Identification of Tea Red Leaf Spot and Tea Red Scab Based on Hybrid Feature Optimization

Abstract

Tea leaf diseases seriously affect the quality and the yield of tea. In order to determine whether the tea leaves are infected by diseases or any types of infection, technical support is essential for taking appropriate measures of disease control. Images of normal tea leaves, tea leaves infected with Tea Red Leaf Spot, and leaves infected with Tea Red Scab disease were studied. An identification algorithm for both of the tea leaf diseases based on hybrid feature optimization was proposed. First, the image features were extracted using the Histogram of Oriented Gradient and the Inception v3 model. Then, hybrid feature optimization processing was performed on two types of extracted features. Finally, the Gradient Boosting Decision Tree algorithm was used as the classifier for the identification of tea leaf diseases. Experiments demonstrate that the hybrid feature optimization algorithm reduces the image feature from 36, 068 to less than 150 dimensions while maintaining a high identification accuracy, which greatly reduces the complexity of the identification algorithm. At the same time, the identification accuracy of tea leaf diseases based on hybrid feature optimization algorithm were higher than 95%.