Machine Learning and Deep Learning-Based Prediction and Monitoring of Forest Fires Using Unmanned Aerial Vehicle

Abstract

Wildfires, also referred to as forest fires, pose serious risks to heavily vegetated forested areas, demanding the development of sophisticated techniques for accurate forecasting and early detection. Unmanned aerial vehicles (UAV) and machine learning integration has been identified as a possible strategy to improve forest fire prediction systems. This thorough study seeks to provide an overview of the research that has been done in the field of machine learning-based UAV-based forest fire prediction. It discusses the benefits of using UAVs for data collection, the use of machine learning techniques, current difficulties, and potential future developments in this area. The main goal of this research is to clearly explain the state-of-the-art UAV-based forest fire prediction in order to facilitate future research projects and practical applications. Drones with sensors and imaging equipment make it possible to collect vital information on vegetation, weather, and fire behavior in real-time, which aids in more efficient wildfire management.