Research on Plant Leaf Disease Identification Based on Transfer Learning Algorithm
Han Jiang, Zhi Peng Xue, Yan Guo
Abstract
Abstract Plant disease is one of the important threat factors that hinder the normal growth and development of plants. The intelligent identification of plant disease species has become increasingly important in the agricultural field. In This paper, the open-source data set including Black rot, bacterial spot, rust, and healthy leaves are used to train the ResNet model. And the transfer learning algorithm is applied on ResNet to establish a plant disease recognition model with good versatility and high training efficiency. The experiment results show that the disease identification accuracy of the transfer learning model is 83.75%, which is much higher than that of the traditional ResNet-101 model. Therefore, the plant disease recognition model based on transfer learning algorithm is highly feasible.