fruits in a plot. The R2 ValUe WaS also very high among the three approaches. The model

was applied to the 22 plots in the validation data set to estimate the number of fruits/plot.

The number of fruits/plot estimated using fruits based on pixels/plot from the machine

vision algorithm was used in the yield prediction model.

 Yield predicted for the 22 plots in validation data set using NPfivats-plxels model is

tabulated in Table 4-9. The percentage error was as low as 0.1% for plot 101 and as high

as 214.8% for plot 33. The main cause for the high error rate was due to the fact that

using a single camera, it was not possible to cover the entire citrus tree. 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. Yield

estimation model depends on the imaging scene of a particular tree. If large distribution

of fruits on a particular tree were not captured on the image, the model would have

predicted very less yield than the actual harvested yield. On the other hand, if a tree with

low yield had fruits distributed over a dense region that was captured using the camera,

then the model would have predicted more yield than the actual harvested yield. Since

fruits were stretched throughout the tree canopy in irregular patterns, yield estimation

based on portion of a tree was not very successful. The smallest number of images

needed to estimate yield in a grove depends on the amount of variability present in the

grove. If the variability is very high, then acquiring images of many trees is a best

approach to predict the yield accurately. On the other hand if the yield were relatively

uniform, then acquiring images of some trees would be a better option to predict yield of

the grove accurately.