Skip to main content
ARS Home » Research » Publications at this Location » Publication #81756
Title: IDENTIFYING SPATIAL PLANT GROWTH VARIABILITY IN GRAIN SORGHUM USING AERIAL VIDEOGRAPHY

Author
item YANG, CHENGHAI - TAMU,WESLACO, TX
item Anderson, Gerald

Submitted to: Biannual Workshop in Color Photography and Videography in Resource
Publication Type: Proceedings
Publication Acceptance Date: 7/3/1997
Publication Date: N/A
Citation: N/A

Interpretive Summary: Remote sensing has been used to monitor plant growth conditions and obtain crop yield information for many years. More recently, video imaging technology has emerged as a useful data-gathering tool for precision farming. In this study, airborne digital video images were obtained from two grain sorghum fields in south Texas several times during the 1995 and 1996 growing seasons. Each image was grouped into several uniform management zones using an image processing program. A small number of ground samples were taken from each zone to determine plant growth characteristics such as plant height and grain yield. The results from both growing seasons showed that video imagery could be used to map within-field plant growth variations. This study also indicated that plant growth variation patterns changed between the two seasons, although some areas exhibited stable patterns within both fields.

Technical Abstract: Remote sensing imagery is becoming a valuable data source for precision agriculture. In this study, airborne color-infrared digital videography was acquired from two grain sorghum fields in south Texas several times during the 1995 and 1996 growing seasons. The video images were registered and classified into several zones of homogeneous spectral response using an unsupervised classification procedure. Ground measurements and plant samples were taken from a limited number of sites within each zone, and plant density, plant height, leaf area index, biomass and grain yield were determined. The results from both seasons showed that the digital video imagery could be used to identify within-field plant growth variability and that classified maps could effectively differentiate grain production levels and growth conditions within the two fields. A comparison of the images and classified maps between the two successive seasons indicated that plant variation patterns did change from season to season, though areas exhibiting consistently high or low yield were found within each field.