Automatic Moth Detection from Trap Images for Pest Management
This provides an automated solution for pest monitoring in agriculture, though it appears incremental as it builds on deep learning methods without major paradigm shifts.
The paper tackles the problem of automatically detecting and counting insect pests in trap images for pest management, achieving promising performance on a commercial codling moth dataset.
Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems. In this paper, we propose an automatic detection pipeline based on deep learning for identifying and counting pests in images taken inside field traps. Applied to a commercial codling moth dataset, our method shows promising performance both qualitatively and quantitatively. Compared to previous attempts at pest detection, our approach uses no pest-specific engineering which enables it to adapt to other species and environments with minimal human effort. It is amenable to implementation on parallel hardware and therefore capable of deployment in settings where real-time performance is required.