  Animal behavior serves as a reliable indicator of the adaptation of organisms
to their environment and their overall well-being. Through rigorous observation
of animal actions and interactions, researchers and observers can glean
valuable insights into diverse facets of their lives, encompassing health,
social dynamics, ecological relationships, and neuroethological dimensions.
Although state-of-the-art deep learning models have demonstrated remarkable
accuracy in classifying various forms of animal data, their adoption in animal
behavior studies remains limited. This survey article endeavors to
comprehensively explore deep learning architectures and strategies applied to
the identification of animal behavior, spanning auditory, visual, and
audiovisual methodologies. Furthermore, the manuscript scrutinizes extant
animal behavior datasets, offering a detailed examination of the principal
challenges confronting this research domain. The article culminates in a
comprehensive discussion of key research directions within deep learning that
hold potential for advancing the field of animal behavior studies.
