Classification system of banana types and ripeness levels based on convolutional neural network - Jambola, Lucia; Darlis, Arsyad Ramadhan; Malaha, Windi; Aryanta, Dwi (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
Recently, the availability of bananas in supermarkets has been relatively abundant. However, most buyers experience problems categorizing the type and level of ripeness of bananas, so the level of purchases of this fruit decreases. This study implements a system that can automatically classify bananas based on type and level of ripeness so that buyers can choose them based on their needs. In this study, the proposed system could classify the types and degrees of banana ripeness using a Convolutional Neural Network (CNN) where the system was implemented in real-time using the hardware of the Jetson Nano as a processing unit and a camera system as a sensor. The methodology adopted in this research involves implementing CNN architectures, i.e., ResNet-18 and ResNet-50, under various conditions. The training phase comprises 60 epochs, while testing considers illumination parameters from LED lights with power of 6 watts, 12 watts, and 22 watts under distances ranging from 10 to 100 cm. The results show that the system could classify the type and level of ripeness of bananas in real-time with an accuracy of 93% that is achieved using the 22-watt power for all type and ripeness levels.