Local Vegetable Freshness Classification Using Transfer Learning Approaches

Abstract

A vegetable’s quality performs a significant role in customer consumption. At the same time, the categorization of vegetable freshness is crucial for the food industry. Freshness is a key indicator of vegetable quality that has a direct impact on human physical well-being and desire to make purchases. Initially, identifying the freshness condition of vegetables and distinguishing among fresh, aged, and rotten vegetables by the observation of vegetable’s outer shell manually is very difficult for humans. That can be eradicated by replacing the monitoring system with an automated computer program. An automatic fresh vegetable detection system is proposed using the Densenet201 Transfer Learning model in this study. The primary goal of this research is to identify a vegetable’s freshness condition by observing the outer shell and differentiate a fresh vegetable from a rotten one. Five types of vegetables are divided into three classes using custom datasets for vegetable freshness classification using different transfer learning models. However, DenseNet201 performed enormously enough on the vegetable dataset which achieved a test accuracy of 98.56%. Thus, this study will attempt to assist in reducing the reliance on the human eye and accurately identify the freshness conditions of a vegetable.