Chili crop segregation system design and development strategies - Wan Daud, Wan Mohd Bukhari; Abdul Aziz, Mohd Fareezuan; Ahmad Izzuddin, Tarmizi; Norasikin, Mohd Adili; Abdul Rasid, Ahmad Fuad; Wakhi Anuar, Nur Farah Bazilah; M. N. Sukhaimie (Penerbit Universiti Teknikal Malaysia Melaka, 2021)
An automation process is a need in the agricultural industry specifically chili crops, that implemented image processing techniques and classification of chili crops 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. 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 on the Machine Learning (ML) technique, which is the 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.