A Deep Learning-Based Model for the Detection and Tracking of Animals for Safety and Management of Financial Loss
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Abstract
The paper presents a deep learning-based model for detecting and tracking animals to protect agricultural fields. It addresses the challenge of crop destruction by stray animals, a significant issue for farmers, by proposing an intelligent camera-based solution. This solution utilizes IoT for real-time information transfer and employs advanced image and video processing methods, including YoloV5, for efficient animal detection and classification. The study evaluates various existing solutions and highlights the advantages of the proposed model in terms of accuracy and cost-effectiveness, offering a promising approach to mitigate animal raids on crops.
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References
K. Balakrishna, F. Mohammed, C. R. Ullas, C. M. Hema, and S. K. Sonakshi, “Application of IOT and machine learning in crop protection against animal intrusion,” Global Transitions Proceedings, vol. 2, no. 2, pp. 169–174, Nov. 2021, doi: 10.1016/j.gltp.2021.08.061.
Rwetsiba and T. Rusoke, “CROP PROTECTION INTERVENTIONS AND WILDLIFE DAMAGE MANAGEMENT AROUND KIBALE NATIONAL PARK, UGANDA,” International Journal of Agriculture, Environment and Bioresearch, vol. 06, no. 04, pp. 88–128, 2021, doi: 10.35410/ijaeb.2021.5653.
K. Mahajan, R. Parate, E. Zade, S. Khante, and S. Bagal, “REVIEW PAPER ON SMART CROP PROTECTION SYSTEM,” International Research Journal of Engineering and Technology, 2021, [Online]. Available: www.irjet.net
S. Giordano, I. Seitanidis, M. Ojo, D. Adami, and F. Vignoli, “IoT Solutions for Crop Protection against Wild Animal Attacks,” IEEE Access, 2018.
Vayssade, JA., Arquet, R., Troupe, W. et al. CherryChèvre: A fine-grained dataset for goat detection in natural environments. Sci Data 10, 689 (2023). https://doi.org/10.1038/s41597-023-02555-8.
S. Santhiya, Y. Dhamodharan, N. E. Kavi Priya, C. S. Santhosh, and M. Surekha, “A SMART FARMLAND USING RASPBERRY PI CROP PREVENTION AND ANIMAL INTRUSION DETECTION SYSTEM,” International Research Journal of Engineering and Technology, vol. 5, no. 3, 2018, [Online]. Available: www.irjet.net
C. A. Kumar, S. R. H, and S. M. S Asst, “IOT in Agricultural Crop Protection and Power Generation; IOT in Agricultural Crop Protection and Power Generation,” International Journal of Engineering Research & Technology (IJERT), vol. 9, no. 5, 2020, [Online]. Available: www.ijert.org
S. L. Shylaja, S. Fairooz, J. Venkatesh, D. Sunitha, R. P. Rao, and M. R. Prabhu, “IoT based crop monitoring scheme using smart device with machine learning methodology,” in Journal of Physics: Conference Series, Sep. 2021, vol. 2027, no. 1. doi: 10.1088/1742-6596/2027/1/012019.
S. Deepa and A. Professor, “Implementation of Crop Protection System against Wild Animals Attack,” Annals of R.S.C.B, vol. 25, no. 5, pp. 3638–3646, 2021, [Online]. Available: http://annalsofrscb.ro
R. Agale, “2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA),” 2017.
R. Anusha, T. Nirmala, and N. T. Chitra, “SYSTEM FOR SMART PROTECTION OF CROP USING IOT,” International Journal of Grid and Distributed Computing, vol. 13, no. 2, pp. 1707–1715, 2020.
L. S. and H. B., “Design and Implementation of IOT based Smart Security and Monitoring for Connected Smart Farming,” International Journal of Computer Applications, vol. 179, no. 11, pp. 1–4, Jan. 2018, doi: 10.5120/ijca2018914779.
H. Zhu, H. Wei, B. Li, X. Yuan, and N. Kehtarnavaz, “A review of video object detection: Datasets, metrics and methods,” Appl. Sci., vol. 10, no. 21, pp. 1–24, 2020, doi: 10.3390/app10217834.