AE—Automation and Emerging Technologies: A Vegetation Localization Algorithm for Precision Farming

Abstract

The primary goal of this study was to develop the image processing component of a weed detection and mapping system that can be applied to precision farming. The image processing algorithm first located the green objects in the digital image, then calculated weed density based on the greenness ratio. The greenness ratio was the portion of an image in which the green intensity was larger than the red and blue intensities. Some smoothing filters were also investigated to remove noise from the image. Three approaches to determine the greenness ratio were evaluated and compared to the manual survey using a planimeter. With the similar greenness ratios obtained from these approaches, the approach using MATLAB built-in function ‘vectorization of loops’ gave comparable results to those using a planimeter in the shortest amount of time, 0·8s per image, compared to 83–88s per image by the other two approaches applying the greenness method to different image formats without the vectorization function. Using digital images of a maize field taken in 1999, the weed distribution on a portion of the field was mapped. The results showed that only 59% of the maize field required full herbicide application based on having a weed density of 5% or more. The proposed image processing method is a simple and fast way to detect weeds with a commercial digital camera. It can become an important component of a weed detection and mapping system by assisting in site-specific application of herbicides in order to reduce herbicide costs and environmental pollution.