In this project, I built an effective image classifier for the most common vegetables found across the globe. For image classification, I employed the use of a Convolutional Neural Network (CNN) in tandem with a few popular preprocessing steps. This project was purely meant as a learning experience so there is no defining problem statement to support this project. Instead, my goal was to tune and improve my model to maximize performance. The final iteration achieved a training accuracy of 96.73%.
The dataset used can be found on Kaggle here. The data consists of 15 types of common vegetables found throughout the world. The vegetables are- bean, bitter gourd, bottle gourd, brinjal, broccoli, cabbage, capsicum, carrot, cauliflower, cucumber, papaya, potato, pumpkin, radish and tomato. A total of 21000 images from 15 classes are used where each class contains 1400 images of size 224x224 and in *.jpg format. The dataset split 70% for training, 15% for validation, and 15% for testing purpose.