COMPUTATIONAL MODEL OF COCONUT MATURITY DETECTION USING YOLO AND ROBOFLOW

Abstract

Currently there is no technology in the market to identify the maturity of the coconut. Coconut is not just used in food but also used in medical Coconut is taken by mouth for bladder stones, diabetes, high cholesterol, and weight loss. In foods, coconut is used in various preparations. The sweetness consistency of coconut is closely related to the quality of coconut byproducts. And this is due to multiple factors. One of which is the maturity of coconut. As a result, maturity classification is the primary concern for both customers and manufacturers. However, the maturity rating process is now based primarily on individual experience and skills earned through time in the sector. Our research proposes an automatic maturity classification with deep learning which could minimize the maturity classification time cost. The experimental results showed that our proposed comparison between YOLOv7 and Roboflow, Roboflow Algorithm achieved the best validation accuracy results.