Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications

Abstract

In precision farming, image analysis techniques can aid farmers in the site-specific application of herbicides, and thus lower the risk of soil and water pollution by reducing the amount of chemicals applied. Using weed maps built with image analysis techniques, farmers can learn about the weed distribution within the crop. In this study, a digital camera was used to take a series of grid-based images covering the soil between rows of corn in a field in southwestern Quebec in May of 1999. Weed coverage was determined from each image using a “greenness method” in which the red, green, and blue intensities of each pixel were compared. Weed coverage and weed patchiness were estimated based on the percent of greenness area in the images. This information was used to create a weed map. Using weed coverage and weed patchiness as inputs, a fuzzy logic model was developed for use in determining site-specific herbicide application rates. A herbicide application map was then created for further evaluation of herbicide application strategy. Simulations indicated that significant amounts of herbicide could be saved using this approach.