Smart detection and repulsive system for animal intrusion

In the field of agriculture, there exists a conflict between animal and human, leading the human life in danger by losing a massive quantity of resources in the forest zone too. To address this issue, smart detection and repulsive system for animal intrusion is done using Deep Neural Network (DNN)....

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Bibliographic Details
Main Authors: Bharathi, Deepa, Palanisamy, Ramya, Moses, Leeban, Shanmugam, Karthikeyan, Rangasamy, Nirmala
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Computer vision
Edge computing
Electric fences
Intrusion
Neural networks
Repellents
Online Access:Get full text
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Summary:In the field of agriculture, there exists a conflict between animal and human, leading the human life in danger by losing a massive quantity of resources in the forest zone too. To address this issue, smart detection and repulsive system for animal intrusion is done using Deep Neural Network (DNN). Historically, this problem has been solved through both deadly (such as shooting or trapping) and non-lethal (such as educating the public) approaches (e.g., chemical repellents, electric fences). On the other hand, some of the more common approaches result in pollution that is harmful to humans and ungulates equally, while others are prohibitively expensive to maintain and provide only a minor degree of reliability and efficiency at best. The proposed research combines the use of AI Computer Vision with deep convolutional neural networks (DCNN) to detect and locate animals with the use of species-specific ultrasonic emission (i.e., each species of different types) to repel them. An ultrasonic signal tailored to the kind of animal detected is generated by the Animal Repelling Module and sent back to the camera via the edge-computing device.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0214577