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Transforming Agriculture Through Machine Learning
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Author(s): V. Sathiyamoorthi (Government Polytechnic College, Dharmapuri, India), Lakshmi Chandrakanth Kasireddy (ThoughtSpot Inc., USA), Richard William A. (Rajarajeswari College of Engineering, Bangalore, India), O. Josna (Rajarajeswari College of Engineering, Bangalore, India), R. Gopi (Dhanalakshmi Srinivasan Engineering College, India)and Francis Shamili S. (Dhanalakshmi Srinivasan Engineering College, India)
Copyright: 2027
Pages: 18
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/408690
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Abstract
This article presents a series of case studies that demonstrate the practical implementation and impact of Machine Learning (ML) technologies across diverse domains within the agricultural sector. Each case study explores how ML algorithms—ranging from decision trees and support vector machines to deep learning models—are being used to solve real-world agricultural challenges such as crop yield forecasting, soil quality assessment, pest and disease detection, weed classification, precision irrigation, and commodity price prediction. By analyzing field-level data, satellite imagery, and sensor inputs, these applications highlight measurable improvements in productivity, cost-efficiency, and environmental sustainability. Challenges related to data acquisition, model accuracy, scalability, and user adoption are critically examined. Through these real-world examples, the article provides a comprehensive understanding of the transformative potential of ML in reshaping modern agriculture and paving the way for smart and sustainable farming practices.
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