first maj or task of a yield mapping system using a machine vision system. Citrus fruits

are distributed in a strip about one meter deep within the canopy, in a completely

unstructured environment. Detecting the citrus fruit on the tree involves many complex

operations. Automatic visual identification of fruit is further complicated by variation in

lighting conditions from bright sunlight on the outer parts of the canopy to deep shadow

within the canopy. Citrus fruits often grow in clusters and also some of the fruits are

occluded by branches and foliage.

 Fruit distribution was studied (Juste et al., 1988) with 'Salustiana' and

'Washington' navel using a system of cylindrical coordinates and it appears that around

80% of fruits were between the outer boundary and at a distance of 1 m 1.4 m from the

outer canopy. But in the case of mandarins most of the fruits were at a distance of 0.75 m

from the outer canopy. Distribution of fruit clusters in citrus trees was studied (Schertz

and Brown, 1966) for six navel orange trees in Tulane County, California. An evolution

of fruit clusters showed that 68 percent of the fruits were borne as separate fruits, 19

percent in clusters of two, and 7 percent in clusters of three. The remaining 6 percent had

four through eleven fruits per cluster.

 2.5 Image Segmentation

 Some of the earlier studies regarding fruit recognition were conducted for apple,

citrus and tomatoes. Parrish and Goskel (1977) developed the earliest prototype for an

apple harvester and studied the feasibility of harvesting methods based on pictorial

pattern recognition and other artificial intelligence techniques. The prototype used a

standard black-and-white camera, to acquire apple images, and a pattern recognition

method to guide the fruit-harvesting robot. Slaughter and Harrell (1989) used a color