Computer vision techniques using colour images are becoming popular in food and agriculture sector. Need of a standard illumination source is an important criterion in this approach to determine various attributes based on RGB values of the objects. In general, under laboratory conditions with standard lighting, an imaging system performs with high consistency in digitizing colour. However, in field conditions where the availability of a standard light source cannot be guaranteed, the colour interpretations may not yield accurate results. The objective of this study was to develop a simple algorithm to compensate for the variations in RGB values due to varying light conditions. It is intended to be useful in situations where taking digital images of objects without standard light sources is essential for a particular purpose. A set of quadratic transformation algorithms were developed to transform the RGB values of the images acquired under five different lighting conditions. The mean variance in RGB values of the image of a colour palette) with 6 different colours) taken under five lighting conditions were in the range of 277 – 548. After implementing the developed algorithm, this was reduced to 34 – 142. Similarly, this variance was reduced from 180 – 294 to 63 – 128 in the test conducted with a plant material. This algorithm can be easily adopted in all computer vision applications where variations in colour interpretations due to nonstandard lighting sources are common.