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

✍ Scribed by Chun-Chieh Yang; Shiv O. Prasher; Jacques-André Landry; Robert Kok

• Publisher: Elsevier Science
• Year: 2002
• Tongue: English
• Weight: 543 KB
• Volume: 81
• Category: Article
• ISSN: 1537-5110
• DOI: 10.1006/bioe.2002.0006

✦ Synopsis

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 "rst 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 "lters 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)8 s per image, compared to 83}88 s per image by the other two approaches applying the greenness method to di!erent image formats without the vectorization function. Using digital images of a maize "eld taken in 1999, the weed distribution on a portion of the "eld was mapped. The results showed that only 59% of the maize "eld 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-speci"c application of herbicides in order to reduce herbicide costs and environmental pollution.