Robust Maturity Level Classification of Bell Pepper using CNN - Fadhillah, Rino Aziz; Irwansyah, Irwansyah (International Journal of Informatics and Computation, 2025)
The development of artificial intelligence has opened up new opportunities in various fields, including Bell Pepper image detection. The remaining issues are that the selection of the ripeness level of bell peppers manually can take a long time and requires more accuracy. The purpose of this research is to classify the maturity level of bell peppers and to determine the level of accuracy. The research method used is Convolutional Neural Network (CNN) with tools or tools, namely Visual Studio Code in Python with TensorFlow Framework, as well as a pre-trained CNN architecture called VGG16. Bell peppers are divided into 3 levels of ripeness with different types of colors: green (unripe), yellow (half-ripe), and red (ripe). The results showed that in classifying the maturity level of bell peppers with an accuracy of 89%, precision 84%, recall of 83%, and F1-Score of 84%.