College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, China.
Plant Protection Research Institute, Academy of Agricultural Sciences, Changsha, Hunan, China.
PLoS One. 2022 Apr 28;17(4):e0267650. doi: 10.1371/journal.pone.0267650. eCollection 2022.
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.
在基于深度学习的玉米叶片病害检测中，提出了一种名为基于小波阈值引导双边滤波、多通道 ResNet 和衰减因子（WG-MARNet）的网络的玉米病害识别方法。该方法可以解决玉米叶片病害图像的噪声、背景干扰和低检测精度问题。首先，基于 WG-MARNet 模型使用一种名为基于小波阈值引导双边滤波（WT-GBF）的处理层来减少图像噪声，并使用 WT-GBF 对输入图像进行高低频分解。这增加了输入图像对环境干扰的抵抗力和特征提取能力。其次，对于多尺度特征融合技术，采用平均下采样和平铺方法来增加特征表示并限制深度网络训练中的过拟合风险。然后，在高低频多通道上，引入衰减因子来优化网络训练过程中的性能不稳定问题。最后，在比较收敛和精度时，使用 PRelu 和 Adabound 代替 Relu 激活函数和 Adam 优化器。实验结果表明，我们的方法的平均识别准确率为 97.96%，单张图像的检测时间为 0.278 秒。平均检测准确率有所提高。该方法为田间玉米病害的精确控制奠定了基础。
