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Title: AIRBORNE VIDEOGRAPHY TO IDENTIFY SPATIAL PLANT GROWTH VARIABILITY FOR GRAIN SORGHUM

Author
item YANG, CHENGHAI - TEX A&M,WESLACO,TX
item Anderson, Gerald

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/2/1997
Publication Date: N/A
Citation: N/A

Interpretive Summary: Remote sensing imagery is becoming a valuable data source for precision farming. In this study, aerial 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 a 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 patterns changed between the two seasons, although some areas exhibited stable patterns within both fields.

Technical Abstract: Much research has focused on the use of intensive grid soil sampling and yield monitors to identify within-field spatial variability in precision farming. This paper reports on the use of airborne videography to identify spatial plant growth variability for agricultural crops. Color-infrared (CIR) digital video images were 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 to determine plant density, plant height, leaf area index, biomass and grain yield. Results from both years showed that the digital video imagery identified within-field plant growth variability and that classification maps effectively differentiated grain production levels and growth conditions within the two fields. A comparison of the images and classification maps between the two successive growing seasons indicated that plant growth variation patterns differed somewhat from one season to the next, though areas exhibiting consistently high or low yield were found within each field for both years.