Classification of Papaya Fruit Ripeness Based on Color Using a Convolutional Neural Network
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Abstract
Advancements in digital image processing technology have significantly simplified human life, leading to the development of numerous applications across various fields. Digital image processing methods can transform input images into output images, which can then be utilized to identify and classify objects. Papayas are a fruit frequently consumed by humans; traditionally, the harvesting process relies on visual assessment, specifically by observing the fruit's color and size. In this study, RGB values were collected from several papaya samples and processed using a convolutional neural network (CNN) to determine the fruit's ripeness level. The ultimate goal of this research is to develop an application capable of identifying and classifying such objects. System testing yielded a success rate of 95%.
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