Deployment of an autonomous fleet of UAVs for Assessing the NDVI of Regenerative Farming

Unmanned Aerial Vehicles (UAVs) have emerged rabidly as a new technology for crop farming promising to increase the productivity of a farm, on the one hand, and to reduce its farmer’s workload, on the other hand, through their integration with other technologies such as Machine Learning (ML), and Internet of Things (IoT). This article aims at developing a framework for deploying an autonomous fleet of UAVs for regenerative farming. Hence, the framework objective is twofold: firstly, is to develop an autonomous fleet of UAVs using Deep Reinforcement Learning (DRL), and, secondly, is to synchronize their flying and schedule their tasks using Ant Colony Optimization (ACO). The implementation of the framework shows great potential when using a set of indicators including, Normalized Difference Vegetation Index (NDVI), Mean Squared Error (MSE) and Received Signal Strength Index (RSSI), to assess its performance for Regenerative Farming.