Camera Vision based Animal Beat System for Agriculture using Machine Learning

Authors

S.Shalini, K. Praveena Kamal

Abstract

Agriculture automation has been on the rise leveraging, among others, Deep Neural Networks (DNN) and IoT for the development and deployment of many controlling, monitoring and tracking applications at a fine-grained level. In this rapidly evolving scenario, managing the relationship with the elements external to the agriculture ecosystem, such as wildlife, is a relevant open issue. One of the main concerns of today's farmers is protecting crops from wild animals’ attacks. There are different traditional approaches to address this problem which can be lethal (e.g., shooting, trapping) and non-lethal (e.g., scarecrow, chemical repellents, organic substances, mesh, or electric fences). Nevertheless, some of the traditional methods have environmental pollution effects on both humans and ungulates, while others are very expensive with high maintenance costs, with limited reliability and limited effectiveness. In this project, we develop a system, that combines AI Computer Vision using DCNN for detecting and recognizing animal species, and specific ultrasound emission (i.e., different for each species) for repelling them. The edge computing device activates the camera, then executes its DCNN software to identify the target, and if an animal is detected, it sends back a message to the Animal Repelling Module including the type of ultrasound to be generated according to the category of the animal.

Keywords

Camera Vision based Animal Beat System for Agriculture using Machine Learning. S.Shalini, K. Praveena Kamal. 2024. IJIRCT, Volume 10, Issue 2. Pages 1-4. https://www.ijirct.org/viewPaper.php?paperId=2402011