Chili Crop Segregation System Design and Development Strategies

Abstract

An automation process is a need in the agricultural industry specifically chili crop, that implemented image processing technique and classification of chili crop usually based on their color, shape, and texture. The goal of this study was to review the development of a portable sorting machine that will be able to segregate chili based on their color. The Digital Image Processing (DIP), which is a crucial part to perform the Feature Extraction process was discussed with the elaboration of steps to execute the DIP process. Besides, the analysis of different methods to extract the chili color based on the RGB color component was included. This paper focused more Machine Learning (ML) technique, which is a main component of Artificial Intelligence. The image data taken from chili samples can be trained by using Learning Algorithm in the MATLAB program. The performance of the trained network then can be evaluated by using the Confusion Matrix technique. The methods that have been reviewed in this paper were general enough to be used in the agricultural industry that requires a high volume of chili crops and with other differentiating features to be processed at the same time. Improvements can be made to the sorting system but will come at a higher price.