Ressource-Efficient Moth Detection for Pest Monitoring with YOLOv5

Moths pose a significant threat to agricultural crops, and identifying them accurately is crucial for effective pest monitoring and crop conservation efforts. However, manually evaluating glue traps is a time-consuming and labor-intensive process, which has led to the development of automated solutions. In this study, we present a deep learning-based automated detection pipeline that can detect moths in images captured by field traps with pheromone-emitting glue pads. To train our model, we collected a comprehensive dataset that includes moths from various environments, such as agricultural plants, homes, and food production facilities. We augmented this dataset and included additional glue pad datasets, enabling the model to detect moths regardless of the species. We base our model on the YOLOv5 algorithm and fine-tune it using transfer learning, which enables us to identify moths in real-time and on embedded hardware. Our evaluation of the algorithm reveals that it achieves an average precision of 98.2 % on a test dataset, which outperforms reference models from previous research. We also assess the model's ability to handle disturbances such as other insects, varying lighting conditions, and foreign objects. Importantly, our solution maintains a tiny memory footprint and low inference time of 2.3 ms, making it a highly efficient and effective tool for moth detection in the field.