":233,"card":316,"direction":161,"ingested_from":162},"W7212307638",[299,301,304,307,310,313],{"name":300,"orcid":8},"Shreya Sriram",{"name":302,"orcid":303},"Prajeesh C B","https:\u002F\u002Forcid.org\u002F0000-0002-8404-8583",{"name":305,"orcid":306},"Delphin Raj Kesari Mary","https:\u002F\u002Forcid.org\u002F0000-0002-8989-7090",{"name":308,"orcid":309},"Anju S. Pillai","https:\u002F\u002Forcid.org\u002F0000-0001-5298-6789",{"name":311,"orcid":312},"R. V.","https:\u002F\u002Forcid.org\u002F0000-0002-0699-2990",{"name":314,"orcid":315},"V. M. Manikandan","https:\u002F\u002Forcid.org\u002F0000-0001-6903-7563",{"tldr":317,"method":318,"finding":319,"direction":161,"opportunity":320},"提出IoT与AI融合框架，结合实时土壤数据与天气预报推荐适宜作物。","三层IoT架构采集NPK、pH等数据，用RF、CNN等8种模型分类22种作物。","RF与CNN分类准确率达99.54%，降雨、湿度和钾是作物选择最关键因素。","可探索多源遥感与边缘计算融合，实现小农户低成本、可解释的实时作物推荐系统。","2026-09-13T23:30:09.745236Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":8,"source_name":328,"source_url":325,"published_at":329,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":330,"score_detail":331,"sources":333,"tags":335,"primary_topic":337,"secondary_topics":338,"classification":345,"search_phrases":390,"slug":393,"view_count":68,"doi":394,"paper":395,"created_at":413},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)","2026-10-21T00:00:00Z",80,{"impact":16,"substance":17,"depth":16,"authority":92,"freshness":125,"relevant":20,"comment":332},"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。",[334],{"name":328,"url":325},[25,26,128,27,336],"气候韧性",{"code":57,"slug":100,"name":101,"path":102,"parent_code":35},[339,340],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":341,"slug":342,"name":343,"path":344,"parent_code":186},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":38,"locked":13,"version":39,"method":40,"evidence":346},{"candidates":347,"agricultural_evidence":45},[348,354,358,364,370,374,378,384],{"code":57,"score":16,"supported":45,"title":349,"abstract":350,"tags":353},[194],[113,114,351,111,115,352],"计算机视觉","computer vision",[26],{"code":51,"score":118,"supported":45,"title":355,"abstract":356,"tags":357},[122],[27,122],[25,27],{"code":31,"score":12,"supported":45,"title":359,"abstract":360,"tags":363},[],[128,361,47,362],"边缘计算","edge computing",[128],{"code":341,"score":125,"supported":45,"title":365,"abstract":366,"tags":369},[],[367,368,342],"灌溉","水资源",[],{"code":274,"score":19,"supported":45,"title":371,"abstract":372,"tags":373},[],[277,278],[336],{"code":182,"score":78,"supported":45,"title":375,"abstract":376,"tags":377},[],[201,183],[],{"code":379,"score":65,"supported":45,"title":380,"abstract":381,"tags":383},"A7",[],[382],"间作",[],{"code":385,"score":65,"supported":45,"title":386,"abstract":387,"tags":389},"F3",[],[388],"养分管理",[],[391,392],"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":394,"openalex_id":396,"authors":397,"venue":328,"cited_by_count":68,"oa_url":325,"card":408,"direction":161,"ingested_from":162},"W7223121456",[398,401,403,405],{"name":399,"orcid":400},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":402,"orcid":8},"Oluwadare OYEBODE",{"name":404,"orcid":8},"Iretiayo Adelaiye",{"name":406,"orcid":407},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":409,"method":410,"finding":411,"direction":73,"opportunity":412},"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":415,"title":416,"url":417,"summary":418,"summary_zh":419,"content":8,"source_name":420,"source_url":417,"published_at":421,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":422,"score_detail":423,"sources":426,"tags":428,"primary_topic":430,"secondary_topics":431,"classification":432,"search_phrases":461,"slug":464,"view_count":65,"doi":465,"paper":466,"created_at":484},4877,"IoT-Enabled Autonomous Agriculture Monitoring Rover for Smart Farming: A Comprehensive Literature Review","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85108","The rapid development of the Internet of Things (IoT), autonomous robotics, artificial intelligence (AI), embedded systems, wireless communication, and precision agriculture has created new opportunities for improving agricultural productivity and resource utilization. Conventional agricultural monitoring relies extensively on manual field inspection, which is time-consuming, labor-intensive, and often unable to provide continuous information about crop and soil conditions. Autonomous agricultural rovers provide an alternative approach by enabling mobile sensing, real-time environmental monitoring, crop observation, navigation, and precision agricultural operations. This paper presents a comprehensive literature review of IoT-enabled autonomous agricultural rovers for smart farming, with particular emphasis on recent developments reported in IEEE literature. Existing research is analyzed according to rover architecture, sensing technologies, navigation techniques, communication technologies, IoT platforms, agricultural applications, AI integration, and autonomy levels. Recent studies have demonstrated the feasibility of ESP32-based IoT rovers, ROS-based agricultural monitoring robots, autonomous navigation systems, precision irrigation platforms, soil-monitoring robots, and AI-enabled agricultural rovers. However, several limitations remain, including restricted autonomous navigation in unstructured environments, limited multi-parameter sensing, communication reliability in rural areas, high computational requirements for AI, energy limitations, and insufficient integration of monitoring with autonomous decision-making. Based on the reviewed literature, a research gap is identified for a low-cost, modular, IoT-enabled autonomous agricultural monitoring rover capable of simultaneously performing soil and environmental sensing, crop monitoring, obstacle avoidance, real-time wireless communication, location-aware operation, and intelligent decision support.","物联网（IoT）、自主机器人、人工智能（AI）、嵌入式系统、无线通信和精准农业的快速发展，为提升农业生产率和资源利用率创造了新的机遇。传统农业监测在很大程度上依赖人工田间巡检，这种方式耗时、费力，且往往无法持续提供作物和土壤状况的信息。自主农业巡田机器人通过实现移动传感、实时环境监测、作物观测、导航和精准农业作业，提供了一种替代方案。本文对面向智慧农业的物联网赋能自主农业巡田机器人进行了全面的文献综述，特别关注IEEE文献中报道的最新进展。现有研究按照巡田机器人架构、传感技术、导航技术、通信技术、物联网平台、农业应用、人工智能集成和自主性水平进行了分析。近期研究已证明基于ESP32的物联网巡田机器人、基于ROS的农业监测机器人、自主导航系统、精准灌溉平台、土壤监测机器人和人工智能赋能农业巡田机器人的可行性。然而，仍存在若干局限，包括非结构化环境中的自主导航受限、多参数传感能力有限、农村地区通信可靠性不足、人工智能的高计算需求、能量限制，以及监测与自主决策的集成不充分。基于所综述的文献，本文识别出一个研究空白，即缺乏一种低成本、模块化、物联网赋能的自主农业监测巡田机器人，能够同时执行土壤与环境传感、作物监测、避障、实时无线通信、位置感知作业和智能决策支持。","International Journal for Research in Applied Science and Engineering Technology","2026-10-13T00:00:00Z",52,{"impact":93,"substance":424,"depth":118,"authority":78,"freshness":93,"relevant":20,"comment":425},14,"综述性论文，系统梳理IoT自主农业巡检机器人架构与瓶颈，但来源期刊影响力有限，属细分领域参考价值。",[427],{"name":420,"url":417},[25,26,429,128,27],"农业机器人",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":433},{"candidates":434,"agricultural_evidence":45},[435,439,443,448,452,456],{"code":31,"score":16,"supported":45,"title":436,"abstract":437,"tags":438},[47],[128,129,47],[128],{"code":51,"score":44,"supported":45,"title":440,"abstract":441,"tags":442},[],[25,27,122],[25,27],{"code":57,"score":19,"supported":45,"title":444,"abstract":445,"tags":447},[],[194,446],"decision support",[26],{"code":182,"score":78,"supported":45,"title":449,"abstract":450,"tags":451},[],[201,183],[],{"code":341,"score":78,"supported":45,"title":453,"abstract":454,"tags":455},[],[367,342],[],{"code":457,"score":20,"supported":13,"title":458,"abstract":459,"tags":460},"F6",[],[],[429],[462,463],"ESP32 ROS 农业机器人","农业人工智能 农业机器人 智慧农业 精准农业","ESP32ROS农业机器人-4877","10.22214\u002Fijraset.2026.85108",{"doi":465,"openalex_id":467,"authors":468,"venue":420,"cited_by_count":68,"oa_url":417,"card":479,"direction":161,"ingested_from":162},"W7221682750",[469,471,473,475,477],{"name":470,"orcid":8},"Chaitra A S",{"name":472,"orcid":8},"Venugopal Gowda V",{"name":474,"orcid":8},"Vinay H",{"name":476,"orcid":8},"Jeevan T",{"name":478,"orcid":8},"Puneeth M",{"tldr":480,"method":481,"finding":482,"direction":161,"opportunity":483},"综述了物联网赋能的自主农业监测机器人，分析架构、传感、导航与AI集成，指出低成本模块化系统的研究空白","文献综述，聚焦IEEE近期研究，按架构、传感、导航、通信、AI与自主等级分类分析","现有系统在非结构化导航、多参数传感、农村通信、AI算力与能耗上存在局限，缺乏监测与决策一体化。","可研发低成本模块化IoT自主巡田机器人，融合多传感、避障、实时通信与智能决策，填补集成空白。","2026-10-14T23:30:08.717386Z",{"id":486,"title":487,"url":488,"summary":489,"summary_zh":490,"content":8,"source_name":491,"source_url":488,"published_at":492,"category":11,"cover_url":8,"hotness":493,"is_selected":13,"score":494,"score_detail":495,"sources":497,"tags":501,"primary_topic":504,"secondary_topics":505,"classification":507,"search_phrases":522,"slug":525,"view_count":20,"doi":8,"paper":526,"created_at":542},4672,"AI-BASED ENERGY EFFICIENT COMMUNICATION IN IOT SYSTEM","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F309","The rapid growth of the Internet of Things (IoT) has led to a massive increase in the number of connected devices across various domains such as smart homes, healthcare, agriculture, and industrial automation. As these devices are often battery-powered and deployed in remote or large scale environments, energy efficiency has become a critical concern in IoT communication systems. Traditional IoT networks typically operate using fixed transmission power levels, which do not adapt to changing environmental or network conditions, resulting in unnecessary energy consumption and reduced battery life.To address these limitations, there is a growing need for intelligent and adaptive communication mechanisms that can dynamically optimize power usage without compromising reliability. In this context, Artificial Intelligence (AI)-based adaptive transmission power control has emerged as a promising solution. This review paper presents an analysis of AI-driven techniques that enable IoT devices to adjust their transmission power based on real-time conditions and network requirements.The study explores approaches such as machine learning-based prediction models, which utilize parameters like Received Signal Strength Indicator (RSSI), distance, interference levels, and packet loss rates to determine optimal transmission power. Additionally, the role of edge computing is examined, enabling faster decision-making with reduced latency. The integration of Low Power Wide Area Network (LPWAN) technologies is also discussed for supporting long-range and energy-efficient communication.Furthermore, the paper highlights how adaptive power control improves network performance by reducing energy wastage, minimizing interference, and extending device lifetime. These benefits are especially useful in applications like smart cities, precision agriculture, and industrial automation.Despite these advantages, challenges such as implementation complexity, resource constraints, and data privacy concerns remain. The paper also outlines future research directions, including the development of lightweight AI models and improved integration with emerging technologies.In conclusion, AIbased adaptive transmission power control significantly enhances energy efficiency and communication reliability, making it essential for the sustainable development of next- generation IoT systems. It also supports scalable and cost-effective deployment of IoT networks in diverse real- world environments.","物联网（IoT）的快速发展使得智能家居、医疗健康、农业和工业自动化等各个领域的联网设备数量大幅增加。由于这些设备通常由电池供电，且部署在偏远或大规模环境中，能效已成为物联网通信系统中的关键问题。传统物联网网络通常采用固定传输功率运行，无法适应不断变化的环境或网络条件，导致不必要的能耗和电池寿命缩短。为解决这些局限，人们日益需要能够在不影响可靠性的前提下动态优化功率使用的智能自适应通信机制。在此背景下，基于人工智能（AI）的自适应传输功率控制已成为一种有前景的解决方案。本综述论文分析了使物联网设备能够根据实时条件和网络需求调整传输功率的AI驱动技术。研究探讨了基于机器学习的预测模型等方法，这些方法利用接收信号强度指示（RSSI）、距离、干扰水平和丢包率等参数来确定最优传输功率。此外，还考察了边缘计算的作用，其能够以更低延迟实现更快决策。论文还讨论了低功耗广域网（LPWAN）技术的集成，以支持长距离和节能通信。进一步地，本文强调了自适应功率控制如何通过减少能量浪费、最小化干扰和延长设备寿命来改善网络性能。这些优势在智慧城市、精准农业和工业自动化等应用中尤为有用。尽管具有这些优势，实施复杂性、资源约束和数据隐私问题等挑战仍然存在。论文还概述了未来研究方向，包括开发轻量级AI模型以及与新兴技术的改进集成。总之，基于AI的自适应传输功率控制显著提升了能效和通信可靠性，使其成为下一代物联网系统可持续发展的关键。它还支持物联网网络在多样化现实环境中的可扩展且具有成本效益的部署。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",25,47,{"impact":93,"substance":424,"depth":92,"authority":78,"freshness":78,"relevant":20,"comment":496},"综述性论文，方法层面有参考价值，但非农业专属突破，且期刊权威性一般，仅作主题聚合素材。",[498,499],{"name":491,"url":488},{"name":491,"url":500},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[25,26,502,27,503],"农业物联网","低功耗通信",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[506],{"code":57,"slug":100,"name":101,"path":102,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":508},{"candidates":509,"agricultural_evidence":45},[510,514,518],{"code":31,"score":16,"supported":45,"title":511,"abstract":512,"tags":513},[47],[128,361,129,47,362],[502],{"code":57,"score":12,"supported":45,"title":515,"abstract":516,"tags":517},[],[114,111,194],[26],{"code":51,"score":93,"supported":45,"title":519,"abstract":520,"tags":521},[],[27,122],[25,27],[523,524],"AI 物联网 传输功率控制","LPWAN 精准农业","AI物联网传输功率控制-4672",{"doi":8,"openalex_id":527,"authors":528,"venue":491,"cited_by_count":68,"oa_url":488,"card":537,"direction":161,"ingested_from":162},"W7221414266",[529,531,533,535],{"name":530,"orcid":8},"Shital Chavan",{"name":532,"orcid":8},"Anam Bedrekar",{"name":534,"orcid":8},"Sanika Babar",{"name":536,"orcid":8},"Prof. M. V. Naiknavare",{"tldr":538,"method":539,"finding":540,"direction":161,"opportunity":541},"综述AI自适应发射功率控制提升IoT通信能效，面向智慧农业等场景。","综述机器学习预测模型、边缘计算与LPWAN技术，基于RSSI等参数。","AI自适应功率控制可降低能耗、减少干扰并延长设备寿命，但存在实现复杂与隐私挑战。","可研究面向农田的轻量级AI功率控制模型，结合土壤墒情与作物需水优化能耗。","2026-10-11T23:30:16.397155Z"]