A Species Identification Model Using Infrared Images for Wildlife Damage Management

Abstract

In recent years, with the advancement of LPWA (Low Power Wide Area) communication technologies and the increasing use of renewable energy, there has been growing interest in ICT-based solutions to address the escalating issue of wildlife damage. Among these, animal species identification technologies play a crucial role not only in improving the efficiency of trap-based capture methods but also in devising effective deterrent strategies such as repelling techniques. This study aims to develop a sustainable wildlife damage prevention framework for agricultural lands in mountainous and hilly regions, focusing on the development of core animal classification technology. Since many wild animals appear predominantly at night, identification is carried out using infrared (IR) images—rather than standard color images—captured by trail cameras and similar devices. Specifically, this research explores the application of deep learning techniques to classify animal species from IR images and evaluates the required amount of training data, classification accuracy, and the feasibility of practical deployment.