Abstract: Olives come in a number of external and internal varieties. The Shengeh kind, which is available in three colours—green, brown, and black—was chosen at random by the researchers to ensure that the sample was diverse. To avoid discoloration throughout the experiment, 150 healthy olives were harvested and stored correctly. These olives had not been subjected to any external harm, such as crushing or milling. The particular kind of olives were kept chilled at 2°C and preserved in water. This study investigates the possibility of grading Shengeh cultivars from olives that have different uses, based on color using image processing. After preparing images of olives using MATLAB software and image processing techniques, olives are graded based on their color in three categories: immature with green, semi-ripe with brown, and ripe with black. The results showed that image processing technology can be used to grade olives of the Shengeh type in terms of their ripeness as a single-color grain with acceptable accuracy. The HSV color space is one of the best color spaces to separate the colors of the olive cultivar. The accuracy of the software for detecting olives with the mentioned degrees is 98%, 96%, and 100%, respectively.

Dongliang Jin, “Develop an Olive-based Grading Algorithm using Image Processing” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140483

@article{Jin2023,
title = {Develop an Olive-based Grading Algorithm using Image Processing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140483},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140483},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Dongliang Jin}
}