camera to exploit high contrasting colors of orange to implement a color image-

processing algorithm to distinguish orange from a typical image of a citrus grove.

 Whittaker et al. (1987) used fruit shape rather than the color information to detect

tomatoes. This method could be used even in the presence of interference caused by

bright reflection and when fruits were shaded. They used circular Hough

transform (CHT) to locate fruit or part of fruit in the image. CHT is a mathematical

transform that uses angle matrix and range of radii to locate circles or part of circles in a

digital image having discrete pixels. Before applying circular Hough transform, the

image was passed through a Sobel gradient operator, which calculated the gradient

magnitude and direction at each pixel point. Using this method, partially occluded fruits

could also be detected.

 Slaughter and Harrell (1987; 1989) were involved in the development of a robotic

fruit harvesting system and presented two approaches for detecting the fruit in an image

based on color information. In the first approach (Slaughter and Harrell, 1987), the hue

and saturation components of each pixel were used as features to segment an image by

applying a traditional classification in a bi-dimensional feature space. The segmentation

was carried out using a maximum and minimum threshold for each feature. Since color

segmentation required some form of illumination control, they used an artificial lighting

system. In the second approach (Slaughter and Harrell, 1989), a classification model was

developed for discriminating oranges from the natural background of an orange grove

using only color information. A Bayesian classifier was used in the RGB color space and

fruits were segmented out from the background by checking whether they belonged to the