Abstract
Abstract: Vegetables are an essential component of a healthy diet and play a critical role in promoting overall health and well-
being. Vegetables are rich in important vitamins and minerals, including vitamin C, folate, potassium, and iron. They also provide
fiber, which helps maintain digestive health and prevent chronic diseases. We are proposing a deep learning model for the
classification of vegetables. A dataset was collected from Kaggle depository for Vegetable with 15000 images for 15 different classes.
The data was preprocessed, normalized and split into three sets (train, valid, test). The proposed model was trained and validated
using the train and valid sets and accuracy of both training and validation was very high. For the evaluation of the proposed model
we utilized these metrics: accuracy, F1-score, precision, Recall and time required for testing. We then tested the proposed model
using the test set. The result of the testing was accuracy (99.95%), F1-score (99.95%), precision (99.95%), Recall (99.95%) and
time required for testing the test set was 1.38 seconds.