Common pests image recognition based on deep convolutional neural network

In order to achieve rapid recognition of the common pests in agriculture and forestry, a new method based on deep convolution neural network was proposed. In this paper, the images of 19 insects and 1 larvae were collected. The data were enhanced by image processing methods such as flipping, rotating, scaling, adding Gussian noise and fancy PCA. The constructed image dataset CPAF had 73,635 insect images, including 4909 original images and 68,726 enhanced images. A 3-folds validation method was used to recognize pest images with VggA, VGG16, Inception V3, ResNet50, as well as CPAFNet, an deep neural network model we designed in this paper, on the CPAF dataset. For the better optimization of identification results, the balanced accuracy was computed and analyzed in this paper. We first compared performance of different models on CPAF dataset. After the same number of iterations of training, the recognition accuracy of the CPAFNet model reached 92.26% when the learning rate was set to 0.02, which is the best performance of all the models participating in the test. The least time spent on training is also the CPAFNet model. Then, the influence of different number of convolution kernels on recognition rate of CPAFNet was analyzed. Results shown that the balance accuracy achieved to 92.63% when the number of convolution kernels corresponding to the convolutional layer group was set to 64128-256-256. Finally, the influence of different optimization algorithms and dropout probability on training was assessed. Results shown that when the RMSProp algorithm was used and dropout probability was set to 0.8 of CPAFNet, the balance accuracy achieved to 92.63%. In addition, different enhancement algorithms were assessed on pests image recognition of CPAFNet. Results shown that the balance accuracy was decreased from 0.9263 to 0.9152, 0.9125 and 0.9230 on CPAF dataset without expanded data obtained by flipping, rotating and scaling algorithm respectively, which indicate the enhancement algorithms of them can improve the identification precision of pest image. At the same time, the class activation map algorithm was used for feature visualization. Results shown that the CPRAFNet is good for capturing features on pest of CPAF dataset. The results of the model optimization research and the CPAFNet depth model proposed for the CPAF dataset have a good practical significance for the intelligent identification of agricultural and forestry pests.