关键词: 深度学习, 卷积神经网络, 多尺度融合, 空洞卷积, 害虫分类

Abstract: Crop diseases and insect pests are serious natural disasters, which need to be predicted and monitored in time to ensure crop yields. Due to the wide variety of pests and the similar shapes of crops in the early stage of growth, it is difficult for agricultural workers to accurately identify various crop insects, which brings great challenges to the prevention and control of pests and diseases. Aiming at this problem, a network model based on multiscale feature fusion (FFNet) was proposed to accurately identify and classify crop pests. First, a multiscale feature extraction module (MFEM) was designed by using cavity convolution to obtain the multiscale feature map of the pest image; then, the deep feature extraction module (DFEM) was used to extract the deep feature information of the image. The feature maps extracted by the extraction module (MFEM) and the deep feature extraction module (DFEM) were fused to achieve accurate classification and identification of crop pests in an endtoend manner. Experiments showed that the proposed method achieved excellent classification performance on the dataset of 12 types of pests, the classification accuracy rate (ACC) reached 98.2%, the loss function Loss was 0.031, and the model training time was 197 min respectively.

Key words: deep learning, convolutional neural network, multiscale fusion, dilated convolution, pest classification