Abstract: Animal assaults that cause crop damage are one of the main factors lowering agricultural yields. Crop raiding is turning into one of the most vexing human-wildlife conflicts as a result of the extension of farmed land into former animal habitat. India's farmers face significant risks from pests, natural disasters, and animal damage, which lowers production. In order to monitor crops and deter wild animals, farmers cannot afford to pay guards and their traditional tactics are not very efficient. Given the equal importance of ensuring the safety of humans and animals, it is crucial to safeguard crops from animal damage and safely redirect animals away from crops. Crop striking is turning into one of the most acrimonious human-wildlife conflicts due to the expansion of cultivated land into former animal habitat. It is essential to thoroughly and effectively verify that wild animals are allowed to remain in their natural habitat. Therefore, we employ deep learning to identify animals visiting our farm by applying the deep neural network idea, a branch of computer vision, in order to overcome the aforementioned issues and achieve our goal. This suggested system would use a camera to capture the surrounding area all day long and monitor the entire farm at predictable periods. When an animal enters the area, the system uses a deep learning model to recognise it and plays the proper noises to scare it away.
Keywords: Convolutional Neural network, Deep learning, Remote monitoring, Alert system.
Cite:
Manjunath Hebbagilu, Dhanush, Ramnath Nayak, Sahil Faraz, Sanjay P "WILD ANIMAL DETECTION IN FARMLAND", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13392.
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DOI:
10.17148/IJARCCE.2024.13392
