CLASSIFICATION OF AMBARELLA FRUIT RIPENESS BASED ON COLOR FEATURE EXTRACTION

Saragih, Raymond Erz and Gloria, Dessy and Santoso, Albertus Joko CLASSIFICATION OF AMBARELLA FRUIT RIPENESS BASED ON COLOR FEATURE EXTRACTION. ICIC Express Letters, 15 (9). pp. 1013-1020. ISSN 1881-803X

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Text (Raymond Erz Saragih, Dessy Gloria and Albertus Joko Santoso) 01. Classification of Ambarella Fruit Ripeness Based on Color Feature Extraction.pdf Download (973kB) | Preview

Abstract

In the food industry, determining fruit maturity is very important to obtain fruit of good quality. The ripeness of some fruits can be determined by the color of the skin. Like several other types of fruit, the skin color can be used to determine the ripeness of Ambarella fruit. However, determining the ripeness of Ambarella fruit is still done manually by human labor, which is considered time-consuming, tiring, requires a lot of workers, and can cause inconsistencies. The development of technology such as computer vision allows the determination of fruit ripeness to be carried out automatically, accurately, and relatively quickly. This study aims to classify the ripeness of the Ambarella fruit based on its color feature with the use of machine learning. The color features used are RGB, HSV, and L*a*b*, with Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as the classi�ers. Segmentation was done using the Otsu method. Google Colab, OpenCV, and scikit-learn are utilized in carrying out the experiments. The performance result shows that the highest accuracy, precision, recall, and f-measure were achieved by using SVM on the L*a*b* color feature.

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