Tea Leaf Disease Classification using Domain Adaptation Method

Abstract

Tea trees are extremely vulnerable to diseases and insect pests in the growth process, which seriously affects the yield and quality of tea leaves. This requires the identification and treatment of infected tea leaves in time. With the rapid development of artificial intelligence and computer vision, there is a new trend to identify the images by computer. However, there often exists environmental changes such as illumination intensity, sample angles and background in the leaf images collected in natural scenes, which results in the difference of data distribution. This makes the traditional deep learning method unable to solve the problem of cross-domain classification very well, thus seriously affecting the accuracy of classification. This study mainly focuses on the cross-domain task with unaligned data distribution. A domain-adaptive based method was proposed to realize the cross-domain classification of tea leaf diseases. The experimental results verify the effectiveness of the presented method and provide new thoughts for the cross-domain classification problem in agriculture.