Saving Wildlife using Artificial Neural Network Technology
Keywords:
Buzzer, Electric fencing, Micro SD, Raspberry Pi, Smart fencingAbstract
Electric fencing is widely employed to prevent wildlife from encroaching on agricultural areas. However, it presents a considerable danger to wildlife, particularly large mammals such as elephants, which may sustain severe injuries upon contact. This study introduces an innovative strategy to address these concerns through the application of image detection technology. The main goal is to create an automated system capable of real-time detection of elephants near electric fences. This system leverages image detection algorithms that have been taught on a comprehensive data set of elephant imagery to identify along with monitoring elephants in the vicinity of the fencing. By continuously surveilling the area, the system will emit a buzzer sound upon detecting an elephant. Essential elements of the system consist of a network of cameras strategically positioned along the poles to maximize coverage, paired with an image processing algorithm designed to recognize elephants based on their distinctive features. Additionally, a buzzer will be activated to signal the presence of an elephant. The study incorporates artificial intelligence and image detection techniques to facilitate the identification of elephants.
References
B. Kellenberger, M. Volpi, and D. Tuia, “Fast animal detection in UAV images using convolutional neural networks,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul. 2017, doi: https://doi.org/10.1109/igarss.2017.8127090
K. S. P. Premarathna, R. M. K. T. Rathnayaka, and J. Charles, “An Elephant Detection System to Prevent Human-Elephant Conflict and Tracking of Elephant Using Deep Learning,” IEEE Xplore, Dec. 01, 2020. https://ieeexplore.ieee.org/abstract/document/9310798
D. Patel and S. Sharma, “Automated detection of elephants using AI techniques,” in Soft Computing and Optimization. SCOTA 2021, S. D. Jabeen, J. Ali, and O. Castillo, Eds., Springer Proceedings in Mathematics & Statistics, vol. 404, Singapore: Springer, 2022, pp. 42–52. doi: https://doi.org/10.1007/978-981-19-6406-0_4
S. Gunasekara, M. Jayasuriya, N. Harischandra, L. Samaranayake and G. Dissanayake, "A Convolutional Neural Network Based Early Warning System to Prevent Elephant-Train Collisions, 2021 IEEE 16th International Conference on Industrial and Information Systems (ICIIS), Kandy, Sri Lanka, 2021, pp. 271-276, doi: https://doi.org/ 10.1109/ICIIS53135.2021.9660651.
E. M. K. de Silva et al., “Feasibility of using convolutional neural networks for individual-identification of wild Asian elephants,” Mammalian Biology, vol. 102, no. 3, pp. 931–941, Jan. 2022, doi: https://doi.org/10.1007/s42991-021-00206-2
B. Natarajan, R. Elakkiya, B. Ramachandran, K. Saleem, D. Chaudhary, and S. Samsudeen, “Creating Alert messages based on Wild Animal Activity Detection using Hybrid Deep Neural Networks,” IEEE Access, vol. 11, pp. 67308–67321, Jan. 2023, doi: https://doi.org/10.1109/access.2023.3289586
B. Kądziołka, P. Jurkiewicz, R. Wójcik, and J. Domżaoł, “Elephant Flow Classification on the First Packet with Neural Networks,” IEEE Access, vol. 12, pp. 65298–65309, 2024, doi: https://doi.org/10.1109/access.2024.3398065
J. S. Dhanaraj and A. Kumar Sangaiah, “Elephant detection using boundary sense deep learning (BSDL) architecture,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 33, no. 4, pp. 561–576, Jul. 2021. https://www.tandfonline.com/doi/abs/10.1080/0952813X.2018.1552316
M. Maheswari, M. S. Josephine, and V. Jeyabalaraja, “Customized deep neural network model for autonomous and efficient surveillance of wildlife in national parks,” Computers and Electrical Engineering, vol. 100, p. 107913, Mar. 2022, doi: https://doi.org/10.1016/j.compeleceng.2022.107913
