A Rice Pest Identification Method Based on a Convolutional Neural Network and Migration Learning

Abstract

In this paper, we construct models for convolutional neural networks and migration learning, conduct in-depth research on rice pest recognition methods, and design a plan based on convolutional neural networks and migration learning. The weight parameters obtained from the VGG16 model trained on the image dataset Image Net are migrated to recognize rice pests through the migration learning method. The convolutional and pooling layers of VGG16 are used as feature extraction layers. In contrast, the top layer is redesigned as a global average pooling layer and a SoftMax output layer, and some of the convolutional layers are frozen during training. The proposed method effectively improves the recognition accuracy of water to pest images and significantly reduces the number of model parameters.