GPPK4PCM: pest classification model integrating growth period prior knowledge

Recent advancements in computer vision technology have significantly improved pest classification. However, pests of the same species exhibit distinct morphological changes throughout different life periods. Traditional methods apply the same feature extraction techniques to all periods, limiting classification precision. In addition to its inherent visual characteristics, pest images contain implicit growth period information. To address this issue, we propose a Pest Classification Model Integrating Growth Period Prior Knowledge. The model is composed of three sub-modules where: i) A deep learning network first identifies the growth periods of pests, and this prior knowledge is then used to guide the text encoder of the CLIP pre-trained model in generating period-specific textual features. ii) A parallel deep learning network extracts visual features from pest images. iii) An efficient low-rank multimodal fusion module integrates textual and visual features through parameter-optimized tensor decomposition, significantly improving classification accuracy across pest developmental phases. To evaluate its effectiveness, a dataset containing pests at different growth periods was constructed from Sichuan Agricultural University's pest dataset. Experimental results show that GPPK4PCM outperforms well-established deep learning neural networks. Compared to other advanced models, the proposed model excels in pest and disease classification tasks, effectively handling significant morphological differences across life periods.