acquisition time. The algorithm was tested on 329 validation images and the R2 ValUe

between the number of fruits counted by the machine vision algorithm and the average

number of fruits counted by human observers was 0.79. The variation in the number of

fruits correctly classified was partially due to clusters of citrus fruits, uneven lightning

and occlusion.

 Images belonging to a same plot were grouped together and the data from 22 plots

were used to predict fruit/plot with the following three variables:

 1) Number of fruit estimated using fruit counting algorithm (NPfruzes),

 2) Number of citrus pixels/plot estimated using fruit counting algorithm (NPpxezs)

 3) Number of fruits/plot estimated using citrus pixels/plot data (NPfruits-plxels).

 Yield prediction model was developed using NPfruits-ptxels Variable. The model was

applied over 22 plots and the R2 value between the yield predicted by the model and the

actual harvested yield was 0.46. The main cause for low R2 was due to the fact that using

a single camera, it was not possible to cover the entire citrus tree. Further, fruits that were

inside the canopy would have been completely occluded by leaves in the images. Hence,

the fruit counting algorithm was not able to identify these occluded fruits. The results

indicate that the yield prediction model could be enhanced by using multiple cameras for

covering the maj ority of tree canopy.

 5.2 Future Work

 Highly non-uniform illumination in an image presented a problem for color vision

based segmentation approach. One improvement to the present system would be to

improve the imaging of natural outdoor scenes with wide variation in illumination.

Automatic brightness control before imaging could be implemented by using a

phtotransistor to measure the intensity of the imaging scene and sending control to the