Department of Computer Science and Engineering, School of Engineering and Technology, Sri Padmavathi mahila university (Tirupati), India.
Research Article
World Journal of Advanced Research and Reviews, 2022, 16(03), 767-774
Article DOI: 10.30574/wjarr.2022.16.3.1393
Received on 09 November 2022; revised on 20 December 2022; accepted on 23 December 2022
One of the greatest dangers to agricultural productivity is animal damage to agriculture. Crop raiding has become one of the most antagonistic human-wildlife conflicts as cultivated land has expanded into previous wildlife habitat. Farmers in India endures major risks from pests, natural disasters, and animal damage, all of which result in lesser yields. Traditional farming methods are unsuccessful and hiring guards to watch crops and keep animals at bay is not a practical solution. It is critical to protect crops from animal damage while also redirecting the animal without injuring it, as the safety of both animals and people is essential. To get over these obstacles and accomplish our goal, we employ the deep learning concept of convolutional neural networks, a subfield of computer vision, to identify animals as they enter our farm. The primary goal of this project is to constantly monitor the entire farm using a camera that records the surroundings at all hours of the day. We identify animal infiltration using a CNN algorithm and Xgboost and notify farmers when this occurs.
CNN; XG Boost; Computer vision; Deep learning
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K Bhumika, G Radhika and CH Ellaji. Detection of animal intrusion using CNN and image processing. World Journal of Advanced Research and Reviews, 2022, 16(3), 767-774. Article DOI: https://doi.org/10.30574/wjarr.2022.16.3.1393