An intelligent edge enabled 6G-flying ad-hoc network ecosystem for precision agriculture

Abstract

Unmanned aerial vehicle based precision agriculture is a predominant research area. The modern flying ad-hoc network leverages the advanced low latency vehicular communication and intelligent computing paradigms that help the ecosystem to grow up to the next level. In this work, we propose an ecosystem for precision agriculture that leverages the use of the opportunistic MQTT protocol in an edge-enabled intelligent drone network for sensing and performing crop prediction using an intelligent ensemble machine learning model. The proposed approach leverages the edge computing system that requires low energy devices and also exploits the ultra-low latency opportunistic message transfer methodology. The experimental results show the maximum of 0.9 message delivery ratio and a minimum of 600 ms latency is achieved by opportunistic MQTT protocol in an ultra-low latency sparse network scenario. A weighted ensemble model is deployed onto the edge enabled devices or the drones. An accuracy of 96.5% is achieved in predicting the type of crops that can be grown in the soil about the selected area of interest.