Application of UAV’s in Pasture Management: Case Study from Greenbrier Farm, South Carolina

Keywords: UAV’s in Agriculture, Greenbrier Farm, South Carolina, Pasture Management
Session Type: Virtual Poster
Day: Saturday
Session Start / End Time: 4/10/2021 03:05 PM (Pacific Time (US & Canada)) - 4/10/2021 04:20 PM (Pacific Time (US & Canada))
Room: Virtual 52

Abstract

A history of deforestation and cotton/corn farming have drained the soil in South Carolina of its nutrients and caused severe erosion of topsoil. Many farmers, battling the legacy impacts resorted to using synthetic fertilizers and pesticides to increase productivity. Some farmers are using crop rotation, no-till farming, and rotational grazing were implemented to help increase soil organic carbon (SOC) and soil quality. Previous studies have shown a close relationship between SOC and productivity. The question of how spatial variability in crop growth is related to that of SOC is not clearly understood in the piedmont region of SC. Our research used drone technology to estimate crop height and Normalized Difference Vegetation Index (NDVI) to compare it to field based SOC data. Using DJI’s P4 Multispectral RTK system, we captured over 17,000 images covering 180 acres of cattle pasture located at Greenbrier Farms in Easley, SC. The multispectral and RGB images were processed using Pix4d and ArcGIS Pro, to produce high resolution Digital Surface/Terrain Models (DSM/DTM), NDVI, and Crop Height Model. The ongoing pandemic has restricted us from collecting field data to verify crop height estimations from the drone data. Results from spatial analysis of NDVI, Crop Height Model, and SOC showed that both crop height and NDVI are negatively correlated with SOC. This is opposite of what was expected. We identified potential sources of problems with DTM calculation that is likely skewing the crop height calculation, which can be rectified using field data in future.