A Case Study for the Use of Image‐Based Monitoring of Moth Populations in Stored‐Product Environments Using Smart Sticky Traps

ABSTRACT

Effective monitoring of stored‐product insect pests is essential for timely intervention and informed decision‐making in integrated pest management (IPM) programs. In practice, however, routine inspection of sticky traps remains labor‐intensive and is often constrained by limited temporal resolution and observer variability. Here, we evaluate an automated, image‐based monitoring system designed to estimate moth populations from sticky traps under realistic storage conditions. The system combines camera‐equipped traps with image preprocessing and a convolutional neural network (CNN) to distinguish moths from non‐moth insects and other trap‐associated artifacts. A dataset of 1739 high‐resolution sticky‐trap images collected under variable lighting, trap orientation, and contamination levels was annotated and used for model training and evaluation. The automated approach achieved an overall classification accuracy of 95.8%, with precision, recall, and F1‐scores consistently exceeding 90%, demonstrating reliable performance even in images containing overlapping insects and debris. Beyond classification accuracy, the system enabled continuous estimation of moth abundance over time, revealing site‐specific and temporal variation in moth activity relevant to IPM decision thresholds. By substantially reducing manual inspection effort while maintaining biologically meaningful population estimates, this approach offers a practical and scalable tool for enhancing stored‐product pest surveillance. The results support the feasibility of integrating automated image analysis into routine monitoring workflows to improve responsiveness and efficiency in postharvest pest management.