Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/29/2001
Publication Date: N/A
Citation: Interpretive Summary: Remote sensing imagery is becoming an important data source for precision agriculture. This study was designed to evaluate the relationships between grain sorghum yield and aircraft-based digital image data. Digital images were acquired from three grain sorghum fields on five different dates over a 62-day period during the 1998 growing season, and yield data were also collected from these fields using a grain yield monitor. These images clearly show plant growth patterns over the season. Statistical analyses showed significant correlations occurred between grain yield data and image data for each of the five dates. There was a distinct temporal progression in relations between yield and image data. The relations reached the strongest around the peak growth, indicating imagery taken during this period (approximately one month) could be a better indicator of yield for grain sorghum. The yield maps generated from the images agreed well with those from the yield monitor data. These results show that airborne digital imagery obtained during the growing season provides valuable crop growth and yield information and can be used to identify yield patterns for fields where yield monitor data are not available.
Technical Abstract: Remote sensing imagery taken during a growing season not only provides spatial and temporal information about crop growth conditions, but also is indicative of crop yield. This study aimed to evaluate the relationships between yield monitor data and airborne multispectral digital imagery for grain sorghum. Color-infrared (CIR) digital images were acquired from three grain sorghum fields on five different dates during the 1998 growing season, and yield data were also collected from these fields using a yield monitor. The images and the yield data were georeferenced to a common coordinate system. Four vegetation indices (two band ratios and two normalized differences) were derived from the green, red, and near-infrared (NIR) band images. The image data for the three bands and the four vegetation indices were aggregated to generate reduced-resolution images with a cell size equivalent to the combine's effective cutting width. Correlation analyses showed that grain yield was significantly related to the digital image data for each of the three bands and the four vegetation indices. Multiple regression analyses were also performed to relate grain yield to the three bands and to the three bands plus the four indices for each of the five dates. Images taken around peak vegetative development produced the best relationships with yield and explained approximately 63, 82, and 85% of yield variability for fields 1, 2, and 3, respectively. Yield maps generated from the image data using the regression equations agreed well with those from the yield monitor data. These results demonstrated that airborne digital imagery can be a very useful tool for determining yield patterns before harvest for precision agriculture.