  This paper proposes a new machine learning based system for forest fire
earlier detection in a low-cost and accurate manner. Accordingly, it is aimed
to bring a new and definite perspective to visual detection in forest fires. A
drone is constructed for this purpose. The microcontroller in the system has
been programmed by training with deep learning methods, and the unmanned aerial
vehicle has been given the ability to recognize the smoke, the earliest sign of
fire detection. The common problem in the prevalent algorithms used in fire
detection is the high false alarm and overlook rates. Confirming the result
obtained from the visualization with an additional supervision stage will
increase the reliability of the system as well as guarantee the accuracy of the
result. Due to the mobile vision ability of the unmanned aerial vehicle, the
data can be controlled from any point of view clearly and continuously. System
performance are validated by conducting experiments in both simulation and
physical environments.
