A Multi-stage Prediction Framework for Pest Identification

With the development of computer vision technology and smart agriculture, deep learning techniques have been widely applied to crop pest identification tasks. However, existing studies do not consider the problem of large differences in pests across multiple growth stages, leading to unsatisfactory performance in pest identification in practical applications. This article proposes a simple framework for multi-stage prediction of pests, which can effectively predict the growth stage of pests and improve pest classification performance. The framework consists of a classification branch and a stage prediction branch. The stage prediction branch predicts the growth stage of pests based on feature similarity using K-Means, and guides the classification branch to classify images from different stages into different categories to avoid interfering with the performance of the classifier. In addition, to update the entire network parameters, we propose a multi-stage cross-entropy loss that optimizes feature extractors and classifiers by fusing image labels and stage prediction outputs. Experimental results show that the proposed multi-stage prediction framework for pest identification can accurately classify pest stages and improve pest classification accuracy. In addition, our work provides research ideas for pest stage prediction and identification, which is expected to help achieve more efficient pest control.