Identi fi cation of tomato leaf diseases based on combination of ABCK-BWTR and B-ARNet

In the existing machine vision technology for tomato leaf disease recognition, due to interference from the external environment, it is easy to generate noise during image acquisition, and the characteristics of different diseases are similar, which makes image disease recognition difficult. Therefore, we propose a new framework for tomato leaf disease recognition. First, the image is denoised and enhanced by Binary Wavelet Transform combined with Retinex (BWTR), noise points and edge points are removed, and important texture information is retained. Then, the tomato leaves were separated from the background using KSW optimizatied by Artificial Bee Colony algorithm (ABCK). Finally, the Both-channel Residual Attention Network model (B-ARNet) was used to identify the pictures. The application results of 8616 images show that the overall detection accuracy is about 89%. Experiments show that the tomato leaf disease recognition method based on the combination of ABCKBWTR and B-ARNet is effective.