Improve Unsupervised Machine Learning Model on Fruits by Using VGG-16

Abstract

The demand for efficient fruit classification has risen with supply chain complexities. For the sake of saving the labor force and improving efficiency, applying machine learning algorithms on fruit categorization to multiple stages in the factory is a feasible solution. The paper briefly delivers the rationale of algorithm VGG-16 and validates its advantage in accuracy by comparing it with other convolutional neural network models. Traditional convolutional network reaches its peak accuracy which is around 40% after 45 training epochs. VGG-16 results with a 97% percent accuracy on the classification of more than 20 kinds of fruits only after 6 epochs and can be improved further by enriching and augmenting the dataset. However, although VGG-16 only requires a small number of epochs, the weighted parameter for each layer increases dramatically and increases the running time. Future researchers should focus on optimizing the algorithm to make it more feasible in the industry. Also, remote computing might be a solution for large computational requirements.