Skip to main content
ARS Home » Research » Publications at this Location » Publication #104488
Title: AIRBORNE DIGITAL IMAGERY AND YIELD MONITOR DATA FOR IDENTIFYING SPATIAL PLANT GROWTH AND YIELD PATTERNS

Author
item Everitt, James
item YANG, CHENGHAI - TX A&M EXPERIMENT STATION
item Bradford, Joe

Submitted to: Transactions of the ASAE
Publication Type: Proceedings
Publication Acceptance Date: 8/20/1999
Publication Date: N/A
Citation: N/A

Interpretive Summary: Remote sensing imagery is becoming a valuable data source for monitoring crop growth conditions and mapping crop yield variability for precision agriculture. Airborne images were obtained from a grain sorghum field five times during the 1998 growing season, and yield monitor data were collected from the field during harvest. The imagery clearly revealed plant growth patterns over the growing season. Statistical analyses showed grain yield was significantly related to the image data for the five dates and the best correlations were found with the images obtained shortly after peak growth. The yield map generated from the image data was very similar to the yield map generated from the yield monitor data. These results indicated that airborne digital imagery can be a useful data source for identifying plant growth and yield variability for precision farming.

Technical Abstract: In this study airborne digital imagery and yield monitor data were jointly used to map spatial and temporal plant growth and yield variability. Color-infrared images were acquired from a 17-ha grain sorghum field five times during the 1998 growing season, and yield monitor data were collected from the field during harvest. The imagery and unsupervised classification maps clearly reveal the consistency and change of plant growth patterns over the growing season. Correlation analyses showed grain yield was significantly related to each of the three image bands (near-infrared, red and green) and the normalized difference vegetation index for the five dates (r=0.13-0.75, p=0.0001). Stepwise regression was also used to relate yield to the three bands for each date (R-squared=0.373-0.662, p=0.0001) with the image obtained on May 29 (after peak growth) producing the highest R-squared value. The yield monitor data were then grouped into zones based on the classification maps for May 29 and mean yields differed significantly among the zones. The yield map derived from the May 29 image and the yield map generated from the yield monitor data agreed well. These results indicated that airborne digital imagery can be a useful data source for identifying plant growth and yield variability for precision farming.