A Comparative Study of Convolutional Neural Network Model for Chinese Flower Tea Classification

Abstract

While flower teas are well-known for their health benefit, little did people know, there are several types of flower tea, and each type has its health benefit. Due to the unavailability of an automated system for classifying Chinese flower tea at the meantime, we then decided to apply the Convolutional Neural Network to help the wider community or flower tea plantation owners to classify flower tea more quickly, accurately, and automated. The purpose of this research is to classify flower tea based on their type by using CNN algorithm. In this research, we used multiple CNN models to find the most suitable architecture. The CNN models compared are ResNet50, SqueezeNet, AlexNet, and ResNet18. The result indicates AlexNet to achieve the highest accuracy of 97.92%