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Abstract: The field of image classification and identification tasks has seen tremendous progress driven by the quick development of Convolutional Neural Network (CNN) methods. Unlike the widely used Vision Transformer (ViT) methods, this study presents an improved CNN-based model for pest recognition, segmentation, and classification. According to recent research, ViT is better at classifying images than standard machine learning and CNN methods. Taking this into consideration, we investigate how CNN models can incorporate two branch segment representations by using a double-layer CNN encoder. This new CNN-based method handles token chunks with different sizes and levels of computational complexity with ease. These elements are then combined with various attention processes to improve the overall aspects of the image. We use publicly accessible pest databases that impact peanut & other crops in our experiments. When compared to cutting-edge algorithms, the suggested CNN model shows unique characteristics and performs better in pest picture prediction, obtaining a high accuracy rate of 99.25%.