Table 4-9. Performance comparison of the yield prediction model for 22 plots.
 Plot Number Actual yield Predicted Yield ErrorPlot o%)
 (fruits/m2) (fruits/m2)
 33 4.9 15.5 214.8
 79 25.2 18.9 -25.2
 90 34.3 22.6 -34.1
 63 33.4 26.7 -20.0
 36 32.7 28.1 -14.3
 27 44.1 33.3 -24.5
 28 40.5 34.6 -14.7
 26 26.4 36.7 39.3
 99 37.8 37.4 -1.1
 53 29.6 37.9 27.9
 127 43.1 38.7 -1 0.2
 41 60.4 39.6 -34.5
 30 28.7 40.9 42.5
 101 42.0 42.1 0.1
 56 38.8 42.6 9.7
 98 39.2 43.8 11.6
 35 34.2 43.9 28.5
 119 44.0 44.3 0.6
 80 66.6 45.7 -31.4
 91 51.6 47.5 -7.9
 128 40.5 47.5 17.4
 117 57.9 47.4 -18.1



 Before the experiment, it was considered that keeping the camera 5.2 m high and

focusing at 45 degree with respect to ground would cover maj ority of the tree canopy.

But during the field-testing, it was found that the resolution of the image was not good

with this setup. Hence, in order to take clear and high quality images, the camera lens

was zoomed in by a factor of two, thus covering small percentage of the tree canopy. If

multiple cameras were used to cover the maj ority of the tree canopy, then the model

could be used to predict yield with improved accuracy. A regression analysis was

conducted between the yield estimated by the yield prediction model and the actual yield

for 22 plots, Figure 4-16. The R2 Value for the regression analysis was 0.46, RMSE was

45.1 fruits/meter2 and CV was 70.42%.