Classification of Vegetable Types Using the Convolutional Neural Network (CNN) Algorithm - Wilis Arum Karunia; Safriya Murni Puspita; Dwi Rolliawati; Ahmad Yusuf (LPPM Universitas Bina Sarana Informatika, 2025)
This study aims to classify vegetable types using the Convolutional Neural Network (CNN) algorithm with a dataset encompassing 15 vegetable classes and a total of 31,000 images. By utilizing the TensorFlow and Keras libraries, the CNN model was designed with convolutional, pooling, and dense layers to recognize visual features such as color, texture, and shape. The results indicate a highest validation accuracy of 95.83% and a testing accuracy of 93%. This research contributes to the application of the CNN algorithm for image classification and demonstrates its potential in handling multi-class datasets effectively. However, since the vegetables used have very distinct shapes and textures, this study is more relevant in the context of the technical application of the CNN algorithm rather than practical benefits. The research would be more impactful if applied to vegetables with similar shapes and characteristics, thereby supporting farmers or individuals studying vegetable traits in greater depth. Additionally, such an approach could address challenges in differentiating visually similar vegetable types, making the technology more valuable in real-world agricultural or educational settings.