A lightweight few-shot learning model for crop pest and disease identification

Abstract

Production quality is directly related to the economic development of agriculture. However, the growth of crops is susceptible to pest and disease infestations, which can negatively affect agricultural yields. Therefore, adopting efficient pest and disease identification methods is of the utmost importance. This paper proposes a lightweight few-shot learning model for crop pest and disease identification. The model utilizes a lightweight backbone network and incorporates adaptive spatial feature fusion to aggregate multi-scale features, thus avoiding feature redundancy and interference between multi-scale features. Additionally, a lightweight and efficient attention module is introduced to further explore the salient information in images from both channel and spatial dimensions. Experimental results demonstrate that, compared to the state-of-the-art methods in the field, the model achieved an average recognition accuracy improvement of 0.41% under the 10-shot setting on the PlantVillage dataset and improvements of 4.03% and 2.47% under the 5-shot and 10-shot settings, respectively, on the PlantDoc dataset. Furthermore, the model achieved a 1.46% increase in overall average recognition accuracy on the IP102 dataset, while also showing strong generalization capabilities on locally collected datasets.