Weeds typically grow in patches across agricultural landscapes. Because of this characteristic growth pattern, it seems logical to apply herbicides site-specifically to control them. To do this effectively, methods must be identified to accurately map weed presence and make cost effective herbicide application decisions to control them. The primary objective of this research was to develop a site-specific herbicide decision support system. Additional objectives include evaluating the effects of sampling patterns and interpolation techniques for weed mapping accuracy and evaluating texture analysis for weed patch detection in row-crops. A geographic information system (GIS) extension was developed to work in conjunction with a commercial software component for calculating site-specific herbicide applications based on user input weed maps. Results of the extension were compared to that of the commercial software. The GIS extension was able to accurately develop herbicide options based on the given weed densities and potential net return for treatment of the weeds in any specific area of the field. Sampling techniques and interpolation methods were compared to assess the accuracy of each pattern/method combination. The patterns used in this study were the W- and Z-shaped pattern, and the interpolation methods used were kriging and inverse distance weighted. Neither the pattern nor method impacted the results of the predicted average values for a given weed species. The last objective addressed was texture analysis? ability to distinguish weed patches in row-crops. Texture analysis was also tested to determine its ability to distinguish between areas requiring a herbicide application and areas not requiring a herbicide application. The analysis was performed on 3 vegetative indices and the NIR band of multispectral imagery at three different spatial resolutions (0.14 m, 0.5 m, and 1 m), and for two dates in the growing season. Texture analysis performed better on late season for both scenarios, with the highest classification accuracies (45 to 75%) coming from distinguishing areas that were below a given weed threshold from those that were above.

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