Abstract
The species information is fundamental for ensuring biodiversity. The distinguishing proof of birds by customary keys is intricate, tedious, and because of the unavailability of information about exact name, it is difficult to identify and often baffling for non-specialists. This makes a difficult to conquer leap for tenderfoots intrigued by procuring species information. Today, there is an expanding enthusiasm for computerizing the procedure of species distinguishing proof. The accessibility and pervasiveness of important advancements, such as, computerized cameras and cell phones, the remote access to databases, new strategies in picture preparing and design acknowledgment let computerized species recognizable proof move toward becoming reality. This paper presents a deep learning neural network technique for identifying bird species. Tensor flow framework is used for the implementation.
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Pillai, S.K., Raghuwanshi, M.M., Shrawankar, U. (2019). Deep Learning Neural Network for Identification of Bird Species. In: Peng, SL., Dey, N., Bundele, M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-13-7150-9_31
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DOI: https://doi.org/10.1007/978-981-13-7150-9_31
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