Exploiting Drone Images for Forest Fire Detection using Metaheuristics with Deep Learning Model

<p>Forest fires are a global natural calamity causing significant economic damage and loss of lives. Professionals forecast that forest would raise in the future because climate change. Early prediction identification fire spread enhance firefighting reduce affected zones. Several systems have been advanced to detect fire. In recent times, Unmanned Aerial Vehicles (UAVs) used for tackling this issue their ability, high flexibility, cheap price cover vast areas during nighttime or daytime. But still they limited by difficulties like image degradation, small size, background complexity. This study develops an automated Forest Fire Detection using Metaheuristics with Deep Learning (FFDMDL-DI) model. The presented FFDMDL-DI technique exploits DL concepts on drone images identify occurrence To accomplish this, makes use Capsule Network (CapNet) model feature extraction purposes biogeography-based optimization (BBO) algorithm-based hyperparameter optimizer. For accurate detection, uses unified deep neural network (DNN) Finally, tree growth (TGO) is utilized parameter adjustment DNN method. depict enhanced detection efficiency approach, series simulations were performed. outcomes reported improvements method over other models.</p>