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    "title": "Image-Based Animal Detection and Breed Identification Using Neural Networks",
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            "value": "Having accurate, detailed, and up-to-date information about the behaviour of animals in the wild world would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data through various sources, which could help catalyse the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, animal behaviour into “big data” sciences and many more. So extracting information from the pictures remains an expensive, time-consuming, and manual task for us. We demonstrate that such information can be automatically extracted by deep learning and convolutional neural network. Leveraging on recent advances in deep learning techniques in computer vision, we propose in this project a framework to build automated animal recognition in the wild, aiming at an automated wildlife monitoring system. In particular, we use a single-labelled dataset done by citizen scientists, and the state-of-the-art deep convolutional neural network architectures, face biometrics, to train a computational system capable of filtering animal images and identifying species automatically and counting the number of species. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild and this, in turn, can, therefore, speed up research findings, construct more efficient citizen science-based monitoring systems and subsequent management decisions, having the potential to make significant impacts to the world of ecology and trap camera images analysis .",
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