ANALYZING MACHINE LEARNING METHODS TO ENHANCE GRAIN QUALITY ASSESSMENT AND EVOLUTION

Abstract

Assessing grain quality is a crucial component of agricultural production, with significant implications for global food security and economic prosperity. With the increasing demand for top-tier grains, there arises a pressing need for sophisticated methodologies to aid in the ongoing assessment and monitoring of grain quality throughout its development. This paper offers a comprehensive examination of machine learning (ML) techniques utilized in grain quality evaluation. Through an analysis of recent research advancements, methodologies, and practical applications, this review sheds light on the effectiveness and potential obstacles associated with ML-driven approaches for enhancing grain quality assessment. Key areas of focus include the deployment of ML algorithms for predicting, classifying, and monitoring grain quality, as well as the incorporation of advanced sensing technologies and data analytics into grain quality assessment systems. Additionally, the review delves into emerging trends, future research avenues, and the broader implications of ML techniques in streamlining grain production processes and ensuring food safety and sustainability. By conducting a systematic review of existing literature, this paper contributes to a deeper comprehension of the role played by ML in addressing the multifaceted challenges of grain quality assessment and management within contemporary agriculture.