Objective  With the continuous development of artificial intelligence and big data technology, aiming at solving the problems of low accuracy and low efficiency in conventional identification methods of corn pest, we proposed a corn borer image identification method based on the improved GoogLeNet convolution-neural network model.

Method  Firstly, through migration learning, the structural knowledge of the Inception-v4 network of GoogLeNet was transferred to the task of corn borer (Pyrausta nubilalis) identification, and the training mode of model construction was established. The data set of neural network training model was obtained by expanding the sample of corn borer image through data enhancement technique. At the same time, the Inception module was used to construct the network model with the ability of multi-scale convolution kernel extraction of the distribution characteristics of multi-scale corn borer, and the activation function, gradient descent algorithm and other model parameters were optimized in the experimental process. Finally, batch normalization (BN) operation was performed to accelerate optimizating model network training, and the model was applied in corn borer identification.

Result  Experimental results of TensorFlow framework showed that the average recognition accuracy of the optimized neural network algorithm for corn borer image was 96.44%.

Conclusion  The convolutional neural network recognition model based on optimization has higher robustness and feasibility, which can provide a reference for identification and intelligent diagnosis of plant pests on corn and other crops.