Abstract
Artificial intelligence (AI) and machine learning (ML) have slowly but steadily become integral to human lives. While much remains to be learned, it surely has contributed to research works which were beyond the comprehension of the human mind. This paper focuses on the concept of standard machine learning techniques used for terrestrial species identification using 3D images. A novel methodology is proposed for the extraction of the characteristics and structural properties of these species based on images using convolution neural networks (CNNs). This study mainly contributes to disseminating awareness and knowledge to research groups in similar fields of study. Observation has been made that despite the advancement of science and technology, people can still not differentiate a buffalo from a bison. In addition, recent developments in the field of machine learning are still a novice in the field of species studies. The emergence of AI and ML is slowly changing educational entities and industrial services, and rightly so. The proposed methodology can also act as a supplementary tool for restructuring traditional higher education, which is still prevalent in India.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
TechTarget.: A guide to artificial intelligence in the enterprise. https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence
Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., SantamarÃa, J., Fadhel, M.A., Al-Amidie, M., Farhan, L.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 1–74 (2021)
Ai in chess: The evolution of artificial intelligence in chess engines ... https://towardsdatascience.com/ai-in-chess-the-evolution-of-artificial-intelligence-in-chess-engines-a3a9e230ed50. Accessed 9 Nov 2022
8 ways AI is used in education. In: Analytics insight. https://www.analyticsinsight.net/8-ways-ai-is-used-in-education/#:~:text=AI%20enhances
Tammina, S.: Transfer learning using VGG-16 with deep convolutional neural network for classifying images. Int. J. Scient. Res. Publ. (IJSRP) (2019). https://doi.org/10.29322/ijsrp.9.10.2019.p9420
Sumit, S.:. A comprehensive guide to convolutional neural networks—the ELI5 way. Towards data science 15 (2018%20the%20personalization%20of,universal%2024%2F7%20learning%20access. Accessed 9 Nov 2022
Chaudhari, G., Patil, A., Gangurde, G.: Identification Using Machine Learning Technique, March (2020). Retrieved from https://sreyas.ac.in/wp-content/uploads/2021/07/6.-Dr.-Rohit-Raja.pdf
Shetty, H., Singh, H., Shaikh, F.: Animal Detection using Deep Learning, June (2021). Retrieved from https://ijesc.org/upload/f001d8180864788afc97068687a1f59b.Animal%20Detection%20using%20Deep%20Learning%20(1).pdf9
Patil, A.: Animal Identification Using Machine Learning Technique, March (2019). Retrieved from https://www.researchgate.net/publication/349532649_Animal_Identification_Using_Machine_Learning_Technique
Karlsson, J., Ren, K., Li, H.: Tracking and Identification of Animals for a Digital Zoo, December (2010). Retrieved from https://ieeexplore.ieee.org/abstract/document/5724879
Saxena, A., Gupta, D.K., Singh, S.: An Animal Detection and Collision Avoidance System Using Deep Learning, August (2020). Retrieved from https://link.springer.com/chapter/https://doi.org/10.1007/978-981-15-5341-7_81
Hridayami, P., Putra, I.K., Wibawa, K.S.: Fish species recognition using VGG16 deep convolutional neural network. J. Comput. Sci. Eng. 13, 124–130 (2019). https://doi.org/10.5626/jcse.2019.13.3.124
Architecture (June 2022).CNN Architecture—Detailed Explanation. Retrieved from https://www.interviewbit.com/blog/cnn-architecture/
O'Shea, K., Nash, R.: An Introduction to Convolutional Neural Networks, November 26 (2015). Retrieved from https://typeset.io/papers/an-introduction-to-convolutional-neural-networks-5342q71fyx
Norouzzadeh, M.S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M.S., Packer, C., Clune, J.: Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning, June 5 (2018). Retrieved from https://www.pnas.org/doi/https://doi.org/10.1073/pnas.1719367115
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sangkhro, P., Pyngrope, P., Bangermayang, Nandi, G. (2023). A Novel Model for Automated Identification of Terrestrial Species. In: Bhattacharyya, S., Das, G., De, S., Mrsic, L. (eds) Recent Trends in Intelligence Enabled Research. DoSIER 2022. Advances in Intelligent Systems and Computing, vol 1446. Springer, Singapore. https://doi.org/10.1007/978-981-99-1472-2_12
Download citation
DOI: https://doi.org/10.1007/978-981-99-1472-2_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1471-5
Online ISBN: 978-981-99-1472-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)