# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Correlative Methods
#### 2.5.5 Correlation to Spectral Indices  
Another focus is the use of within-season vegetation indices extracted from remote imaging. There are many indices that can be trialed, utilizing visible, far red, or thermal bands. For example, a 2007 study reported on the relationship between canopy temperature estimated from airborne imagery (2 m resolution) minus air temperature and stem water potential, and a correlation of olive yield (kg/tree) to canopy temperature (R\({}^{2}\) = 0.84 and 0.77 for images captured at 09:30 and 11:30 h in 2004) [75]. Olive and peach fruit weight (g) were also correlated to canopy temperature (R\({}^{2}\) = 0.91 and 0.92 in 2004 and 2005, respectively, for olive, and R\({}^{2}\) = 0.82 and 0.81 in 2004 and 2005 seasons, respectively). [PERSON] et al. [76] reported the development of a mechanistic orientated model of regional olive yield using data for ten seasons (2000-2009) across 10 regions of Tuscany. The NDVI from the MODIS satellite and meteorological data were used as inputs to a parametric model (C-Fix) for the prediction of daily tree Gross Primary Production (GPP). The GPP estimates were joined to respiration estimates from a bio-geochemical model based on climatic variables and descriptors of local conditions to estimate Net Primary Production (NPP). The NPP accumulated over the fruiting periods of each season was then related to fruit yield, with comparison to provincial statistics. The methodology was reported to partly capture yield variability at a province level, but more accurately forecast inter-year yield variation over the entire region. The C-Fix model achieved an R\({}^{2}\)cv = 0.83 and an RMSEcv = 299 kg/ha for the ten regions across the ten seasons, while the C-Fix + respiration model achieved an R\({}^{2}\)cv = 0.89 and an RMSEcv = 224 kg/ha (cross validation based on use of yearly datasets), on an average yield of 1600 kg/ha, and thus an MAE of 14%. High-resolution satellite imagery (WorldView (WV)-2 and WV-3; \(\sim\)46 and 30 cm panchromatic resolution, respectively, used in segmentation of canopies, and 1.24 and 1.8 m multispectral resolution, respectively, used in reflectance index calculation) was utilized for the prediction of avocado and mango yield [77; 78]. For each orchard block to be assessed, an NDVI classification of trees was undertaken, with six trees selected from each of low, medium and high NDVI classifications. Manual measurements of fruit count and/or weight for these trees (n = 18) were used in correlation to 18 different VIs. The best model was then used for the prediction of yield (kg/tree) of the whole block. In the avocado study, the best calibration correlations across five blocks and three seasons were obtained with the index RENDVI1, calculated as (NIR1-RE)/(NIR1+RE), where NIR1 is the 774-874 nm band and RE is the red edge band 698-749 nm (R\({}^{2}\) = 0.45, 0.28 and 0.29; n = 90 trees). Better results were obtained by using models developed for individual blocks, using the VI with the highest correlation to yield in each case (R\({}^{2}\) = 0.21 to 0.89). Yield predictions of orchard blocks using the correlations developed on 18 trees were within 0.1 to 3.4 t ha\({}^{-1}\) of actual harvest (packhouse data), with average totals around 8 t/ha. In the mango study, [PERSON] et al. [78] utilized ANN models based on WV-3 image-derived tree crown area and all spectral bands. For a model across all orchards and seasons, the highest correlation (R\({}^{2}\) = 0.30) to fruit count was achieved with the NDVI red-edge band. As for the avocado work, superior results were obtained with use of local (orchard) models compared to use of a generic model. For individual orchard models, the best result of 18 VIs gave R\({}^{2}\) of between 0.56 to 0.63. Yield estimations within 1 to 7% of harvested yield for each orchard were reported. [PERSON] et al. [79] examined the correlation of NDVI calculated from Landsat 8 satellite imagery obtained at multiple times of the year to the yield of 181 juibube orchards across two seasons (2016-2017).