A semi-supervised approach for classifying insect developmental phases from repurposed IP102

Identifying insect pests, whether as adults, larvae, or eggs, is critical in pest management. Computational learning algorithms have demonstrated strong potential in achieving high identification performance, but these methods typically require large, balanced, and well-annotated datasets. This creates a challenge for insect pest identification, as rare species, despite often being the most damaging to crops, are underrepresented in available datasets. Moreover, annotating large-scale datasets is both costly and labour-intensive. To address this issue, we develop a semi-supervised learning approach, Cost-Focal FixMatch, which extends the widely used FixMatch framework by integrating class-aware reweighting and focal loss to better handle class imbalance. Specifically, we introduce a simple yet robust method for applying class weighting in cross-entropy and focal loss functions. The proposed method generates higher-quality pseudo labels compared to the baseline, ensuring better learning. We evaluate our approach using a repurposed IP102 dataset, which comprises four primary insect life stages, and a mixed IP102 dataset, where the class labels jointly represent insect species and their corresponding life stages. Our method considerably improves the classification of minority classes, achieving a notable increase in recall for the Larva class from 64% under the baseline FixMatch to 82% using MobileNetV3Small backbone. On the Mixed IP102 dataset, our approach achieves almost 9% better improved average recall than the baseline FixMatch built upon the EfficientNetV2S network. • A semi-supervised learning pipeline for identifying insect species and life stages. • Focal Loss ensures better training in a class-imbalance scenario. • A unified class weighting method has been proposed to prioritise minority classes. • Recall on larva classification is 8%–18% higher than baseline, varying by backbone.