CNN and transformer framework for insect pest classification

Insect pests pose a significant and increasing threat to agricultural production worldwide. However, most existing recognition methods are built upon well-known convolutional neural networks, which limits the possibility of improving pest recognition accuracies. This research attempts to overcome this challenge from a novel perspective, constructing a simplified but very useful network for effective insect pest recognition by combining transformer architecture and convolution blocks. First, the representative features are extracted from the input image using a backbone convolutional neural network. Second, a new transformer attention-based classification head is proposed to sufficiently utilize spatial data from the features. With that, we explore different combinations for each module in our model and abstract our model into a simple and scalable architecture; we introduce more effective training strategies, pretrained models and data augmentation methods. Our models performance was evaluated on the IP102 benchmark dataset and achieved classification accuracies of 74.897% and 75.583% with minimal implementation costs at image resolutions of 224 × 224 pixels and 480 × 480 pixels, respectively. Our model also attains accuracies of 99.472% and 97.935% on the D0 dataset and Li's dataset, respectively, with an image resolution of 224 × 224 pixels. The experimental results demonstrate that our method is superior to the state-of-the-art methods on these datasets. Accordingly, the proposed model can be deployed in practice and provides additional insights into the related research.