Remote Sensing for Precision Agriculture: Within -Field Spatial Variability Analysis and Mapping With Aerial Digital Multispectral Images

161 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2000.Unsupervised clustering of color infrared (CIR) image of a field soil was able to identify soil mapping units with an average accuracy of 76%. Spectral reflectance from a crop field was highly correlated to the chlorophyll reading. A regression model developed to predict nitrogen stress in corn identified nitrogen-stressed areas from nitrogen-sufficient areas with a high accuracy (R2 = 0.93). Weed density was highly correlated to the spectral reflectance from a field. One month after planting was found to be a good time to map spatial weed density. The optimum range of resolution for weed mapping was 4 m to 4.5 m for the remote sensing system and the experimental field used in this study. Analysis of spatial yield with respect to spectral reflectance showed that the visible and NIR reflectance were negatively correlated to yield and crop population in heavily weed-infested areas. The yield potential was highly correlated to image indices, especially to normalized brightness. The ANN model developed for one of the research fields mapped spatial yield with 70% to 83% accuracy in different fields and seasons. The models at 6 m resolution performed better than the models at 3 m resolution. The best time to map yield potential of a field was after tasseling.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD