Identification of tea leaf diseases based on deep transfer learning

Abstract

Tea trees are often vulnerable to diseases and insect pests in the process of growth, resulting in the decline of tea production and quality. It is of great significance to identify and prevent tea leaf diseases in time to ensure the steady increase of tea. This study proposes a tea leaf disease identification method based on deep transfer learning, which improves the recognition accuracy of the model through knowledge transfer. Besides, for the unbalanced distribution of the number of samples, the cross-entropy loss function is replaced with the focal loss function, which further improves the identification effect of the model. The experiment shows that the identification model of tea leaf disease proposed in this study can achieve the accuracy of more than 90.42%, which verifies the effectiveness of this research and has important theoretical and practical significance in promoting the development of intelligent agriculture.