MoCoProto: Enhancing few-shot pest image classification with self-supervised representation learning

Accurate pest detection is crucial for safeguarding crop productivity and quality. Few-shot image classification offers a promising solution for automated pest recognition in limited-data settings. Recent studies have demonstrated the effectiveness of meta-learning with episodic tasks, enabling rapid adaptation to new classes. However, due to the limited number of samples available for each task, pretraining an appropriate embedding network for meta-learning plays a critical role in determining the few-shot classification performance. To enhance feature extraction, recent approaches have adopted supervised whole-classification training as a preparatory step before meta-learning. Nevertheless, when the labeled data for whole-classification training is insufficient, the performance improvements become limited. To address these challenges, this study proposes a method called MoCoProto, which combines self-supervised learning with meta-learning. The proposed method first utilizes momentum contrast-based self-supervised visual representation learning to pretrain an embedding network using unlabeled images. The pretrained embedding network is then applied to a prototypical network for few-shot image classification through meta-learning. For self-supervised pretraining, a broad-domain agricultural dataset comprising 357,705 images from 65 pest species and 144 plant diseases was used, from which all label information was excluded, relying solely on the image data. Subsequently, meta-training and final evaluation were conducted using the IP102 insect pest dataset, and the experimental results demonstrated that the proposed MoCoProto achieved the best performance, recording accuracies of 49.33% in 1-shot and 70.13% in 5-shot classification.