Maize leaf disease identification based on WG-MARNet

Abstract

In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can solve the problems of noise, background interference, and low detection accuracy of maize leaf disease images. To begin, a processing layer called Wavelet threshold guided bilateral filtering (WT-GBF) based on the WG-MARNet model is employed to reduce image noise and perform high and low-frequency decomposition of the input image using WT-GBF. This increases the input image’s resistance to environmental interference and feature extraction capability. Secondly, for the multiscale feature fusion technique, an average down-sampling and tiling method is employed to increase feature representation and limit the risk of overfitting. Then, on high and low-frequency multi-channel, an attenuation factor is introduced to optimize the performance instability during training of the deep network. Finally, when the convergence and accuracy are compared, PRelu and Adabound are used instead of the Relu activation function and the Adam optimizer. The experimental results revealed that our method’s average recognition accuracy was 97.96%, and the detection time for a single image was 0.278 seconds. The average detection accuracy has been increased. The method lays the groundwork for the precise control of maize diseases in the field.

Tomato leaf disease recognition based on improved convolutional neural network with attention mechanism

“…Thangaraj et al (2021) proposed a deep convolutional neural network model based on transfer learning to identify tomato leaf diseases. Li, Zhou, et al (2022) proposed a method for maize leaf disease recognition based on wavelet thresholding guided bilateral filtering, multichannel ResNet and attenuation factor. Bi et al (2022) proposed a low‐cost, stable and high‐precision method for apple leaf disease recognition based on MobileNet.…”