Journal Article

A Design of Deep Learning Experimentation for Fruit Freshness Detection

Pardamean, Bens, Valentino, Febrian, Cenggoro, Tjeng Wawan

2021

Overview

Indonesia is a country with a tropical climate so that fruit and vegetable plants can grow easily in Indonesia.Fruits have many good nutrients such as vitamins, proteins and others. But the fruit also has a period where the fruit is said to be fresh fruit.During this time there are still many fruit supplier companies that send fruit unfit for consumption due to lack of accuracy in the process of sorting the fruit when the fruit is taken from the plantation and the entry of other fruit into an improper packaging. Thus, it makes detecting food spoilage from the production stage to consumption is very important. We propose a design of computer vision-based technique usingdeep learning with the Convolutional Neural Network (CNN) model to detect fruit freshness. The specially designed CNN model is then evaluated with public datasets of fruits fresh and rotten for classification derived from Kaggle.

• Publisher: IOP Publishing
• Subject: Artificial neural networks / Computer vision / Deep Learning / Detection / Experimentation / Flowers & plants / Food spoilage / Freshness / Fruit / Fruits / Image Classification / Machine learning / Neural networks / Nutrients / Spoilage / Vitamins