Shengbo Chen ^1* Yun He ^1,2* Quan Gao ^1,2

• ¹ College of Big Data, Yunnan Agricultural University, Kunming, Yunnan Province, China
• ² The Laboratory for Crop Production and Intelligent, Yunnan Agricultural University, Kunming, Yunnan Province, China

Deep learning has revolutionized numerous fields, notably image classification. However, conventional methods in agricultural pest recognition struggle with the long-tail distribution of pest image data, characterized by limited samples in rare pest categories, thereby impeding overall model performance. This study proposes two state-of-the-art techniques: Instance-based Data Augmentation (IDA) and Constraint-based Feature Tuning (CFT). IDA collaboratively applies resampling and mixup methods to notably enhance feature extraction for rare class images. This approach addresses the long-tail distribution challenge through resampling, ensuring adequate representation for scarce categories. Additionally, by introducing data augmentation, we further refined the recognition of tail-end categories without compromising performance on common samples. CFT, a refinement built upon pre-trained models using IDA, facilitated the precise classification of image features through fine-tuning. Our experimental findings validate that our proposed method outperformed previous approaches on the CIFAR-10-LT, CIFAR-100-LT, and IP102 datasets, demonstrating its effectiveness. Using IDA and CFT to optimize the ViT model, we observed significant improvements over the baseline, with accuracy rates reaching 98.21%, 88.62%, and 64.26%, representing increases of 0.74%, 3.55%, and 5.73% respectively. Our evaluation of the CIFAR-10-LT and CIFAR-100-LT datasets also demonstrated state-of-the-art performance.