Animal Intrusion Detection using Deep Learning and Transfer Learning Approaches
Keywords:
Animal Intrusion Detection, Deep Learning, Transfer Learning, Image processing, CNN, VGG-16, VGG-19, MobileNetAbstract
One of the major risks to decreasing the harvest yield is crop damage caused by monster attacks. Crop attacking is one of the most aggravating conflicts between tamed and untamed life as a result of the expansion of developed land into former natural life habitat. India's ranchers are in grave risk from pests, natural calamities, and animal attacks that reduce production. Employing gatekeepers to keep an eye on crops and deter wild animals is not a practicable solution, contrary to conventional wisdom used by ranchers. Since the safety of both humans and animals is crucial, it is essential to protect the crops from animal-caused damage and to reroute the animal with little chance of mischief. In order to overcome these problems and get to our point, we employ artificial intelligence (AI) to detect animals as they enter our ranch using a division of computer vision known as a deep brain network. This paper promotes the ability to identify organisms in the natural world. Since there are so many different types of species, it might be difficult to physically discern between them. This algorithm organises animals based on their images so that we may more effectively filter them. In this paper, we will use a camera to take daily pictures of the surrounding area while simultaneously seeing the entire ranch at regular intervals. With the aid of sophisticated learning models, we can recognise the movement of creatures and play the appropriate sounds to scare them away. The various convolutional brain network libraries and concepts used to create the model are identified in this Paper.
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