[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127272-en":3,"doc-seo-127272-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127272,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Evaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System - A Comprehensive Review","Digital data growth is accelerating a transformation in agriculture through machine learning and smart farming. This comprehensive review examines how information and communications technology (ICT) supports precision agriculture and how machine learning models use historical and real-time environmental, soil, and operational data to predict suitable crops, detect diseases, and optimize irrigation. It further highlights autonomous vehicles and drones for precision planting, harvesting, and monitoring, while improving resource efficiency through energy and fertilizer optimization and climate-resilient practices. The work connects these capabilities to productivity gains and reduced environmental impact.","Received 16 March 2024, accepted 26 March 2024, date of publication 17 April 2024, date of current version 3 May 2024. Digital Object Identifier 10.1109/ACCESS.2024.3390581  \nEvaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System: A Comprehensive Review  \nGHULAM MOHYUDDIN 1, MUHAMMAD ADNAN KHAN2, ABDUL HASEEB3, SHAHZADI MAHPARA 1, MUHAMMAD WASEEM4,(Member, IEEE),  \nAND AHMED MOHAMMED SALEH5  \n1Department of Agriculture, Ghazi University, Dera Ghazi Khan, Punjab 32200, Pakistan  \n2Department of Electrical Engineering, HITEC University, Taxila, Punjab 47080, Pakistan  \n3LUMS Energy Institute, Department of Computer Science, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore 54792, Pakistan  \n4International Renewable and Energy Systems Integration Research Group (IRESI), Department of Electronic Engineering, Maynooth University, Kildare, Maynooth, W23 F2H6 Ireland  \n5Faculty of Engineering, University of Aden, Aden, Yemen  \nCorresponding author: Ahmed Mohammed Saleh ([engahmedsaleh14@gmail.com](engahmedsaleh14@gmail.com))  \nThis research was funded by the European Union through the Horizon Europe framework under Grant 101075582 .  \nABSTRACT In the era of digital data proliferation, agriculture stands on the cusp of a transformative revolution driven by Machine Learning (ML) . This study delves into the intricate interplay between Information and Communications Technology (ICT) and conventional agriculture, emphasizing the role of ML in reshaping farming practices. With the ongoing data tsunami impacting data-driven businesses, the fusion of smart farming and precision agriculture emerges as a beacon of innovation. ML algorithms, analyzing historical and real-time environmental data, soil conditioning, predicts suitable crop for maximum yields, detect diseases, and optimize irrigation in smart farming, facilitating informed decision-making. Precision agriculture benefits from autonomous vehicles and drones, driven by ML, ensuring precision in planting, harvesting, and crop monitoring. Resource efficiency increases as ML optimizes energy consumption, manages fertilizer application, and promotes climate-resilient practices. This comprehensive assessment underscores ML’s pivotal role in maximizing productivity, minimizing environmental impact, and navigating the complexities of modern agriculture.  \nINDEX TERMS Smart agriculture, precision farming, machine learning, unmanned aerial vehicles, artificial intelligence.  \nI. INTRODUCTION  \nThe 6.4% of global Gross Domestic Product (GDP) comes from agriculture, making it the primary source of both food supply and economic growth. In nine nations throughout the world, agriculture is a major economic driver. Energy and employment for millions of people are provided by the agricultural sector [1] . Supplying the demands of the world’s population would need a threefold rise in yearly wheat output and a more than twofold increase in annual meat  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Michele Nappi  .  \nproduction by 2050 [2] . Increases in grain production yields will provide a steady supply. It is necessary to adopt a more modern perspective on farming and to expand the scale of your crop production. The question of whether or not this can be accomplished in a way that is both sustainable and welcoming remains open. But re-engineering the agricultural operations at massive size and speed calls for a major and quick transition. Because of climate change and population expansion, the agricultural industry is looking for ways to apply new technologies to increase yields. As Artificial Intelligence (AI) advances, it is being put to more useful use in the agricultural sector. Concurrently, the Fourth Industrial  \nVOLUME 12, 2024  \n􀀊 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.  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