groups, and one was used as a calibration data set and the other was used as a validation
data set, Table 4-8.
Table 4-8. Number of plots in calibration and validation data sets to develop prediction
models.
Calibration Validation Total Plots
22 22 44
Fruit/plot data for the 44 plots were sorted based on these three variables. Then,
alternate plots were chosen and combined into two groups, and one was used as the
calibration data set and the other was used as validation data set so that data was evenly
distributed throughout the entire range. Regression analysis was conducted between NA
and the variables: NPfrmes, NPplxels, and NPfruits-ptxels for the calibration data set, and the
results are shown in Figure 4-13, 4-14, and 4-15. A second-degree polynomial equation
was estimated using Excel to fit the data between NA and predicted number of fruits by
NPfrmes in a plot for the calibration data set. The R2 Value for the regression analysis was
0.47.
NPfrmes = -0.0339(M~Y frult s)2 + 17.112 pyfults
where M~yfruzes = number of fruits/plot counted by the fruit counting algorithm.
A second-degree polynomial equation was estimated using Excel to fit the data between
NA and predicted number of fruits by NPpxezs in a plot for the calibration data set. The R2
value for the regression analysis was 0.32.
NPpxezs = 0.00007(M11pyxezs)2) + 0.051M~Ypxezs 79.69
where M~pxezs = number of pixels/plot counted by the fruit counting algorithm