Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the
coverage of the most important and influential journals
to meet the needs of the global scientific community.
Grayscale conversion of images is an important step in all image processing tasks. It is done to
minimize the complexity of processing a color image. Moreover, grayscale images preserves the brightness,
contrast, edges, shape, texture and structure of color images. Traditional methods use standard NTSC
coefficients for color to grayscale conversion. However, previous studies have revealed that the standard NTSC
coefficients are not optimal for all types of image classification problems. This study presents a study on color
to grayscale image conversion for classifying disease affected regions in a leaf from normal regions. We present
an optimization technique using Genetic Algorithm (GA) for color to grayscale image conversion. By using GA,
the coefficients for grayscale conversion are optimized to get minimum error in classification.
