Postharvest Quality Classification of Fruits Using Machine Learning – A Review

Abstract

India is the second largest producer of the fruits after China, but due to poor postharvest management huge quantity of the fresh produces are wasted. The quality of fruits significantly determines their processing quality, marketability, and consumer acceptability. Fruit quality classification using the conventional methods are more challenging, labor-intensive, error-prone, and inconsistent in accuracy and reliability. A machine vision system provides a swift and specific method for fruit quality classification. Machine vision systems have the huge potential to replace subjective classification. It revolutionizes the fruit processing, storage, and export industries as an important tool for rapid and precise classification. Therefore, in the foreseeable future, machine vision systems have the potential to offer an efficient and accurate solution for fruit quality classification, variety identification, maturity classification, and disease identification. This review focused on the advancement of machine vision system for the postharvest classification of fruits.