MKS Publication

Chapter 2

Artificial Intelligence and Machine Learning: Principles and Applications to Agricultural Produce Quality Determination

Abstract/Preface

The growing use of Artificial Intelligence (AI) and Machine Learning (ML) is changing agricultural production systems worldwide. These technologies offer new solutions to various long-standing issues in agriculture, especially in assessing produce quality. Historically, quality evaluation depended on manual inspections and lab methods, which can be slow, subjective, and often damaging. AI-driven methods provide a faster, more accurate, non-invasive, and cost-effective way to assess quality. This chapter explains the basic concepts and principles of AI and ML and explores how these technologies are used to determine the quality of agricultural products. Important tools and techniques such as machine vision systems, deep learning models, sensor technologies, predictive analytics, and intelligent decision-support systems are considered. The chapter also highlights real-world applications for evaluating fruits, vegetables, grains, and livestock products through selected case studies. Lastly, it addresses the current challenges in using AI for agricultural quality assessment and discusses future opportunities for enhancing smart, data-driven quality management systems in farming.