# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Correlative Methods
#### 2.5.2 Prediction of Yield in Cropping  
A number of empirical yield models have been developed based on within-season remote sensing-derived vegetation indices (VIs), particularly for broadacre annual crops. These models anticipate a relationship between yield and canopy characteristics, such as leaf area index (LAI), biomass, and chlorophyll content, which are indexed by VIs. Commonly used VIs which have been related to canopy structure include the Normalized Differential Vegetation Index (NDVI), a simple ratio, the Enhanced Vegetation Index (EVI) and the Optimized Soil-Adjusted Vegetation Index (OSAVI). These VIs are calculated from reflectance in the red (R) and near-infrared (NIR) bands, e.g., NDVI = (R - NIR)/(R + NDVI). Other VIs have been used to index chlorophyll content, such as the red edge chlorophyll index [67].  
The results for the use of spectra reflectance information to estimate annual crop yield provide an incentive to assess the technique with tree fruit crops, given the scalability of the technique. Examples of results with annual crops follow: cotton yield was estimated using the canopy reflective indices of NDVI and EVI at flowering [68]. Wheat yield was predicted (R\({}^{2}\) = 0.91 and RMSE = 0.54 t/ha) of a set of fields independent to that used in training using OSAVI, CI and'stress index' from Sentinel 2 imagery by [PERSON] et al. [67]. Soybean and corn yield was predicted (R\({}^{2}\) = 0.92 and 0.88, respectively) using neural network and Multiple Linear Regression (MLR) models with a combination of Landsat and SPOT image data from early to mid-season crop growth stages [69].  
Other work has related crop yield to environmental and management parameters. A recent review reported 567 relevant studies on the use of machine learning for crop yield prediction, with reports on annual crops predominating [1]. The features most commonly used as inputs for a yield model were temperature, solar radiation, humidity, rainfall, nutrient level and soil type, and the most commonly used modeling method was an Artificial Neural Network (ANN). Of studies employing deep learning, the CNN was the most used algorithm. For example, with inputs of soil type, varieties, planting spacing, cane field age, average number of cuts, rainfall and temperature and yield of sugarcane was predicted using several data mining techniques (random forest, boosting and support vector machines) based on [70], with the best model returning a prediction RMSE that was 20.0 t ha\({}^{-1}\), for yields around 70 t ha\({}^{-1}\), i.e., 29% RRMSE.  
A recent focus is the combination of climate and satellite data to predict crop yield. For example, the prediction using a neural network model of wheat yield across Australia over 14 years was improved by the combination of climate data and the EVI from MODIS (R\({}^{2}\) \(\sim\) 0.75) for prediction two months before crop maturity ([PERSON], 2017). It was suggested that time series satellite data allows for the tracking of crop growth, capturing the variability of yield through the growing season, with the contribution to yield prediction saturating at the time of maximum vegetative growth. In contrast, climate data provided added value across the whole season.