A Synthesis of Machine Learning and Internet of Things in Developing Autonomous Fleets of Heterogeneous Unmanned Aerial Vehicles for Enhancing the Regenerative Farming Cycle

Abstract

The use of Unmanned Aerial Vehicles (UAVs) for agricultural monitoring and management offers additional advantages over traditional methods, ranging from cost reduction to environmental protection, especially when they utilize Machine Learning (ML) methods, and Internet of Things (IoT). This article presents an autonomous fleet of heterogeneous UAVs for use in regenerative farming the result of a synthesis of Deep Reinforcement Learning (DRL), Ant Colony Optimization (ACO) and IoT. The resulting aerial framework uses DRL for fleet autonomy and ACO for fleet synchronization and task scheduling inflight. A 5G Multiple Input Multiple Output-Long Range (MIMO-LoRa) antenna enhances data rate transmission and link reliability. The aerial framework, which has been originally prototyped as a simulation to test the concept, is now developed into a functional proof-of-concept of autonomous fleets of heterogeneous UAVs. For assessing performance, the paper uses Normalized Difference Vegetation Index (NDVI), Mean Squared Error (MSE) and Received Signal Strength Index (RSSI). The 5G MIMO-LoRa antenna produces improved results with four key performance indicators: Reflection Coefficient (S11), Cumulative Distribution Functions (CDF), Power Spectral Density Ratio (Eb/No), and Bit Error Rate (BER).