CLASSIFICATION OF SUGAR LEVELS IN BANANA FRUIT BASED ON COLOR FEATURES USING DIGITAL IMAGE PROCESSING-BASED ARTIFICIAL NEURAL NETWORKS
Abstract
Bananas are a fruit that has many benefits for human health, because bananas contain a source of vitamins, minerals and carbohydrates. Bananas are a fruit that is often consumed by Indonesian people because of their sweet taste. With this sweet taste, of course bananas have quite high sugar levels, so diabetes sufferers must pay attention to this when choosing bananas. The level of sugar content in bananas can be distinguished by looking at the ripeness of the fruit. To differentiate between them, of course, we use human vision, but human observation also has weaknesses and errors can occur in the process, whether due to lack of lighting, visual impairment, or age. Therefore, this study proposes a classification of the level of sugar content in bananas in the RGB color space using artificial neural networks (ANN). The proposed method consists of 6 stages, namely image acquisition, preprocessing, segmentation, morphological operations, RGB feature extraction, and classification stage. In this study, 300 samples of banana fruit images were used. 210 datasets will be used for training and 90 datasets for testing. The dataset is divided into 3 classes, namely low sugar content, medium sugar content, and high sugar content. Based on the test results that have been carried out, the accuracy of the classification results is 97.78%, the misclassification is 2.22%, and the computing time is 375 seconds. These results show that the proposed method can accurately classify the level of sugar content in bananas.
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