Classification of Dried Chilli Quality Using Image Processing

Abstract

Chillies are a high-value export commodity. One of the chilli products that has a high export value, such as to the European market, is dried red chillies. Dried chillies' quality is the main parameter that must be maintained if this commodity is to be continuously exported. This study aimed to precisely and accurately classify the quality of dried chillies using physical parameters of the length and color of chillies based on digital image processing. In this research, the quality of dried red chillies was classified using a combination of digital image processing and artificial neural networks (ANN). The quality parameters of dried chillies used as inputs were chilli length, mean energy, mean a∗, mean blue, and mean contrast. This study used 150 dried chillies for training data and 36 samples for test data. The classification of the quality of dried red chilli was divided into three classes, which were extra class, class I, and class II. The result of this study was an artificial neural network structure consisting of 5 input layer cells, 16 hidden layer cells, and two output layer cells. The testing of the system using 36 testing data that determined the values of dried chillies reached 94.4%.