{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:56:12Z","timestamp":1790751372412,"version":"4.1.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>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 \u201cIoT and machine learning and agriculture\u201d, \u201ccrop recommendation\u201d, \u201cplant disease detection\u201d, and \u201cprecision farming\u201d. 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\u201396% of water savings, 25% increased yield, and 36\u201338% 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.<\/jats:p>","DOI":"10.3389\/frai.2026.1878006","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:01:01Z","timestamp":1790748061000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning classification techniques for smart agriculture: a review of disease detection and crop recommendation"],"prefix":"10.3389","volume":"9","author":[{"given":"M.","family":"Umamaheswari","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kavitha","family":"D","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s42452-024-05739-y","article-title":"Revolutionizing Somali agriculture: harnessing machine learning and IoT for optimal crop recommendations","volume":"6","author":"Abdullahi","year":"2024","journal-title":"Discov Appl Sci"},{"key":"ref9002","doi-asserted-by":"publisher","first-page":"144456","DOI":"10.1109\/ACCESS.2024.3471647","article-title":"Smart agriculture: current state, opportunities, and challenges","volume":"12","author":"Ahmed","year":"2024","journal-title":"IEEE Acc."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"123468","DOI":"10.1016\/j.eswa.2024.123468","article-title":"Automated weather forecasting and field monitoring using GRU-CNN model along with IoT to support precision agriculture","volume":"249","author":"Akilan","year":"2024","journal-title":"Exp. Syst. Appl."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"78686","DOI":"10.1109\/ACCESS.2023.3298215","article-title":"Artificial intelligence and internet of things for sustainable farming and smart agriculture","volume":"11","author":"Alzubi","year":"2023","journal-title":"IEEE Access"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1560","DOI":"10.22214\/ijraset.2024.63365","article-title":"IoT based smart farming system using machine learning","volume":"12","author":"Awasthi","year":"2024","journal-title":"Internat. J. Res. Appl. Sci. Engineer. Technol."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"162799","DOI":"10.1109\/ACCESS.2024.3486653","article-title":"Crop classification and yield prediction using robust machine learning models for agricultural sustainability","volume":"12","author":"Badshah","year":"2024","journal-title":"IEEE Acc."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"114352","DOI":"10.1109\/ACCESS.2023.3324722","article-title":"Machine learning and deep learning for plant disease classification and detection","volume":"11","author":"Balafas","year":"2023","journal-title":"IEEE Acc."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"634","DOI":"10.3390\/iot5040028","article-title":"Integrated IoT approaches for crop recommendation and yield-prediction using machine-learning","volume":"5","author":"Bouni","year":"2024","journal-title":"IoT"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"48","DOI":"10.3390\/digital5040048","article-title":"TinyML classification for agriculture objects with ESP32","volume":"5","author":"Donskoy","year":"2025","journal-title":"Digital"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1109\/TAFE.2025.3585065","article-title":"Multimodal data fusion by integrating IoT-enabled sensors and images for jamun crop disease detection with machine learning","volume":"3","author":"Garg","year":"2025","journal-title":"IEEE Transact. AgriFood Electron."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MIOT.2025.3575887","article-title":"Farm-LightSeek: an edge-centric multimodal agricultural IoT data analytics framework with lightweight LLMs","volume":"8","author":"Jiang","year":"2025","journal-title":"IEEE Internet. Thing. Magaz."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"141124","DOI":"10.1109\/ACCESS.2025.3594162","article-title":"AI-driven smart agriculture: an integrated approach for soil analysis, irrigation, and crop-fertilizer recommendations","volume":"13","author":"Khaliq","year":"2025","journal-title":"IEEE Acc."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"168412","DOI":"10.1109\/ACCESS.2024.3492973","article-title":"XAI-powered smart agriculture framework for enhancing food productivity and sustainability","volume":"12","author":"Martin","year":"2024","journal-title":"IEEE Acc."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"332","DOI":"10.11591\/ijres.v13.i2.pp332-340","article-title":"Agriculture data analysis using parallel k-nearest neighbour classification algorithm","volume":"13","author":"Muninarayanappa","year":"2023","journal-title":"Internat. J. Reconfig. Embedd. Syst."},{"key":"ref9016","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s43926-025-00119-3","article-title":"IoT and AI for smart agriculture in resource-constrained environments: challenges, opportunities and solutions","volume":"5","author":"Nawaz","year":"2025","journal-title":"Discov Internet Things"},{"key":"ref9017","doi-asserted-by":"publisher","first-page":"38","DOI":"10.58915\/aset.v3i1.787","article-title":"AI assisted and IoT based fertilizer mixing system","volume":"3","author":"Nik","year":"2023","journal-title":"Adv. Sustain. Technol."},{"key":"ref9018","doi-asserted-by":"publisher","first-page":"172910","DOI":"10.1109\/ACCESS.2024.3480868","article-title":"Toward precision agriculture: integrating machine learning techniques for smart farming systems","volume":"12","author":"Omer","year":"2024","journal-title":"IEEE Acc."},{"key":"ref9019","doi-asserted-by":"publisher","first-page":"124318","DOI":"10.1016\/j.eswa.2024.124318","article-title":"IoT-based prediction and classification framework for smart farming using adaptive multi-scale deep networks","volume":"254","author":"Padmavathi","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref9020","doi-asserted-by":"publisher","first-page":"e70213","DOI":"10.1111\/jph.70213","article-title":"HARNN-IoT: IoT-enabled hybrid deep learning model for cotton plant disease detection and classification","volume":"173","author":"Patil","year":"2025","journal-title":"J. Phytopathol."},{"key":"ref21","first-page":"2306","article-title":"IoT-based precision agriculture system using machine learning algorithms","volume":"8","author":"Priya","year":"2025","journal-title":"Internat. J. Scient. Res. Engineer. Develop."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1007\/s42452-025-07561-6","article-title":"Revolutionizing agriculture: a review of smart farming technologies for a sustainable future","volume":"7","author":"Raj","year":"2025","journal-title":"Discov Appl Sci"},{"key":"ref9023","doi-asserted-by":"publisher","first-page":"71","DOI":"10.3329\/jsr.v17i1.72958","article-title":"IoT aware smart agriculture using extreme learning classifier based predictive analytics in a cloud environment","volume":"17","author":"Sangeetha","year":"2024","journal-title":"J. Scient. Res."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"e70139","DOI":"10.1111\/coin.70139","article-title":"Spinalfgnet based intrusion detection for IoT enabled smart irrigation","volume":"41","author":"Saranya","year":"2025","journal-title":"Computat. Intellig."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"e607","DOI":"10.1002\/itl2.607","article-title":"Internet of things integrated deep-learning algorithms monitoring and predicting abnormalities in agriculture land","volume":"8","author":"Selvam","year":"2024","journal-title":"Internet Technol. Lett."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"101263","DOI":"10.1016\/j.atech.2025.101263","article-title":"An IoT-enabled AI system for real time crop prediction using soil and weather data in precision agriculture","volume":"12","author":"Sharafat","year":"2025","journal-title":"Smart Agricult. Technol."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"4","DOI":"10.34028\/iajit\/22\/6\/4","article-title":"Enhancing smart farming with IoT Sensors using FRPGW and HALSTM for Accurate Predictions","volume":"22","author":"ShreneShifna","year":"2025","journal-title":"Internat. Arab. J. Inform. Technol"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41314-025-00089-y","article-title":"Integrating visual and environmental modalities for rice leaf diseases classification and its severity prediction with dual-output CNN","volume":"10","author":"Singha Roy","year":"2025","journal-title":"J. Transform. Technol. Sustain. Dev."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1016\/j.procs.2024.04.215","article-title":"Smart agriculture using ensemble machine learning techniques in IoT environment","volume":"235","author":"Syed","year":"2024","journal-title":"Proc. Comp. Sci."},{"key":"ref9030","doi-asserted-by":"publisher","first-page":"38199","DOI":"10.1038\/s41598-025-22117-9","article-title":"IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases","volume":"15","author":"Verma","year":"2025","journal-title":"Sci Rep"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1878006\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:01:02Z","timestamp":1790748062000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1878006\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,30]]},"references-count":29,"alternative-id":["10.3389\/frai.2026.1878006"],"URL":"https:\/\/doi.org\/10.3389\/frai.2026.1878006","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,30]]},"article-number":"1878006"}}