Deep learning-based analysis of insect life stages using a repurposed dataset

Abstract

Insect pests pose a significant risk to agriculture and biosecurity, reducing crop yields and requiring effective management. Accurate identification of early life stages is often required for effective management but is generally reliant on expert evaluation, which is both costly and time-consuming. To address this, we use a deep learning-based approach for insect species and life-stage classification from digital images. We repurposed the IP102 dataset by adding detailed annotations for four life stages — egg, larva, pupa, and adult — alongside the original species categories. Two deep learning models, based on ResNet50 and EfficientNetV2M, were tested for classification accuracy in this dual-layered identification task. Although both models accomplished the task well, the EfficientNetV2M model performed slightly better than the ResNet50, achieving 72.4% precision, 72.1% recall, and an F1-score of 72.0%. Our results demonstrate the potential of deep learning for automated insect species and life-stage classification, providing a high throughput and efficient solution towards agricultural monitoring and pest management.