Animal Intrusion Detection and Alert System for Crop Protection

Authors

  • Nitika Goyal Research Scholar, Desh Bhagat University, Mandi Gobindgarh
  • Navneet Kaur Sandhu Assistant Professor, Desh Bhagat University, Mandi Gobindgarh

Keywords:

YOLOv5, Animal Detection, IoT, Intrusion, Learning

Abstract

Detection of the wild animals in the field is still an open issue and needs to be addressed with an effective and accurate solution. By using the YOLOv5 network, this model has been trained to efficiently detect and track live animals in real-time. With the added advantage of IoT capabilities, real-time alerts are sent to the farmers, allowing them to take immediate actions. The system has been tested across various animal categories such as elephants, horses, cows, deer, rabbits, birds, and foxes. Preliminary results showcase the model's high accuracy rates, with most of the detections ranging between 85% to 95% .

Downloads

Download data is not yet available.

References

K. Chitra et al., "Animals Detection System In The Farm Area Using Iot," 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2023, pp. 1-6, doi: 10.1109/ICCCI56745.2023.10128557..

Lopez, B., and Martinez, F. (2018). Traditional agricultural practices: An assessment of effectiveness. Agricultural History Review, 66(1), 45-58.

Smith, C. (2020). Internet of Things in modern agriculture: Opportunities and challenges. Agricultural Informatics, 7(2), 110-123.

Chen, D. (2021). Technology in the fields: The changing face of modern agriculture. Journal of Tech and Agriculture, 12(4), 321-335.

Gomez, E. (2022). Privacy concerns in the age of digital farming. Journal of Rural Studies, 45(1), 11-23.

N Gobalakrishnan; K Pradeep; C J Raman; L Javid Ali; M P Gopinath, A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases, International Conference on Communication and Signal Processing (ICCSP), 28-30 July 2020, Chennai, India (2020).

R. R and P. Surekha, A Cohesive Farm Monitoring and Wild Animal Warning Prototype System using IoT and Machine Learning, 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). IEEE, Bengaluru, India (2020).

K. N. Bhanu; H. J. Jasmine; H. S. Mahadevaswamy, Machine learning Implementation in IoT based Intelligent System for Agriculture, International Conference for Emerging Technology (INCET) 05-07 June 2020, Belgaum, India (2020).

Marichamy P, Karuppasamy P, Heera B, Leena Keresi R, Kanagalakshmi , Crop protection from animals based on machine learning E3S Web Conf. 399 04018 (2023) DOI: 10.1051/e3sconf/202339904018

P. A. Mary G, N. P S, N. V and T. G V, "Wild Animal Detection System," 2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2023, pp. 1-6, doi: 10.1109/ICAECA56562.2023.10199701.Liu, IT professional, 22, 3 (2020).

Hung Nguyen; Sarah J. Maclagan; Tu Dinh Nguyen; Thin Nguyen; Paul Flemons; Kylie Andrews; Euan G. Ritchie, Dinh Phung, Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring, IEEE International Conference on Data Science and Advanced Analytics (DSAA), 19-21 October 2017, Tokyo, Japan (2017).

Yong Ai; Chong Sun; Jun Tie; Xiantao Cai, IEEE ACCESS, 8 (2020).

Sijiang Huang; Weijie Liu; Fei Qi; Kepeng Yang, Development and Validation of a Deep Learning Algorithm for the Recognition of Plant Disease, IEEE 21st International Conference on High Performance Computing and Communications 10-12 August 2019, Zhangjiajie, China (2019).

Muhammad Shoaib Farooq; Shamyla Riaz; Adnan Abid; Kamran Abid; Muhammad Azhar Naeem, New Technologies for Smart Farming 4.0: Research Challenges and Opportunities, 7 (2019).

Bhuvaneshwari C, Manjunathan A. Advanced gesture recognition system using long-term recurrent convolution network. Materials Today Proceedings, 21, 731-733 (2020)

C Bhuvaneshwari, A Manjunathan. Reimbursement of sensor nodes and path optimization. Materials Today: Proceedings, 45, 1547-1551 (2021)

Wild, T.A., van Schalkwyk, L., Viljoen, P. et al. A multi-species evaluation of digital wildlife monitoring using the Sigfox IoT network. Anim Biotelemetry 11, 13 (2023). https://doi.org/10.1186/s40317-023-00326-1

Surya T, Chitra Selvi S, Selvaperumal S, The IoT-based real-time image processing for animal recognition and classification using deep convolutional neural network (DCNN), Microprocessors and Microsystems, Volume 95, 2022, 104693, ISSN 0141-9331, https://doi.org/10.1016/j.micpro.2022.104693.

Downloads

Published

07.01.2024

How to Cite

Goyal, N. ., & Sandhu, N. K. . (2024). Animal Intrusion Detection and Alert System for Crop Protection. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 145–151. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4356

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.