Fruits and Vegetables Freshness Categorization Using Deep Learning

Abstract

The nutritional value of perishable food items, such as fruits and vegetables, depends on their freshness levels. The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only. We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories: purefresh, medium-fresh, and rotten. We gathered a dataset comprising of 60K images of 11 fruits and vegetables, each is further divided into three categories of freshness, using hand-held cameras. The recognition and categorization of fruits and vegetables are performed through two deep learning models: Visual Geometry Group (VGG-16) and You Only Look Once (YOLO), and their results are compared. VGG-16 classifies fruits and vegetables and categorizes their freshness, while YOLO also localizes them within the image. Furthermore, we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree. A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset. The dataset is publicly available for further evaluation by the research community.

An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network

“…In view of these limitations of artificial feature extraction combined with machine learning in the freshness detection task of fruits and vegetables, some scholars have considered using end-to-end deep learning methods ( Fahad et al, 2022 ). These deep learning methods can learn feature representations directly from raw data, thereby reducing reliance on manual feature engineering to achieve better performance and generalization capabilities ( Mukhiddinov et al, 2022 ).…”

“…These deep learning methods can learn feature representations directly from raw data, thereby reducing reliance on manual feature engineering to achieve better performance and generalization capabilities ( Mukhiddinov et al, 2022 ). Fahad et al (2022) proposed two deep learning models, VGG-16 and YOLO, to automatically identify and classify fruits and vegetables. The method first identifies the category of the object in the image and then classifies the fruit or vegetable into one of three categories: fresh, medium fresh and rotten.…”

Automated Fruit Identification using Modified AlexNet Feature Extraction based FSSATM Classifier

“…Marketing, dataset analysis are just a few of the many fields that have found success with the use of AI and ML techniques [12]. Consequently, several researchers have been interested in applying proven methods to automated fruit categorisation because to the fast advancements in learning, especially in the last 10 years [13].…”

A Machine Learning-Assisted Three-Dimensional Image Analysis for Weight Estimation of Radish

“…There are several methods for measuring the quality of fruits and vegetables, including methods using information regarding the color of appearance [7][8][9][10][11][12][13][14], shape [2,15], and gas emitted from vegetables and fruits [16]. The method using gas generated from vegetables and fruits as a quality indicator is not easy to perform in practice because of the effects of contamination and dilution during the gas measurement.…”