CFP last date
20 January 2025
Reseach Article

A Review of Existing Farmland Intrusion Detection Systems

by Iyinoluwa Moyosola Oyelade, Olutayo Kehinde Boyinbode, Olumide Adewale
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 22
Year of Publication: 2023
Authors: Iyinoluwa Moyosola Oyelade, Olutayo Kehinde Boyinbode, Olumide Adewale
10.5120/ijca2023922969

Iyinoluwa Moyosola Oyelade, Olutayo Kehinde Boyinbode, Olumide Adewale . A Review of Existing Farmland Intrusion Detection Systems. International Journal of Computer Applications. 185, 22 ( Jul 2023), 41-46. DOI=10.5120/ijca2023922969

@article{ 10.5120/ijca2023922969,
author = { Iyinoluwa Moyosola Oyelade, Olutayo Kehinde Boyinbode, Olumide Adewale },
title = { A Review of Existing Farmland Intrusion Detection Systems },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 22 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number22/32826-2023922969/ },
doi = { 10.5120/ijca2023922969 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:47.149807+05:30
%A Iyinoluwa Moyosola Oyelade
%A Olutayo Kehinde Boyinbode
%A Olumide Adewale
%T A Review of Existing Farmland Intrusion Detection Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 22
%P 41-46
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Several methods for detecting intruders on a farmland be it humans or animals has evolved overtime from traditional to evolving technologies. Some of the traditional methods incorporate structure block walls around the farm, introducing deterrent plants, electric fences or plants that give out displeasing scents while a few of the evolving technologies include but not limited to wireless sensor networks, deep learning algorithms and internet of things. This paper provides a review of existing farmland intruder detection solutions of different authors with focus on methods ranging from electric fences to Wireless Sensor Networks to Internet of Things (IoT).

References
  1. O. Ajayi and O. Olaifa, “Detecting Intrusion in Large Farm Lands and Plantations in Nigeria Using Virtual Fences,”2017 unpublished.
  2. A.P. Shaji, “Raspberry Pi based real time monitoring of Agricultural and Irrigation using IoT,” IJEDR, vol. 6, pp 652-656, 2018
  3. S. Angadi and R. Katagall, “Agrivigilance: A Security System for Intrusion Detection In Agriculture Using Raspberry Pi And Opencv,” IJSTR, vol 8, pp 1261-1267, 2019
  4. O. Cosido, A. Iglesias, A. Galvez, R. Catuogno, M. Campi, L. Terán and E. Sainz, “Hybridization of Convergent Photogrammetry, Computer Vision, and Artificial Intelligence for Digital Documentation of Cultural Heritage-A Case Study,” IEEE Cyberworld Intl. Conf., pp. 369-376
  5. H. Shetty, H. Sing and F. Shaikh, “Animal Detection using Deep Learning,” IJESC, vol. 11, pp 28059-28061, 2021.
  6. F. Schindler and V. Steinhage, “Identification of Animals and Recognition of their Actions in Wildlife Videos using Deep Learning Techniques,” IJCEED, vol. 61, 2021.
  7. K. He, G. Gkioxari, P. Dollar, R. Girshick, Mask R-CNN. Computer Vision and Pattern Recognition, ARXIV Cornell University, 2017
  8. X. Zhu, Y. Wang, J. Dai, L. Yuan, Y. Wei, “Flow-guided feature aggregation for video object detection,” IEEE Intl. Conf. on Computer Vision, 2017
  9. K. Paramasivam, S. Krishnaveni, S. Sowndarya and E. Kavipriya, “Convolutional Neural Network Based Animal Detection Algorithm for Partial Image,” AJ, vol. 8, pp. 1461-1469, 2020
  10. A.V. Sayagavi, T.S.B. Sudarshan and P.C. Ravoor, “Deep Learning Methods for Animal Recognition and Tracking to Detect Intrusions,” ICTIS, col 196, pp.617-626, 2021
  11. A. Singh, M. Pietrasik, G. Natha, N. Ghouaiel, K. Brizel and N. Ray, “Animal Detection in Man-made Environments,” ARXIV Cornell University, 2020
  12. R.S. Sabbbnian, N. Deivanai and B. Mythili, “Wild Animal Intrusion Detection Using Deep Learning Techniques,” IJPR, vol. 12, pp. 1053-1058, 2020
  13. S. Mohandass, S. Sridevi and R. Sathyabama, “A Unified approach to Animal Intrusion Detection for preventing Human-Wildlife Conflict and Crop Protection,” JCR, vol. 7, pp. 8456-8468, 2020.
  14. N. Banupriya, R. Swaminathan, S. Harikumar and S. Palanisamy, “Animal Detection Using Deep Learning Algorithm,” Journal of Critical Reviews, vol 7, pp 434-439, 2020
  15. P.C. Ravoor, T.S.B. Sudarshan and K. Rangarajan, “Digital Borders: Design of an Animal Intrusion Detection System Based on Deep Learning,” CVIP, vol. 1378, pp. 186-200, 2021
  16. B.H. Muneera, D.A. Janeera and A.G. Kumar, “Internet of Things based Wild Animal Infringement Identification, Diversion and Alert System,” ICICT, pp. 801-805
  17. S. Vidhya, T. Vishwashankar, K. Akshaya, P. Aiswarya and R. Rohith, “Smart Crop Protection using Deep Learning Approach,” IJITEE, vol 8, pp 2278-3075, 2019
  18. R. Mythili, M. Kumari, A. Tripathi A. and N. Pal, “IoT Based Smart Farm Monitoring System,” IJRTE, vol 8, pp. 2277-3878, 2019
  19. P. Prajna, B. Soujanya and S. Divya, “IoT-Based Wild Animal Intrusion Detection System,” IJERT, vol. 5, pp. 2278-2285, 2018.
  20. E.O. Ibam, M.O. Afolabi, O.J. Idowu and A.O. Idowu, “Design and Implementation of Farm Monitoring and Security System,” IJCA, vol. 181, pp. 0975-0983, 2018.
  21. K.O. Iyapo, O.M. Fasunla, S.A. Egbuwalo, A.J. Akinbobola and O.T. Oni, “Design and Implementation of Motion Detection Alarm and Security System,” IJEAT, vol. 6, pp. 26-38, 2018.
  22. S. Santhiya, Y. Dhamodharan, N. Priya, C. Santhosh and M. Surekha, “Smart Farmland Using Raspberry Pi Crop Prevention and Animal Intrusion Detection System,” IRJET, vol. 5, 2018.
  23. A. Nagaraju and K. Valli, “Wild-Animal Recognition in Agriculture Farms Using W-COHOG for Agro-Security,” IJCIR, vol. 13, pp. 0973-1873, 2017.
  24. B. Varsha, K. Prasad, S. Vijaykumar, D. Neha and S. Arvind, “WSN Application for Crop Protection to Divert Animal Intrusions in the Agricultural Land,” Computers and Electronics in Agriculture, vol. 8, pp. 88-96, 2017.
  25. S.K. Nagpal and P. Manojkumar, “Hardware Implementation of Intruder Recognition in a Farm through Wireless Sensor Network,” 2017, unpublished.
  26. T. Mahajan and J. Mahajan, “IOT based Agriculture Automation with Intrusion Detection,” IJSTA, vol. 2, pp. 269-274, 2016.
  27. S.K. Roy, A. Roy, S. Misra, N.S. Raghuwanshi and M.S. Obaidat, “AID: A Prototype for Agricultural Intrusion Detection Using Wireless Sensor Network,” IEEE Communications Software, Services and Multimedia Applications Symposium, pp.7059-7064.
  28. M. Kumar, S. Kaul, V.K. Singh and V.A. Bohara, iDART-Intruder Detection and Alert in Real Time, Wirocomm Research Group, Indraprastha Institute Of Information Technology, New Delhi, 2015.
  29. W.K. Wong, Z.Y. Chew, C.K. Loo and W.S. Lim, “An Effective Trespasser Detection System Using Thermal Camera,” IEEE ICCRD, pp.702-706
Index Terms

Computer Science
Information Sciences

Keywords

Internet of Things (IoT) Farmland Intrusion Detection Deep Learning Wireless Sensor Networks Deep learning.