Deep transfer learning CNN based for classification quality of organic vegetables

Abstract

This study introduces a system based on a Convolutional Neural Network (CNN) with deep transfer learning for classifying organic vegetables. It aims to evaluate their quality through artificial intelligence. The approach involves three key steps: collecting data, preparing data, and creating data models. Initially, the data collection phase involves gathering images of organic vegetables from packing facilities, organizing these images into training, testing, and validation datasets. In the preparation phase, image processing techniques are applied to adjust the images for training and testing, resizing each to 224 x 224 pixels. The modeling phase involves using these prepared datasets, which include 3,239 images of two types of organic vegetables, to train the model. The study tests the model's effectiveness using three CNN architectures: Inception V3, VGG16, and ResNet50. It finds that the Inception V3 model achieves the highest accuracy at 85%, VGG16 follows with 82% accuracy, and ResNet50 has the lowest accuracy at 50%. The results suggest that Inception V3 is the most effective at accurately classifying organic vegetables, while VGG16 shows some limitations in certain categories, and ResNet50 is the least effective.