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( Sijan Karki ),( Jayanta Kumar Basak ),( Bhola Paudel ),( Nibas Chandra Dev ),( Hyeon Tae Kim ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.1
Color space is a fundamental concept in image processing and machine learning, as it plays a crucial role in determining how an image is represented and processed by the computer. The choice of color space can have a significant impact on the performance of machine learning algorithms, as different color spaces have different properties and emphasize different features. Therefore, this study aimed to evaluate the efficacy of machine learning models in classifying strawberry ripeness stages using color spaces: RGB, HLS, HSV, CIELab*, and YCbCr. The study results indicate that the four ripeness stages: unripe, semi-ripe, ripe and over-ripe exhibited significant differences in biochemical and color features. While the Unripe stage was the most correctly classified stage, the Semi-ripe stage was the most challenging. The Feed Forward Artificial Neural Network using the CIELab* colour space was the most successful in classifying ripeness stages with an average accuracy of 96.7%. This combination with other features, which indicate fruit ripeness, may be utilized in the automatic detection of strawberry ripeness.