Smart Farming Application for Protecting Animal Intrusion Using AI

Abstract

Abstract: Agriculture automation has been on the rise using, among others, Deep Neural Networks (DNN) and IoT for the development and deployment of numerous controlling, monitoring and shadowing operation at a fine-granulated position. In this fleetly evolving scenario, managing the relationship with the rudiments external to the farming ecosystem, similar as wildlife, is a applicable open issue. One of the main concerns of present cultivators is guarding crops from wild creatures’ attacks. There are different traditional approaches to address this problem which can be murderous (e.g., shooting, trapping) and non-lethal (e.g., scarecrow, chemical repellents, organic substances, mesh, or electric walls). Nonetheless, some of the traditional styles have environmental pollution effects on both humans and ungulates, while others are veritably precious with high conservation costs, with limited trustability 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 emigration (i.e., different for each species) for repelling them. The edge computing device activates the camera, and executes its DCNN software to determine the target, and once the animal is detected, it sends back a communication to the Animal Repelling Module including the type of ultrasound to be generated for the specific division of the animal.