Abstract:

In real farmland scenarios, some pest species are difficult to collect, resulting in very limited sample sizes. Traditional detection methods struggle to achieve reliable detection and classification performance under such few-shot conditions. This study aims to address the problem of few-shot pest detection by improving the model’s generalization ability and detection performance. A dataset named AgriPest-13 containing 13 pest categories was constructed, including 11 common pest species and 2 few-shot pests with only 10 and 30 images respectively. Based on this dataset, an improved few-shot detection method, FSD-MOGA (Few-Shot Detection with Multi-Order Gated Aggregation), is proposed. The method is developed upon the classic few-shot detection model MPSR, integrating a Multi-Order Gated Aggregation (MOGA) module to enhance feature representation under complex backgrounds and incorporating Center Loss to optimize the distribution of classification features. Compared with the original MPSR model, FSD-MOGA achieves significant performance improvements in few-shot pest detection tasks, particularly demonstrating more stable and accurate detection for categories with extremely limited samples. FSD-MOGA effectively alleviates the detection challenges caused by scarce pest samples and provides a feasible and efficient solution for intelligent pest detection in real agricultural environments with limited data.

Key words: few-shot detection, pest recognition, feature aggregation, Center Loss, smart agriculture