# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 7 Conclusions  
Long supply chains require careful management, and management needs data--for tree fruit crops, this includes data on expected fruit numbers, size, quality and timing. The translation of the remarkable technical advances occurring in many fields to horticultural applications is occurring. Sometimes, however, there is a danger of 'playing with new toys', and it is important for researchers not to lose track of the agronomic goal in exploring the uses of the new technologies.  
For fruit number estimation, the rigorous application of a statistical sampling design can greatly improve the efficiency and effectiveness of estimates based on manual counts of a sample of trees from a given block. Machine vision can provide fruit counts of whole orchards, with the caveat that canopy architecture must allow for the fruit to be visible from the interrow. The data can also be used to provide yield maps. The indirect correlation of fruit load to remotely sensed vegetation indices can also provide tree crop load estimates and maps, but there is a requirement for manual counts for the calibration of the method at a farm and season level. Yield models can also be based on time series data of past yields, climatic variables and vegetation spectral indices. Further implementation studies are required for all methods.  
Machine vision techniques also offer promise for the in-orchard estimation of fruit size, quality and flowering level. Again, with development tools now available, the task is to undertake implementation activities for each commodity.  
For harvest timing, the estimation of flowering extent and timing using machine vision offers the promise of a quantitative assessment of peak flowering time, providing input to harvest maturity estimates. The development of IoT technologies is providing scope for a finer spatial resolution on temperature monitoring, allowing for the improved implementation of heat unit estimation of time of maturity. Proximal optical sensors are also allowing for the non-invasive assessment of attributes related to fruit maturity, augmenting the thermal time estimation. As reviewed by [PERSON] et al. [124], there are many sensors available that can plug into such a network to fine tune estimates of harvest timing and quality, from soil and plant water status sensors to dendrometers, and a developing capacity to use deep learning approaches in the use of these data.  
Optimal orchard design is a complex interplay of sociological, economic, political and technological factors. This interplay results in the need for local optimizations. General trends are, however, evident in the adoption of systems that reduce labor requirements while increasing production and/or facilitate mechanical management, e.g., smaller trees, whether by dwarfing or training. The forecast technologies discussed in this review represent one element in this puzzle. The choice of technology will also require local optimization on the basis of commodity, production condition, e.g., protected cropping or open field, and market need, e.g., export or local farmers' market.  
In short, this is an exciting period to be involved in the development and application of tools for the forecast of tree fruit load and harvest timing.  
Conceptualization, N.T.A., K.B.W. and D.W.; investigation, N.T.A., K.B.W. and D.W.; resources, N.T.A., K.B.W. and D.W; writing--original draft preparation, N.T.A., K.B.W. and D.W.; writing--review and editing, N.T.A., K.B.W. and D.W.; visualization, N.T.A., K.B.W. and D.W.; supervision, K.B.W. All authors have read and agreed to the published version of the manuscript.  
K.W. and N.A. received funding support from the Australian Federal Department of Agriculture and Water through Horticulture Innovation Australia (project ST19009, Multiscale monitoring tools for managing Australian tree crops).  
Not applicable.