# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 1 Introduction
### Types of Models  
An empirical model (also referred to as a descriptive model) achieves a forecast through a correlation between measurable variables and the attribute of interest, without regard to the mechanism of the relationship. Empirical models describe the data for the conditions under which the data were collected, but may 'break' when used in predictions of future populations, especially when seasonal conditions differ significantly to those for the populations used in development of the model. A mechanistic model (also known as an explanatory model) is based on physical, chemical, or biological laws describing the behavior of constituting parts. Mechanistic models are useful in developing an understanding of a system, but predictive power is limited by the understanding of the system and the complexity of the model.  
In a 1998 review of forward estimation of biomass production and yield of horticultural crops, [PERSON] et al. [4] noted that attempts to produce mechanistic models of production were generally based on modeling photosynthetic capacity through inputs of leaf area development, light interception, photosynthesis and respiration. In subsequent decades, further attempts have been made to develop mechanistic models of tree fruit production, with notable progress made, e.g., in the QualiTree model for peach trees [5]. These models typically predict fruit growth through the consideration of the tree as a set of subunits competing for resources such as light, photo-assimilates and water. The QualiTree model is probably the most advanced tree fruit mechanistic model, although attempts for other tree crops are advancing, e.g., for mango [6; 7].  
However, the influence of previous growing cycles increases the complexity of tree fruit systems, as seen in irregular bearing between seasons [8]. This complexity hampers the development of a mechanistic model to estimate production and has prevented the practical use of such models for the forecast of fruit load [9]. This review does not further consider the use of fully mechanistic models in tree fruit forecast, but rather focuses on  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Crop** & **\# of Records** & **\%** & **Country** & **\# of Records** & **\%** \\ \hline Apple & 32 & 33 & China & 38 & 21 \\ Mango & 19 & 20 & United States & 34 & 19 \\ Citrus & 17 & 18 & Spain & 29 & 16 \\ Other & 16 & 16 & Australia & 20 & 11 \\ Kiwi & 4 & 4 & India & 16 & 9 \\ Grape & 3 & 3 & Germany & 14 & 8 \\ Olive & 3 & 3 & Brazil & 11 & 6 \\ Tomato & 3 & 3 & France & 11 & 6 \\  & & & Italy & 9 & 5 \\ \hline Method & \# of records & \% & Estimation level & \# of records & \% \\ \hline MV RGB & 65 & 59 & image & 36 & 38 \\ Satellite & 10 & 9 & tree & 28 & 30 \\ MV & 8 & 7 & orchard & 19 & 20 \\ Hyperspectral & 8 & 7 & region & 11 & 12 \\ Proximal indirect & 7 & 6 & & & \\ UAV & 6 & 5 & & & \\ Historical/climate & 6 & 5 & & & \\ Manual & 6 & 5 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature base as indexed by Scopus records (doa 8/2/2021) for the keywords ‘fruit AND violation’ for the years 2010–2021. # represents number.  
the use of empirical models based on direct count, either manual or machine vision, or on correlation to environmental conditions or canopy attributes.  
Tree fruit producers currently commonly rely on empirical models for the prediction of harvest dates [4]. These models rely on the estimation of thermal time since flowering or a measure of a fruit attribute that is related to maturity.