Deep learning and computer vision approach - a vision transformer based classification of fruits and vegetable diseases (DLCVA-FVDC).

As technology progresses, automation gains importance. The automation might be in a large-scale industry with more employees and heavy capital investments, or maybe a small-scale industry in which manufacturing, providing the services, and productions are performed on a smaller scale or micro scale, everywhere the automation gains importance. Similar to this is the food processing industry, where fruits and vegetables are some of the most popular products that enhance our health and help us to stay fit. In this approach, we have developed a framework that classifies fruits and vegetables using computer vision and deep learning-based methods. We test the proposed framework on Kaggle's fresh/stale images of fruits and vegetables dataset and IEEEDataPort's FruitsGB dataset. Experiments were conducted in multiple trials to extract model parameters and were analyzed to classify the fruits and vegetables as fresh/stale. The classification depends on the selection of the optimizer and varying the hyperparameter value like batch size, learning rate, kernel size, number of kernels, patch size, etc. The proposed custom CNN model achieves the highest classification accuracy of 97.65% and 95.86% using Kaggle's and FruitsGB datasets, respectively. Similarly, in the second approach, the vision transform (ViT) achieves the highest classification accuracy of 98.34% and 96.75% on the same datasets, respectively. The results of these methods outperform the results of the baseline algorithm used in the classification of the images. [ABSTRACT FROM AUTHOR]