Year: 2024
Type: article
Abstract: Agriculture automation is becoming more and more sophisticated, utilizing Deep Neural Networks (DNN) and the Internet of Things (IoT) to create and implement a wide range of fine-grained controlling, monitoring, and tracking applications. Managing the interaction with the factors outside the agricultural ecosystem, such wildlife, is a pertinent open topic in this quickly changing situation. 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. Keywords: Animal Recognition, Repellent, Artificial Intelligence, Edge Computing, Animal Detection, Deep Learning, DCNN.
DOI: 10.55041/ijsrem37016
Authors: K Revathi, Dr.K.M ALAAUDEEN
Institution: Government Medical College Thoothukudi
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4401458174