Skip to main content
ARS Home » Research » Publications at this Location » Publication #110255
Title: AIRBORNE MULTISPECTRAL DIGITAL IMAGERY FOR DETECTING PLANT GROWTH AND YIELDVARIABILITY FOR A GRAIN SORGHUM FIELD

Author
item YANG, CHENGHAI - TX A&M UNIV. EXP'T. STN.
item Everitt, James
item Bradford, Joe
item Escobar, David

Submitted to: Intnl Conference On Geospatial Information In Agriculture And Forestry
Publication Type: Proceedings
Publication Acceptance Date: 3/15/2000
Publication Date: N/A
Citation: N/A

Interpretive Summary: Remotely sensed imagery is useful for monitoring crop growth conditions and mapping within-field yield variations for precision agriculture. This paper reports on the use of aircraft-based images for detecting spatial and temporal plant growth variability and for estimating grain yield as compared with yield monitor data. Remote sensing images were acquired from ma grain sorghum field five times during the 1998 growing season, and yield data were also collected from the field using a grain yield monitor. These images clearly show plant growth patterns over the growing season. Statistical analyses showed there existed significant correlations between grain yield data and image data for the five dates. The image obtained shortly after peak growth produced the highest correlation. The yield map generated from this image agreed well with that from the yield monitor data. These results indicate that aircraft-based remote sensing can be an effective tool for detecting spatial and temporal plant growth and yield variability in precision farming.

Technical Abstract: 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 daa were also collected from the field using a yield monitor. The images clearly reveal the consistency an 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-squared value (0.79). Yield maps generated from this image and from the yield monitor data agree well. These results indicate that airborne digital imagery can be useful in precision agriculture for detecting spatial and temporal plant growth and yield variability.