Image-Based Tomato Maturity Classification and Detection Using Faster R-CNN Method
Sigit Widiyanto, Dini Tri Wardani, Singgih Wisnu Pranata
Abstract
Tomato is one of the cultivations that is often used for gardening. Tomato also has high demands on the market as it’s used for daily needs and occurred for many cuisines. Tomato comes in several colors such as red, orange, and green. Their color could tell their maturity levels too. Tomato grows in several quantities even only on one branch. So as the technologies grow, the computer also could be trained to understand what tomato is and how does it look like. Using computer vision, the computer could tell tomatoes according to their color. For this study, the computer will be trained using Faster R-CNN models to recognize the tomato maturity as Faster R-CNN known support for the image classification and object detection. The accuracy for classification in validation stage about 98,70% in average. For the object detection the model has confidentiality about 96,20% to detect the tomato maturity.