Image Recognition of Peach Pests Based on Improved Convnext

Abstract

Abstract A ConvNeXt-based image recognition model for peach tree pests is proposed to address the problem of low accuracy of pest image recognition due to the large intra-class morphological variation of peach tree pests in natural scenes. First, ConvNeXt is used as the backbone network and the model structure is optimized by introducing the visual perceptual field module to extract discriminative features of peach tree pests. Secondly, a joint loss function is used to make the model continuously learn the features that minimize the intra-class distance to accelerate the model convergence and improve the recognition accuracy. Finally, two tandem fully connected layers are used to replace the original fully connected layer, and the feature vectors input to the fully connected layer are visualized to show the pest classification effect. The experimental results show that compared with 10 networks such as RegNet, the model in this paper effectively improves the recognition accuracy of peach tree pest images, and the accuracy is improved by 4.8% compared with the standard ConvNeXt, which verifies the effectiveness of the model in this paper for accurate recognition of peach tree pest images in natural scenes.