Herb Classification with Convolutional Neural Network

Abstract

Herbs are plants with savory or aromatic properties that are widely used for flavoring, food, medicine or perfume. The worldwide use of herbal products for healthcare has increased tremendously over the past decades. The plethora of herb species makes recognizing the herbs remains a challenge. This has spurred great interests among the researchers on pursuing artificial intelligent methods for herb classification. This paper presents a convolutional neural network (CNN) for herb classification. The proposed CNN consists of two convolution layers, two max pooling layers, a fully-connected layer and a softmax layer. The ReLU activation function and dropout regularization are leveraged to improve the performance of the proposed CNN. A dataset with 4067 herb images was collected for the evaluation purposes. The proposed CNN model achieves an accuracy of above 93% despite the fact that some herbs are visually similar.