|
Wild Animals Intrusion Detection using Deep Learning Techniques
|
|
| Author:
|
R.S. SABEENIAN, N. DEIVANAI, B. MYTHILI
|
| Abstract:
|
Crop damage caused by animal attacks is one of the major threats in reducing the crop yield. Due to the expansion of cultivated land into previous wildlife habitat, crop raiding is becoming one of the most antagonizing human-wildlife conflicts. Farmers in India face serious threats from pests, natural calamities & damage by animals resulting in lower yields. Traditional methods followed by farmers are not that effective and it is not feasible to hire guards to keep an eye on crops and prevent wild animals. Since safety of both human and animal is equally vital, it is important to protect the crops from damage caused by animal as well as divert the animal without any harm.
Thus, in order to overcome above problems and to reach our aim, we use machine learning to detect animals, entering into our farm by using deep neural network concept, a division in computer vision. In this project, we will monitor the entire farm at regular intervals through a camera which will be recording the surrounding throughout the day. With the help of a machine learning model, we detect the entry of animals and we play appropriate sounds to drive the animal away. This report specifies various libraries and concepts of convolutional neural networks used to create the model.
|
| Keyword:
|
Convolutional Neural Network, Deep learning, Prediction, Training and Validation, Play Sound.
|
| EOI:
|
-
|
| DOI:
|
https://doi.org/10.31838/ijpr/2020.12.04.164
|
| Download:
|
Request For Article
|
|
|