Machine Vision Systems for Post-Harvest Quality Assessment

Abstract

The postharvest quality assessment of agricultural commodities such as fruit, vegetables, and cereal are a global concern. The complicated process of ensuring food quality and safety involves subjective perception which is full of biasness and labour intensive. These procedures are very time-consuming. Nowadays enterprises and researchers can benefit immensely from machine vision advancements in increasing the productivity of agricultural products. Consequently, machine vision has widespread use in all facets of the agriculture industry. The key function of the machine vision system is image processing. Deep learning and machine learning models can be used in image processing to efficiently determine the kind and caliber of the agriculture sector for the postharvest quality classification of different crops such as fruits, vegetables, and cereals.