Evaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System - A Comprehensive Review

Aug 5, 2026

Upload by Quinn

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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages30

Reading Time1 h 16 min

AccessFree to download

Introduction

Smart agriculture and precision farming: definition

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 Evaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System: A Comprehensive Review GHULAM MOHYUDDIN 1, MUHAMMAD ADNAN KHAN2, ABDUL HASEEB3, SHAHZADI MAHPARA 1, MUHAMMAD WASEEM4,(Member, IEEE), AND AHMED MOHAMMED SALEH5 1Department of Agriculture, Ghazi University, Dera Ghazi Khan, Punjab 32200, Pakistan 2Department of Electrical Engineering, HITEC University, Taxila, Punjab 47080, Pakistan 3LUMS Energy Institute, Department of Computer Science, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore 54792, Pakistan 4International Renewable and Energy Systems Integration Research Group (IRESI), Department of Electronic Engineering, Maynooth University, Kildare, Maynooth, W23 F2H6 Ireland 5Faculty of Engineering, University of Aden, Aden, Yemen Corresponding author: Ahmed Mohammed Saleh ([engahmedsaleh14@gmail.com](engahmedsaleh14@gmail.com)) This research was funded by the European Union through the Horizon Europe framework under Grant 101075582 . ABSTRACT 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. INDEX TERMS Smart agriculture, precision farming, machine learning, unmanned aerial vehicles, artificial intelligence. I. INTRODUCTION The 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 The associate editor coordinating the review of this manuscript and approving it for publication was Michele Nappi . production 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 VOLUME 12, 2024 􀀊 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

FAQs

How does machine learning support precision farming in smart agriculture systems?

Machine learning analyzes historical and real-time environmental and soil data to predict suitable crops, detect diseases, and optimize irrigation for better decisions and yields.

What roles do autonomous vehicles and drones play in precision agriculture?

Driven by machine learning, they enable precision planting, harvesting, and crop monitoring, improving operational accuracy across farming tasks.

How does the review connect smart agriculture with sustainability and resource efficiency?

It explains that machine learning optimizes energy consumption and fertilizer application, supporting climate-resilient practices while reducing environmental impact.

Aug 5, 2026

Upload by Quinn