Having accurate, detailed, and upto-date information about wildlife location and behavior across broad geographic areas would revolutionize our ability to study, conserve, and manage species and ecosystems. Currently, such data are mostly gathered manually at great expense, and thus are sparsely and infrequently collected. Here we investigate the ability to automatically collect such data, which could transform many fields of biology, ecology, and zoology into big data sciences. In areas like an airportor the agricultural areas placed near the forest many animals destroy the crops or even attack on people therefore there is a need of system which detects the animal presence and gives warning about that in the view of safety purpose. In this project, we demonstrate that such data can be automatically extracted by deep neural networks (deep learning), which is a cutting-edge type of artificial intelligence. Thus,the aim is to train neural networks that automatically identifiesanimals
