Classification of Tomato Ripeness Based on Convolutional Neural Network Methods

Abstract

Sorting system for tomato is one of the important things to deploy to achieve better quality of tomato. Nowadays, many sorting system is done manually and this could spend a lot of time and become inefficient. One method can be implemented in the sorting system by using Convolutional Neural Network (CNN) method to classify the ripeness of tomatoes. The objective of this research is to classify the ripeness of tomatoes based on the color of tomatoes. There are three categories of color level such as green for raw tomato, turning for half-ripe tomato and red for ripe tomato. Research methodology of this research is data collection, data pre-processing and image maintenance, CNN model, and training data. The image used in this research are 1148 images. These images were taken manually using smartphone camera in outdoor environment. These images were used to build CNN model. The results of this research show that by testing 10 images of tomatoes achieved raw tomatoes close to 90%, ripe tomatoes close to 90% and half-ripe tomatoes close to 80%. Based on the results, CNN can be used as a good alternative in image classification tasks.