Deep learning insights and methods for classifying wildlife

With the introduction of low-cost and widely accessible sensors such as cellphones, drones, satellites, voice recorders, and bio-logging equipment, the amount of information collected about animals has expanded. Meanwhile, modern data processing systems prohibit them from collecting, digesting, and condensing data into usable information. We think that machine learning, especially deep learning algorithms, will be able to tackle this analytical difficulty by enhancing our understanding, monitoring capacities, and animal welfare. By merging machine learning with ecological processes, it may be feasible to expand the inputs to population and behavior models, resulting in integrated hybrid modeling tools where machine learning models give data-supported insights and ecological models act as constraints. Animal ecologists may basically profit from the quantity of data created by contemporary sensor technologies by integrating cutting-edge machine learning methods with ecological domain expertise. This will enable them to assess population abundances more precisely, research animal behavior, and reduce human-wildlife conflicts.