Airborne multispectral digital imagery for detecting plant growth and yield variability for a grain sorghum field

摘要

This paper describes the use of airborne multispectral digital imagery for detecting spatial and temporal plant growth variability and for estimating grain yield as compared with yield monitor data. Color-infrared (CIR) digital images were acquired from a 20.5-ha grain sorghum field five times during the 1998 growing season, and yield data were also collected from the field using a yield monitor. The images clearly reveal the consistency and change of plant growth patterns over the growing season. Correlation analyses showed grain yield was significantly related to the digital count data of each of the three bands (near-infrared, red and green) of the CIR composite images and the normalized difference vegetation index (NDVI) for the five dates. Stepwise linear regression was also used to relate yield to the digital data of the three bands for each date. The image obtained shortly after peak growth produced the highest R~2-value (0.79). Yield maps generated from this image and from the yield monitor data agreed well. These results indicate that airborne distal imagery can be useful in precision agriculture for detecting spatial and temporal plant growth and yield variability.