## 3 Results
### Models for Relationship between Yield Parameters and VIs  
The scatter plots of the optimal VI for each of the measured yield parameters derived from the 18 trees sampled in 2014, 90 trees sampled in 2016 and 90 trees sampled in 2017 are shown in Figure 4. For both total fruit weight (kg\(\cdot\)tree\({}^{-1}\)) and average fruit size (g) per tree, RENDVI1 produced the highest coefficient of determination across all three years when fitted with an exponential curve (R\({}^{2}\) = 0.45, 0.28, and 0.29 with RMSE = 29.78, 54.23, and 44.04 kg\(\cdot\)tree\({}^{-1}\) for 2014, 2016, and 2017, respectively) and linear trend line (R\({}^{2}\) = 0.56, 0.37, and 0.29 with RMSE = 13.9, 13.92, and 14.17 fruit size (g) per tree for 2014, 2016, and 2017, respectively) (Figure 4). Although the coefficients of determination were low, a consistent positive relationship between VIs and the measured yield parameter was observed over the three year study. Additionally, a stepwise regression and ANCOVA applied to the three years of data identified no significant difference between the slopes of average fruit size (g) per tree versus RENDVI1 (\(F\)-value = 0.24 and \(p\) = 0.78), but a significant difference in the intercepts (\(F\)-value = 5.03 and \(p\) = 0.0074). To address the influence of location, scatter plots of the optimal VI versus total fruit weight (kg\(\cdot\)tree\({}^{-1}\)) were derived for each block, for three years of data collection (Block 1 and 2) and two years (Blocks 3, 4, and 5) (Figure 5). In 2014, 2016, and 2017 the coefficients of determination for total fruit weight (kg\(\cdot\)tree\({}^{-1}\)) using NIR1 GNDVI were for Block 1 (R\({}^{2}\) = 0.78, 0.89, and 0.46 with RMSE = 13.67, 25.46, and 28.7 kg\(\cdot\)tree\({}^{-1}\) respectively) and RENDVI2 for block 2 (R\({}^{2}\) = 0.60, 0.61, and 0.26 with RMSE = 31.15, 40.81, and 40.35 kg\(\cdot\)tree\({}^{-1}\) respectively). In 2016, Block 1 produced the highest coefficient of determination (NIR1 GNDVI, R\({}^{2}\) = 0.89 with RMSE = 25.46 kg\(\cdot\)tree\({}^{-1}\)), followed by Block 5 (RENDVI1, R\({}^{2}\) = 0.72 with RMSE = 21.22 kg\(\cdot\)tree\({}^{-1}\)); Block 3 (CBSIPI, R\({}^{2}\) = 0.68 with RMSE = 48.31 kg\(\cdot\)tree\({}^{-1}\)); block 2 (RENDVI2, R\({}^{2}\) = 0.61 with RMSE = 40.81 kg\(\cdot\)tree\({}^{-1}\)); and Block 4 (SIPI, R\({}^{2}\) = 0.43 with RMSE = 39.72 kg\(\cdot\)tree\({}^{-1}\)). As well as the varying VIs, the models that produced the best fit also varied between exponential and quadratic. Figure 4: Scatter plot between the red edge band (RENDVI1) VI identified to have the highest coefficient of determination across all blocks for (**a**) total fruit weight (kg\(\cdot\)tree\({}^{-1}\)) and (**b**) average fruit size (g) per tree. The 2014 sample points are presented as a triangle (\(\Delta\)) (n = 18) with dotted best fit line, the 2016 as a diamond (\(\Diamond\)) (n = 18) with dashed best fit line, and the 2017 as a circle (o) (n = 108) with solid best fit line. For average fruit size (g), scatter plots of the optimal VIs were again derived for each separate block (Figure 6). Unlike total fruit weight (kg\(\cdot\)tree\({}^{-1}\)), strong coefficients of determination were achieved across all three seasons for Block 1 (RENDVI1: R\({}^{2}\) = 0.78 with RMSE = 13 g (2014), R\({}^{2}\) = 0.65 with RMSE = 24.8 g (2016) and R\({}^{2}\) = 0.41 with RMSE = 43.7 g (2017)) and Block 2 (RENDVI1: R\({}^{2}\) = 0.64 with RMSE = 18.78 g (2014), R\({}^{2}\) = 0.77 with RMSE = 15.83 g (2016) and R\({}^{2}\) = 0.68 with RMSE = 33.5 g (2017)). For the remaining blocks sampled during the 2016 and 2017 growing season, a respectable coefficient of determination were also identified: Block 3 (RENDVI2, R\({}^{2}\) = 0.45 with RMSE = 20.96 g); Block 5 (RENDVI2, R\({}^{2}\) = 0.41 with RMSE = 43.32 g) and Block 4 (SIPI, R\({}^{2}\) = 0.35 with RMSE 88.98 g).