Animal Enchroachment Detection in Croplands using Machine Learning Approaches
Article Sidebar
Main Article Content
Animal Enchroachment is a major threat to the productivity of the crops, which affects food security and reduces the farmer’s profit. Machine learning-based solutions are used to overcome this problem. Convolu- tional Neural Network (CNN), ResNet-50, and Inception v3 are the three methods used to identify the animals. The proposed model classifies the detected animals and alerts humans through a message to avoid animal intru- sions into properties. Hence, minimising the dangerous consequences caused by the intrusion. The Inception v3 model provides more accurate results compared to the other two models, and it is considered the main method for the proposed model.
Downloads
References
Mowen Xue, Theo Greenslade, Majid Mirmehdi, Tilo Burghardt, “Small or Far Away? Exploiting Deep Super- Resolution and Altitude Data for Aerial Animal Surveillance”, IEEE 2022
T.Sandeep, B.Manushree, S.Rahul, T.Bharath,“ Crop Protec- tion using CNN”, International Research Journal of Modern- ization in Engineering Technology and Science, March 2022.
Davide Adami, Mike O Ojo, Stefano Glordano,“ Design, De- velopment and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI ”, IEEE 2021.
Balakrishna K, Fazil Mohammed, C.R. Ullas, C.M. Hema,
S.K. Sonakshi, “Application of IOT and machine learning in crop protection against animal intrusion ”, Global Transitions Proceedings 2, no. 2 (2021): 169-174.
Devsmit Ranparia, Gunjeet Singh, Anmol Rattan, Dr Harpreet Singh, Dr Nitin Auluck, “ Machine learning based Acoustic Repellent System for Protecting Crops against Wild Animal Attacks ”, IEEE 2020.
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”, In 2020 5th International Conference on Information Technology Research(ICITR), 1-6.IEEE 2020.
N S Gogul Dev, K S Sreenesh,P K Binu, “ IoT Based Automated Crop Protection System ”, IEEE 2019.
Kuei-Chung Chang,Zi-Wen Guo, “ The Monkeys are Coming Design of Agricultural Damage Warning System by IoT- Based Objects Detection and Tracking ”, 2018 IEEE Interna- tional Conference on Consumer Electronics-Taiwan (ICCE- TW).
P. Badre, S. Bandiwadekar, P. Chandanshive, A. Chaudhari, and M.S. Jadhav, “ Automatically Identifying Animals Using Deep Learning”, vol. 6, no. 4, pp. 194–197, 2018.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.