Rice Disease Image Recognition Based on Improved Multi-scale Stack Autoencoder

Abstract

Recently, deep learning methods are widely used in the rice diseases identification. However, the actual image background of rice disease is complex, the classification performance is not ideal. Therefore, this paper proposed a multi-scale feature extraction method based on stacked autoencoder, named the multi-scale stacked autoencoder (MSSAE), to improve the recognition accuracy of rice diseases. This method extracts the complex rice disease image’s features by two steps. In the first step, the images are preprocessed. Then, the MSSAE extract the multi-scale features through preprocessed rice diseases data in different scales. Through comparative analysis of experiments, the new method achieved greater than 95% precision in the detection of rice diseases. It indicated that the MSSAE model has an outstanding identification performance for actual crop disease image recognition.