usages include edge detection, noise removal, image enhancement and image

segmentation. In order to process binary images, the following operations were

performed in this research: erosion, dilation and closing. Erosion shrinks the boundary of

a binary image to be smaller, while dilation expands the boundary to be larger depending

on a structuring kernel. Closing is a morphological operation, which consists of applying

dilation and, immediately, erosion to fi11 in small background holes in images.

 Due to the dissimilarity in illumination between the images and the presence of

some dead leaves, certain pixels were falsely classified as fruits. Using the set of

calibration images, immediately after binarization, a threshold was applied based on area

of the selected features to remove false detections. In some of the fruits detected, a few

pixels mostly at the center of the fruit were classified as background due to very high

illumination. The kernel sizes for filling the gaps were determined by applying kernels of

various sizes and of various orders over the calibration images. From this trial, the order

of the erosion and dilation and the optimum kernel size was selected for the algorithm.

Using the set of calibration images, a closing operation with structuring kernel (5 x 5)

was applied to fi11 these gaps. These image-processing steps are shown in Figure 3-5.

Citrus fruits were identified using blob analysis and in this method, connected fruit pixels

were treated as a single fruit. Fruit features such as area was extracted for all fruits and

stored in a text fie for post processing. Fruit area is defined as the number of pixels in a

connected region and compactness value is derived from the perimeter and area for each

blob. Based on the average size of a fruit, a threshold was used to identify and consider

cluster of fruits while determining the total number of fruits in an image. With these