MONITORING THE IMPACT OF UREA DEEP PLACEMENT ON RICE (ORYZA SATIVA l.) GROWTH AND GRAIN YIELD USING UNMANNED AERIAL SYSTEMS TECHNOLOGY (DRONES)

Abstract

Unmanned aerial systems (UAS)-based remote sensing technology is used in precision agriculture due to its ability to monitor individual crop growth, health etc. which was previously unlikely on a wide scale. In this study, UAS technology was used in three zones of the Tono Irrigation Scheme (TIS) in the Upper East region of Ghana to assess the effect of urea deep placement (UDP) compared to non-UDP Nitrogen (N) management systems on rice grain yields. The goal of the study was to capture multispectral images on a fixed wing (eBee) platform at the midseason rice crop stage and develop vegetation indices (VI’s) to study in-field variations in rice yields as a function of N management system. These images were processed through the eMotion 3 software and transferred into Pix4D to create high resolution seamless orthomosaics. Four different vegetation indices (NDVI, NDRE, OSAVI, and GNDVI) were produced. Normal difference vegetation index (NDVI) was used to identify high, medium and low crop health zones. End of season rice grain yields in the health zones were in the order high > medium > low. Rice grain yields had the highest correlation with OSAVI (r = 0.50). Two approaches were used to assess the impact of non-UDP compared to UDP. In the plot scale evaluation, estimated grain yields in non-UDP and UDP fields were 6.14 MT/ha and 6.74 MT/ha, respectively and were statistically different. Variability was high in non-UDP fields compared to UDP fields. In the geospatial approach based on Jenks classified OSAVI maps, similar relationships were obtained. Results showed that UAS technology can be efficient in estimating end of season rice grain yields and their variability in fields based on mid-season multispectral data.