Progress in Developing a Bark Beetrle Identification Tool

Abstract

AbstractThis study presents a tool for the identification of bark beetles. These pests are known for their potential to cause extensive damage to forests globally, as well as for uniform and homoplastic morphology which poses identification challenges. Utilizing a MaxViT-based deep learning model is an innovative approach to classify bark beetles down to the species level from images containing multiple beetles. The methodology involves a comprehensive process of data collection, preparation, and model training, leveraging pre-classified beetle species to ensure accuracy and reliability. The model’s high F1 score estimates of 0.99 indicates its exceptional performance, demonstrating a strong ability to accurately classify species, including those previously unknown to the model. This makes it a valuable tool for applications in forest management and ecological research. Despite the controlled conditions of image collection and potential challenges in real-world application, this study provides the first model capable of identifying the bark beetle species, and by far the largest training set of images for any comparable insect group. We also designed a function that reports if a species appears to be unknown. Further research is suggested to enhance the model’s generalization capabilities and scalability, emphasizing the integration of advanced machine learning techniques for improved species classification and the detection of invasive or undescribed species.