[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1186":3,"related-1186":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":73,"slug":76,"view_count":77,"doi":78,"paper":8,"created_at":79},1186,"Machine Learning Classification Techniques for Smart Agriculture: A Review of Disease Detection and Crop Recommendation (机器学习分类技术在智慧农业中的应用综述:病害检测与作物推荐)","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1878006\u002Ffull","Frontiers in Artificial Intelligence 2026年8月31日综述。研究对2023年1月至2026年1月Scopus、IEEE Xplore、ScienceDirect和SpringerLink数据库中的文章进行系统检索,使用IoT and machine learning and agriculture、crop recommendation、plant disease detection、precision farming等关键词组合,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。结果表明,随机森林( Random Forest)、支持向量机(SVM)和决策树等集成方法与传统机器学习方法在作物产量预测中准确率达到99.9%;CNN和ResNet深度学习模型病害检测准确率超过98%;GRU和LSTM时间序列模型天气条件预测准确率范围94-98%。IoT系统在精准农业应用方面显著改善资源利用,实现60-96%的节水、25%的增产和36-38%的除草剂减量。",null,"[](https:\u002F\u002Fwww.frontiersin.org\u002F)\n\n[![Image 2](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1878006\u002Ffull) Frontiers in Artificial Intelligence](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence)\n\n About us\n\nAbout us\n\n*   Who we are\n*   [Mission and values](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fmission)\n*   [History](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fhistory)\n*   [Leadership](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fleadership)\n*   [Awards](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fawards)\n\n*   Impact and progress\n*   [Frontiers' 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AI in Food, Agriculture and Water\n\nVolume 9 - 2026 |  doi: 10.3389\u002Ffrai.2026.1878006\n\nPublished in\n\n[![Image 6: Frontiers in Artificial Intelligence](https:\u002F\u002Fd2csxpduxe849s.cloudfront.net\u002Fmedia\u002FE32629C6-9347-4F84-81FEAEF7BFA342B3\u002F5C3D1F1F-95D8-470B-A0A80883EA552939\u002Fwebimage-02A3C797-5E54-4C22-BE7DBFE943AEAA54.png)](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence)\n\n[Frontiers in Artificial Intelligence](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence)\n\n*   #### [AI in Food, Agriculture and Water](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Fsections\u002Fai-in-food-agriculture-and-water)\n\n*   [**6.7** impact factor](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence)\n*   [**8** citescore](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence)\n\n[Part of a Research Topic Advanced technologies for water management: targeting sustainable agriculture Submission open * **24k** views * **12** articles](https:\u002F\u002Fwww.frontiersin.org\u002Fresearch-topics\u002F72689\u002Fadvanced-technologies-for-water-management-targeting-sustainable-agriculture)\n\n### Reviewers\n\nReviewed by\n\n![Image 7: 3 Anonymous reviewers](https:\u002F\u002Fstatic2.frontiersin.org\u002Fstatic-resources\u002Fimages\u002Fdefault_profileimage.webp)\n\n3 Anonymous reviewers\n\nOutline\n\n*    [Abstract](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1878006\u002Ffull#h1)\n\n## ORIGINAL RESEARCH article\n\nFront. Artif. Intell.\n\nSec. AI in Food, Agriculture and Water\n\n# Machine Learning Classification Techniques for Smart Agriculture: A Review of Disease Detection and Crop Recommendation\n\n*   [![Image 8: Umamaheswari M](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F3495568\u002F74) Umamaheswari M](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F3495568)\n*   [![Image 9: Kavitha D](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F3406016\u002F74) Kavitha D](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F3406016)\n\n*   VIT University Chennai, Chennai, India\n\nArticle metrics\n\n[View details](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1878006\u002Ffull#metrics)\n\nThe final, formatted version of the article will be published soon.\n\nNotify me\n\nSelect one of your emails\n\nYou have multiple emails registered with Frontiers:\n\nNotify me\n\nNotify me on publication\n\nPlease enter your email address:\n\nEmail \n\nIf you already have an account, please [login](https:\u002F\u002Fwww.frontiersin.org\u002Flogin?returnUrl=\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1878006\u002Fabstract)\n\nYou don't have a Frontiers account ? You can [register here](https:\u002F\u002Fwww.frontiersin.org\u002Fregister?returnUrl=\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.187","Frontiers in Artificial Intelligence 2026","2026-08-31T08:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,18,16,12,3,1,"综述性论文，系统梳理机器学习在农业病害检测与作物推荐中的应用，对智慧农业技术发展有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","机器学习","病害检测","作物推荐",{"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",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,53,61,67],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":52},26,true,[28,48],"machine learning",[50,28,48,51],"深度学习","artificial intelligence",[28],{"code":54,"score":55,"supported":46,"title":56,"abstract":58,"tags":60},"I1",20,[27,57],"smart agriculture",[59],"精准农业",[27],{"code":62,"score":21,"supported":46,"title":63,"abstract":64,"tags":66},"F4",[],[65],"除草",[],{"code":68,"score":21,"supported":46,"title":69,"abstract":70,"tags":72},"I5",[],[71],"IoT",[],[74,75],"作物推荐 智慧农业 机器学习 病害检测","作物推荐 智慧农业","作物推荐智慧农业机器学习病害检测-1186",2,"10.3389\u002Ffrai.2026.1878006\u002Ffull","2026-09-01T00:03:39.942782Z",{"total":81,"page":22,"page_size":81,"items":82},6,[83,151,228,294,381,467],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":8,"source_name":89,"source_url":86,"published_at":90,"category":12,"cover_url":8,"hotness":91,"is_selected":14,"score":92,"score_detail":93,"sources":98,"tags":102,"primary_topic":104,"secondary_topics":105,"classification":106,"search_phrases":131,"slug":134,"view_count":77,"doi":135,"paper":136,"created_at":150},3271,"A Systematic Study of Supervised and Ensemble Learning Approaches for Crop Selection in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896343","Selecting the crop best matched to a field’s soil and climate is one of the highest-leverage decisions in agriculture, and one that farmers have traditionally made by intuition or inherited practice. Soil pH together with nitrogen, phosphorus and potassium concentration, and the local temperature, humidity and rainfall regime, jointly determine which crop will flourish and which will fail, and the relationship between those variables and crop performance is neither linear nor independent. Machine learning has therefore become the dominant approach to automated crop recommendation. This paper reviews the field across four technique families — classical supervised learning, ensemble and boosting methods, deep learning and metaheuristic hybrids, and IoT and deployment-oriented systems — and compares twenty-four representative studies published between 2016 and 2026 in terms of method, data source, reported accuracy, advantage and limitation. A generic seven-stage recommendation pipeline is presented and each family is situated within it. The comparison shows that reported accuracy on the standard nutrient-and-climate benchmark has converged in a narrow band between roughly 98 and 99.5 per cent, that boosting and ensemble methods occupy the upper part of that band, and that further gains on the benchmark are no longer the binding constraint on the field. The gaps that remain open are instead the absence of socio-economic and market variables from the decision, the lack of region-specific and long-horizon environmental validation, dataset narrowness and geographic bias, limited interpretability, and the accessibility of these systems to small and resource-poor farmers. These are consolidated into a set of research directions for future work.","选择与田块土壤和气候最匹配的作物是农业中杠杆效应最高的决策之一，而农民传统上依靠直觉或世代相传的经验来做出这一决策。土壤pH值以及氮、磷、钾浓度，加上当地的气温、湿度和降雨状况，共同决定了哪种作物能够茁壮成长、哪种会歉收，而这些变量与作物表现之间的关系既非线性也非相互独立。因此，机器学习已成为自动化作物推荐的主流方法。本文从四个技术族系——经典监督学习、集成与提升方法、深度学习与元启发式混合方法，以及物联网与面向部署的系统——对该领域进行了综述，并从方法、数据来源、报告精度、优势和局限性方面比较了2016年至2026年间发表的二十四项代表性研究。本文提出了一个通用的七阶段推荐流程，并将每个技术族系置于该流程中加以定位。比较结果表明，在标准养分与气候基准上的报告精度已收敛于约98%至99.5%的狭窄区间内，提升与集成方法占据该区间的上端，而在该基准上进一步提升已不再是该领域的约束瓶颈。真正尚未填补的空白在于：决策中缺乏社会经济和市场变量，缺少针对特定区域和长期环境验证，数据集狭窄且存在地理偏差，可解释性有限，以及这些系统对小型和资源匮乏农户的可及性不足。这些空白被归纳为未来工作的一系列研究方向。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,78,{"impact":19,"substance":94,"depth":18,"authority":95,"freshness":96,"relevant":22,"comment":97},22,13,9,"系统综述24项研究并指出基准精度已趋饱和，真正瓶颈转向社会经济变量与可解释性，对智慧农业选种方向有参考价值。",[99,100],{"name":89,"url":86},{"name":89,"url":101},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896344",[27,103,28,59,30],"农业人工智能",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":107},{"candidates":108,"agricultural_evidence":46},[109,115,119,126],{"code":32,"score":110,"supported":46,"title":111,"abstract":112,"tags":114},11,[],[50,28,48,113],"deep learning",[103,28],{"code":54,"score":13,"supported":46,"title":116,"abstract":117,"tags":118},[57],[],[27,59],{"code":120,"score":81,"supported":46,"title":121,"abstract":122,"tags":125},"H2",[],[123,124],"土壤","soil",[],{"code":68,"score":81,"supported":46,"title":127,"abstract":128,"tags":130},[],[129,71],"物联网",[],[132,133],"农业人工智能 作物推荐 智慧农业 机器学习","农业人工智能 作物推荐","农业人工智能作物推荐智慧农业机器学习-3271","10.5281\u002Fzenodo.22896343",{"doi":135,"openalex_id":137,"authors":138,"venue":89,"cited_by_count":141,"oa_url":86,"card":142,"direction":148,"ingested_from":149},"W7214002748",[139],{"name":140,"orcid":8},"Prof. Nagendra Patel Sahil Verma",0,{"tldr":143,"method":144,"finding":145,"direction":146,"opportunity":147},"系统综述2016-2026年24项作物推荐研究，比较四类机器学习方法并指出基准精度已趋饱和。","综述监督学习、集成提升、深度学习与元启发式、物联网部署四类方法及七阶段流程。","标准基准精度收敛于98%-99.5%，提升集成法最优，但精度已非领域瓶颈。","农业人工智能与决策模型","将社会经济与市场变量、区域长期环境验证及可解释性纳入作物推荐，服务小农户。","智慧农业 \u002F 农业物联网","openalex","2026-09-23T23:30:09.369987Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":8,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":163,"tags":165,"primary_topic":167,"secondary_topics":168,"classification":169,"search_phrases":205,"slug":208,"view_count":22,"doi":209,"paper":210,"created_at":227},1885,"Ai Based Smart Crop Recommendation and Yield Prediction Using Ml and Weather Analytics","https:\u002F\u002Fdoi.org\u002F10.47392\u002Firjaeh.2026.0696","Agriculture plays a vital role in ensuring global food security; however, unpredictable climatic conditions, changing soil characteristics, and inefficient crop selection continue to affect agricultural productivity. Accurate crop recommendation and yield prediction are essential for supporting farmers in making informed cultivation decisions and maximizing crop production. This paper proposes an AI-Based Smart Crop Recommendation and Yield Prediction Framework that integrates machine learning techniques with weather analytics to provide intelligent decision support for precision agriculture. The proposed framework utilizes multiple environmental and agricultural parameters, including soil nutrients (Nitrogen, Phosphorus, and Potassium), soil pH, temperature, humidity, rainfall, and historical weather information, to recommend the most suitable crop and estimate its expected yield. A comprehensive data preprocessing pipeline involving missing value treatment, feature engineering, normalization, and feature selection is employed to improve model performance. Multiple machine learning algorithms, including Random Forest, XGBoost, LightGBM, and Support Vector Machine, are evaluated, and an ensemble learning approach is adopted to enhance prediction accuracy and model robustness. Weather analytics are incorporated to capture seasonal variations and climatic influences that significantly impact crop productivity. The proposed framework is validated using publicly available agricultural datasets and evaluated through cross-validation using standard performance metrics such as accuracy, precision, recall, F1-score, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). The experimental results are expected to demonstrate that integrating weather analytics with ensemble machine learning significantly improves both crop recommendation accuracy and yield prediction performance compared with conventional single-model approaches. The proposed framework provides an intelligent, scalable, and data-driven decision support system that can assist farmers, agricultural experts, and policymakers in improving productivity, optimizing resource utilization, and promoting sustainable farming practices under varying climatic conditions.","农业在保障全球粮食安全方面发挥着至关重要的作用；然而，不可预测的气候条件、不断变化的土壤特性以及低效的作物选择持续影响着农业生产率。精准的作物推荐与产量预测对于帮助农民做出明智的种植决策并最大化作物产量至关重要。本文提出了一种基于人工智能的智能作物推荐与产量预测框架，该框架将机器学习技术与天气分析相结合，为精准农业提供智能决策支持。所提出的框架利用多种环境和农业参数，包括土壤养分（氮、磷和钾）、土壤pH值、温度、湿度、降雨量以及历史天气信息，来推荐最适宜的作物并估算其预期产量。采用包含缺失值处理、特征工程、归一化和特征选择的综合数据预处理流程，以提升模型性能。评估了多种机器学习算法，包括随机森林、XGBoost、LightGBM和支持向量机，并采用集成学习方法以增强预测精度和模型鲁棒性。引入天气分析以捕捉对作物生产力有显著影响的季节性变化和气候因素。所提出的框架使用公开可用的农业数据集进行验证，并通过交叉验证使用标准性能指标进行评估，如准确率、精确率、召回率、F1分数、平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R²）。预期实验结果表明，与传统的单一模型方法相比，将天气分析与集成机器学习相结合能显著提高作物推荐准确率和产量预测性能。所提出的框架提供了一个智能、可扩展且数据驱动的决策支持系统，能够帮助农民、农业专家和政策制定者在不同气候条件下提高生产力、优化资源利用并促进可持续农业实践。","International Research Journal on Advanced Engineering Hub (IRJAEH)","2026-09-07T00:00:00Z",40,{"impact":161,"substance":20,"depth":20,"authority":81,"freshness":77,"relevant":22,"comment":162},8,"论文提出结合天气分析的集成学习框架，但缺乏实证结果，影响有限。",[164],{"name":157,"url":154},[27,103,166,28,30],"产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":170},{"candidates":171,"agricultural_evidence":46},[172,177,183,190,194,200],{"code":32,"score":110,"supported":46,"title":173,"abstract":174,"tags":176},[],[28,48,175],"decision support",[103,28],{"code":54,"score":178,"supported":46,"title":179,"abstract":180,"tags":182},7,[],[59,181],"precision agriculture",[27],{"code":184,"score":81,"supported":46,"title":185,"abstract":186,"tags":189},"A8",[],[187,188],"粮食安全","food security",[],{"code":120,"score":81,"supported":46,"title":191,"abstract":192,"tags":193},[],[123,124],[],{"code":195,"score":21,"supported":46,"title":196,"abstract":197,"tags":199},"H8",[],[198],"可持续农业",[],{"code":201,"score":22,"supported":14,"title":202,"abstract":203,"tags":204},"I3",[],[],[166],[206,207],"农业人工智能 产量预测 作物推荐 智慧农业","农业人工智能 产量预测","农业人工智能产量预测作物推荐智慧农业-1885","10.47392\u002Firjaeh.2026.0696",{"doi":209,"openalex_id":211,"authors":212,"venue":157,"cited_by_count":141,"oa_url":154,"card":222,"direction":148,"ingested_from":149},"W7211877620",[213,215,217,219,220],{"name":214,"orcid":8},"Choudhuri Saswat Pattnaik",{"name":216,"orcid":8},"Rojalini Mohanty",{"name":218,"orcid":8},"Bijaya Laxmi Hazra",{"name":218,"orcid":8},{"name":221,"orcid":8},"Akash Kumar Jena",{"tldr":223,"method":224,"finding":225,"direction":146,"opportunity":226},"提出AI框架，结合机器学习与天气分析，推荐作物并预测产量。","集成学习（RF、XGBoost等）与天气数据，含预处理和特征选择。","集成天气分析的集成模型比单一模型更准确。","可探索多源数据融合、可解释性模型及区域适应性，提升实际部署效果。","2026-09-08T23:30:07.974730Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":8,"source_name":89,"source_url":231,"published_at":234,"category":12,"cover_url":8,"hotness":91,"is_selected":14,"score":235,"score_detail":236,"sources":238,"tags":242,"primary_topic":244,"secondary_topics":245,"classification":246,"search_phrases":278,"slug":279,"view_count":77,"doi":280,"paper":281,"created_at":293},1527,"Smart Crop Recommendation System","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22245870","Agriculture remains the primary driver of India's economic stability and food security. However, conventional farming heavily relies on subjective experience rather than scientific data, resulting in sub-optimal crop selection, improper resource usage, and heightened vulnerability to changing weather and plant diseases. To resolve these limitations, this paper presents the Smart Crop Recommendation System, an integrated decision-support web platform utilizing Artificial Intelligence, Machine Learning, Deep Learning, and Real-time Weather Analytics. The proposed application evaluates soil composition—specifically Nitrogen (N), Phosphorus (P), Potassium (K), and pH levels—alongside environmental parameters including temperature, humidity, and rainfall to accurately recommend optimal crops using Scikit-Learn classification algorithms. Real-time weather forecasting is integrated via the OpenWeather API to guide critical agricultural schedules such as sowing and irrigation. Furthermore, a Deep Learning module employing a Convolutional Neural Network (CNN) detects crop diseases from uploaded leaf images and outputs targeted treatment strategies. Implemented with a Django web framework, SQLite database, Power BI analytical dashboards, and cloud infrastructure, the platform offers an end-to-end digital assistant that boosts yield productivity, minimizes farming risks, and supports sustainable precision agriculture.","农业仍然是印度经济稳定和粮食安全的主要驱动力。然而，传统农业严重依赖主观经验而非科学数据，导致作物选择欠佳、资源利用不当，以及对气候变化和植物病害的脆弱性增加。为解决这些局限性，本文提出了智能作物推荐系统——一个集成的决策支持网络平台，利用人工智能、机器学习、深度学习和实时天气分析技术。该应用系统评估土壤成分——特别是氮（N）、磷（P）、钾（K）和pH值——以及包括温度、湿度和降雨量在内的环境参数，通过Scikit-Learn分类算法准确推荐最佳作物。通过OpenWeather API集成实时天气预报，以指导播种和灌溉等关键农事安排。此外，采用卷积神经网络（CNN）的深度学习模块可从上传的叶片图像中检测作物病害，并输出针对性的治理策略。该平台基于Django网络框架、SQLite数据库、Power BI分析仪表板和云基础设施实现，提供端到端的数字助手，可提高产量生产力、降低农业风险，并支持可持续的精准农业。","2026-09-02T00:00:00Z",62,{"impact":20,"substance":18,"depth":17,"authority":13,"freshness":178,"relevant":22,"comment":237},"系统整合AI与气象数据，提供作物推荐与病害检测，对精准农业有参考价值。",[239,240],{"name":89,"url":231},{"name":89,"url":241},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22245869",[27,103,28,30,243],"病害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":247},{"candidates":248,"agricultural_evidence":46},[249,253,257,261,265,272],{"code":32,"score":20,"supported":46,"title":250,"abstract":251,"tags":252},[],[50,28,48,113,51],[103,28,243],{"code":54,"score":178,"supported":46,"title":254,"abstract":255,"tags":256},[],[59,181],[27],{"code":184,"score":81,"supported":46,"title":258,"abstract":259,"tags":260},[],[187,188],[],{"code":120,"score":81,"supported":46,"title":262,"abstract":263,"tags":264},[],[123,124],[],{"code":266,"score":81,"supported":46,"title":267,"abstract":268,"tags":271},"H3",[],[269,270],"灌溉","irrigation",[],{"code":273,"score":21,"supported":46,"title":274,"abstract":275,"tags":277},"H7",[],[276],"气候变化",[],[132,133],"农业人工智能作物推荐智慧农业机器学习-1527","10.5281\u002Fzenodo.22245870",{"doi":280,"openalex_id":282,"authors":283,"venue":89,"cited_by_count":141,"oa_url":231,"card":288,"direction":148,"ingested_from":149},"W7206166550",[284,286],{"name":285,"orcid":8},"Jayashree S P",{"name":287,"orcid":8},"S Sahana",{"tldr":289,"method":290,"finding":291,"direction":146,"opportunity":292},"提出智能作物推荐系统，结合AI与实时天气，推荐作物并检测病害。","使用Scikit-Learn分类算法、CNN、OpenWeather API、D","系统能提高产量、降低风险，支持可持续精准农业。","可扩展至多作物区域适应性、考虑经济因素及用户反馈的个性化推荐。","2026-09-03T23:30:09.661533Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":8,"source_name":300,"source_url":297,"published_at":301,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":307,"tags":309,"primary_topic":313,"secondary_topics":319,"classification":325,"search_phrases":357,"slug":360,"view_count":141,"doi":361,"paper":362,"created_at":380},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":19,"substance":304,"depth":305,"authority":95,"freshness":13,"relevant":22,"comment":306},21,17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[308],{"name":300,"url":297},[27,28,310,311,312],"遥感监测","土壤墒情","冬小麦",{"code":314,"slug":315,"name":316,"path":317,"parent_code":318},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[320,324],{"code":120,"slug":124,"name":321,"path":322,"parent_code":323},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":326},{"candidates":327,"agricultural_evidence":46},[328,333,337,341,348,352],{"code":314,"score":45,"supported":46,"title":329,"abstract":331,"tags":332},[330,315],"winter wheat",[316,330,315],[312],{"code":120,"score":17,"supported":46,"title":334,"abstract":335,"tags":336},[124],[123,124],[311],{"code":32,"score":17,"supported":46,"title":338,"abstract":339,"tags":340},[48],[28,48],[28],{"code":201,"score":13,"supported":46,"title":342,"abstract":343,"tags":347},[],[344,345,346],"遥感","remote sensing","multispectral",[310],{"code":266,"score":81,"supported":46,"title":349,"abstract":350,"tags":351},[],[269,270],[],{"code":54,"score":353,"supported":46,"title":354,"abstract":355,"tags":356},4,[],[27],[27],[358,359],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":361,"openalex_id":363,"authors":364,"venue":300,"cited_by_count":141,"oa_url":297,"card":374,"direction":148,"ingested_from":149},"W7223156204",[365,368,370,372],{"name":366,"orcid":367},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":369,"orcid":8},"Yi Zhang",{"name":371,"orcid":8},"WEI Lei",{"name":373,"orcid":8},"Chengcai Zhang",{"tldr":375,"method":376,"finding":377,"direction":378,"opportunity":379},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","农业遥感与作物表型","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":8,"source_name":387,"source_url":384,"published_at":388,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":389,"score_detail":390,"sources":393,"tags":395,"primary_topic":398,"secondary_topics":399,"classification":406,"search_phrases":440,"slug":443,"view_count":141,"doi":444,"paper":445,"created_at":466},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",80,{"impact":18,"substance":94,"depth":18,"authority":391,"freshness":161,"relevant":22,"comment":392},14,"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[394],{"name":387,"url":384},[27,103,28,396,397],"食品安全","文献计量",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[400],{"code":401,"slug":402,"name":403,"path":404,"parent_code":405},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":39,"locked":14,"version":40,"method":41,"evidence":407},{"candidates":408,"agricultural_evidence":46},[409,416,421,427,432,436],{"code":32,"score":410,"supported":46,"title":411,"abstract":412,"tags":415},19,[51],[50,28,413,48,113,51,414],"计算机视觉","computer vision",[103,28],{"code":401,"score":17,"supported":46,"title":417,"abstract":419,"tags":420},[418],"food safety",[396,418],[396],{"code":422,"score":17,"supported":46,"title":423,"abstract":425,"tags":426},"I7",[424],"bibliometric",[397,424],[397],{"code":273,"score":81,"supported":46,"title":428,"abstract":429,"tags":431},[],[276,430],"climate change",[],{"code":184,"score":21,"supported":46,"title":433,"abstract":434,"tags":435},[],[188],[],{"code":54,"score":22,"supported":14,"title":437,"abstract":438,"tags":439},[],[],[27],[441,442],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":444,"openalex_id":446,"authors":447,"venue":387,"cited_by_count":141,"oa_url":8,"card":461,"direction":146,"ingested_from":149},"W7222920742",[448,450,452,455,458],{"name":449,"orcid":8},"Harang Lee",{"name":451,"orcid":8},"지은서",{"name":453,"orcid":454},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":456,"orcid":457},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":459,"orcid":460},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":462,"method":463,"finding":464,"direction":146,"opportunity":465},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","2026-10-20T23:30:31.567802Z",{"id":468,"title":469,"url":470,"summary":471,"summary_zh":472,"content":8,"source_name":473,"source_url":470,"published_at":474,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":302,"score_detail":475,"sources":477,"tags":479,"primary_topic":481,"secondary_topics":482,"classification":483,"search_phrases":498,"slug":501,"view_count":141,"doi":502,"paper":503,"created_at":540},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",{"impact":18,"substance":55,"depth":305,"authority":391,"freshness":161,"relevant":22,"comment":476},"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[478],{"name":473,"url":470},[27,103,28,59,480],"农户认知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":484},{"candidates":485,"agricultural_evidence":46},[486,490,494],{"code":32,"score":110,"supported":46,"title":487,"abstract":488,"tags":489},[],[28,48,51],[103,28],{"code":120,"score":81,"supported":46,"title":491,"abstract":492,"tags":493},[],[123,124],[],{"code":54,"score":77,"supported":14,"title":495,"abstract":496,"tags":497},[],[],[27,59],[499,500],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":502,"openalex_id":504,"authors":505,"venue":473,"cited_by_count":141,"oa_url":470,"card":535,"direction":146,"ingested_from":149},"W7222866367",[506,509,512,515,518,521,524,527,529,532],{"name":507,"orcid":508},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":510,"orcid":511},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":513,"orcid":514},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":516,"orcid":517},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":519,"orcid":520},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":522,"orcid":523},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":525,"orcid":526},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":528,"orcid":8},"M. Z. Walkup",{"name":530,"orcid":531},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":533,"orcid":534},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":536,"method":537,"finding":538,"direction":146,"opportunity":539},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z"]