Fruit and Vegetable Classification using Convolutional Neural Network with MobileNetV2
DOI:
https://doi.org/10.61098/jarcis.v2i2.197Keywords:
fruit, vegetable, classification, CNN, MobileNetV2Abstract
Fruits are parts of plants that originate from the plant's pistils and usually contain seeds. Meanwhile, vegetables are leaves, legumes, or seeds that can be cooked. Fruits and vegetables have many variations that can be distinguished based on color, shape, and texture. However, the development of Artificial Intelligence (AI) technology has become pervasive in everyday life, one aspect of which is demonstrated through deep learning, a method of AI learning. Therefore, developing deep learning for tasks such as automatically detecting surrounding objects is necessary. This study aims to classify types of fruits and vegetables by applying a Convolutional Neural Network (CNN) with the MobileNetV2 architecture. In this study, fruits and vegetables encompassing 36 categories, including significant types in daily life, were considered. The results show that the classification system achieved an excellent accuracy rate of 97.31%, demonstrating the effectiveness of using deep learning techniques for this application
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