Development of a Species Classification Model Using Infrared Images for Smart Wildlife Damage Management

Abstract

Damage caused by wild animals and birds affects not only agricultural crops but also the natural environment itself. The Ministry of Agriculture, Forestry and Fisheries (MAFF) of Japan is promoting ”Smart Wildlife Damage Countermeasures” that utilize ICT, creating a demand for more effective methods to mitigate such damage. In this study, we developed an animal species identification model for infrared images using YOLO (You Only Look Once), a convolutional neural network-based object detection algorithm, to validate the effectiveness of training with infrared imagery. Concurrently, we trained the model using two datasets of different sizes to investigate the amount of image data required for effective learning and to consider practical deployment methods.