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Paper ID:IJSREM65018

AI Based System For Animal Detection and Classification

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65018

Volume:

10 |

6 |

Jun 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65018

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

OAS

Om A. Shinde

Department of Electronics

Walchand College of Engineering

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Mentors

PRGM

Prof. R. G. Mevekari

Department of Electronics

Walchand College of Engineering

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Abstract

This paper presents an AI-based animal detection and classification system designed for real-time monitoring appli-cations. The proposed system utilizes Raspberry Pi 5 integrated with a camera module, microphone sensor, PIR motion sensor, and buzzer for detecting and classifying wild, domestic, and farm animals. Image classification is performed using CNN and MobileNetV2 models, while audio analysis is carried out using YAMNet and MFCC feature extraction techniques. A custom dataset of animal images and sounds was used for training and testing. Experimental results demonstrated an overall classifi-cation accuracy of 83.33%, with successful real-time detection and alert generation. The developed system provides a low-cost, energy-efficient, and intelligent solution suitable for wildlife monitoring, smart agriculture, and rural safety applications.

KEYWORDS

Animal Detection Raspberry Pi CNN Mo-bileNetV2 YAMNet MFCC Edge AI.

Cite This Article

Om A. Shinde, Prof. R. G. Mevekari, "AI Based System For Animal Detection and Classification," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-7, Jun 2026. https://doi.org/10.55041/IJSREM65018

Full Article/Conclusion

An AI-based animal detection and classification system was successfully developed using Raspberry Pi 5. The proposed system combines image processing, audio analysis, motion sensing, and deep learning techniques to perform real-time animal monitoring. Experimental testing achieved an overall classification accuracy of 83.33%, demonstrating the effective-ness of the proposed approach. The developed system provides a low-cost, energy-efficient, and intelligent solution suitable for wildlife monitoring, smart agriculture, and rural safety applications.

References

[1]   M. Ibraheam, K. F. Li, and F. Gebali, “An Accurate and Fast Animal Species Detection System for Embedded Devices,” IEEE Access, vol. 11, pp. 23462–23473, 2023, doi: 10.1109/ACCESS.2023.3252499. [2]   J. Chappidi and D. M. Sundaram, “Novel Animal Detection System: Cascaded YOLOv8 With Adaptive Preprocessing and Feature Extrac-tion,” IEEE Access, vol. 12, pp. 110575–110587, 2024, doi: 10.1109/AC-CESS.2024.3439230. [3]   R. Sato, H. Saito, Y. Tomioka, and Y. Kohira, “Energy Reduction Methods for Wild Animal Detection Devices,” IEEE Access, vol. 10,pp. 24149–24161, 2022, doi: 10.1109/ACCESS.2022.3155242. [4]   A. Vally et al., “M2S2: A Multimodal Sensor System for Remote Animal Motion Capture in the Wild,” IEEE Sensors Letters, vol. 9, no. 4, pp. 1–4, Apr. 2025, doi: 10.1109/LSENS.2025.3542233. [5]   M. Ibraheam, K. F. Li, and F. Gebali, “MCFP-YOLO Animal Species Detector for Embedded Systems,” Electronics, vol. 12, no. 24, p. 5044, Dec. 2023, doi: 10.3390/electronics12245044. [6]   M. Knyva, D. Gailius, G. Balcˇiu¯nas, D. Pratasˇius, P. Kuzas, and A. Kukanauskaite˙, “IoT Sensor Network for Wild-Animal Detection near Roads,” Sensors, vol. 23, no. 21, p. 8929, Nov. 2023, doi: 10.3390/s23218929. [7]   B. Natarajan, R. Elakkiya, R. Bhuvaneswari, K. Saleem, D. Chaudhary, and S. H. Samsudeen, “Creating Alert Messages Based on Wild Animal Activity Detection Using Hybrid Deep Neural Networks,” IEEE Access, vol. 11, pp. 67308–67321, 2023, doi: 10.1109/ACCESS.2023.3289586. [8]   J. P. Dominguez-Morales, L. Duran-Lopez, D. Gutierrez-Galan, A. Rios-Navarro, A. Linares Barranco, and A. Jimenez-Fernandez, “Wildlife Monitoring on the Edge: A Performance Evaluation of Embedded Neural Networks on Controllers for Animal Behavior Classification,” Sensors, vol. 21, no. 9, p. 2975, Apr. 2021, doi: 10.3390/s21092975. [9]   B. Dave, M. Mori, A. Bathani, and P. Goel, “Wild Animal Detection using YOLOv8,” Procedia Computer Science, vol. 230, pp. 100–111, 2023, doi: 10.1016/j.procs.2023.12.065. [10]   R. Arablouei, “Animal Behaviour Classification via Deep Learning on Accelerometry Data,” Computers and Electronics in Agriculture, vol. 209, p. 107803, 2023, doi: 10.1016/j.compag.2023.107803.

Editorial & Review Transparency

Publication Date

6/16/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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