Advancing legume quality assessment through machine learning: Current trends and future directions

Legume postharvest assessment is a critical component of maintaining quality, enhancing nutritional value, and ensuring the produce meets market requirements. The traditional methods for estimating legume quality are not effective in terms of accuracy, scalability, and efficiency. Machine Learning (ML) has come forward as a very transforming solution that makes use of advanced algorithms combined with intelligent sensors for the optimization of legumes processes. This review paper targets tracking the metamorphic role of ML in qualification related to legumes postharvest processing (PTP). Sorting, defect detection, nutritional evaluation, authentication, and monitoring moisture-the different stages at which legumes have been qualified by the use of ML-are discussed herein. In addition, this paper highlights advanced ML techniques, especially their interaction with other intelligent sensors, as in the case of machine vision and spectroscopy systems. In this respect, the paper is the roadmap for leveraged applications of ML to improve legume quality assessment across the entire process chain. It identifies best practices, innovative methodologies, and practical applications that form the basis of actionable insight into enhancing quality control processes.