Using Unmanned Aerial Systems Technology to Characterize the Dynamics of Small-Scale Maize Production Systems for Precision Agriculture

Abstract

Precision agriculture (PA) utilizes spatial and temporal variability to improve the sustainability and efficiency of farming practices. This study used high-resolution imagery from UAS to evaluate maize yield variability across three fields in Ghana: Sombolouna, Tilli, and Yendi, exploiting the potential of UAS technology in PA. Initially, excess green index (EGI) classification was used to differentiate between bare soil, dead vegetation, and thriving vegetation, including maize and weeds. Thriving vegetation was further classified into maize and weeds, and their corresponding rasters were developed. Normal difference red edge (NDRE) was applied to assess maize health. The Jenks natural breaks algorithm classified maize rasters into low, medium, and high differential yield zones (DYZs). The percentage of bare spaces, maize, weed coverages, and total maize production was determined. Significant variations in field conditions showed Yendi had 34% of its field as bare, Tilli had the highest weed coverage at 22%, and Sombolouna had the highest maize crop coverage at 73.9%. Maize yields ranged from 860 kg ha−1 in the low DYZ to 4900 kg ha−1 in the high DYZ. Although yields in Sombolouna and Tilli were similar, both fields significantly outperformed Yendi. Scenario analysis suggested that enhancing management practices to elevate low DYZs to medium levels could increase production by 2.1%, while further improvements to raise low and medium DYZs to high levels could boost productivity by up to 20%.

Keywords

• differential yield zones
• orthomosaics
• excess green index (EGI)
• normal difference red edge (NDRE)
• Jenks natural breaks algorithm
• precision agriculture