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Counting of Pine Wood Nematode Based on VDNet Convolutional Neural Network

Pine wood nematode disease is a devastating forest disease caused by pine wood nematode, which seriously destroys the construction of ecological environment. In order to strengthen the supervision of forest epidemic and realize the detection and counting of pine wood nematodes, we propose a deep CNN counting model based on VGG-16 network and dilated convolution (DC), which is called VDNet network. The model is divided into two parts: front-end network and back-end network. The front-end uses the first 13 layers of VGG-16 to extract shallow features, and the back-end uses six layers of DC to extract more advanced features; Dilated convolution can expand the receptive field while maintaining the same resolution, replace the pooling operation, avoid further shrinking the output image, and generate a high-quality distribution density map of pine wood nematode trees; Finally, a traditional convolution layer is used to output the density map. The number of pine wilt trees in the input image is obtained by integrating the pixels of the density map, and theoretical analysis and experimental verification are carried out. The results show that the average absolute error of VDNet network is 2.78 and the mean square error is 3.65. The average accuracy is 86%.