Lightweight apple leaf disease identification method based on knowledge distillation

The invention discloses a lightweight apple leaf disease identification method based on knowledge distillation, and belongs to the technical field of image processing. The method comprises the following steps: improving a classical SqueezeNet structure; enabling a teacher to perform network guidance; carrying out low-precision teacher network modification; and identifying apple leaf diseases. The backbone network adopted by the invention is a lightweight and efficient convolutional neural network model, and the structure of the backbone network is designed and modified to obtain a lighter model; meanwhile, the knowledge distillation method is utilized to remarkably reduce model parameters and keep the model performance at a relatively high level, so that the model can be deployed on equipment with limited embedded resources, such as a mobile terminal, and real-time and accurate identification of apple leaf diseases can be realized; the invention further provides an idea of "advanced renovation", and the probl