An Integrated System for Mapping Red Clover Ground Cover Using Unmanned Aerial Vehicles: A Case Study in Precision Agriculture

In the field of precision agriculture (PA), Un-manned Aerial Vehicles (UAVs) are creating new opportunities for remotely assessing various characteristics of crops. In this paper, we present two main contributions that were evaluated on a novel application: mapping red clover ground cover (RCGC). First, we develop an integrated system for collecting, pre-processing and analyzing aerial data for the mapping of RCGC at a patch-level. Second, we collected, ground-trusted, and pre-processed a RCGC dataset that we make public for further analysis. We evaluated several different machine learning classifiers for mapping image patches to discrete clover coverage levels, reaching an accuracy of 91%.