# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 5 Commercial Systems for Fruit Number, Size and External Quality  
Commercial adoption occurs when a technology achieves a level of effectiveness, robustness, ease of use and a cost point that is attractive to users. For example, a number of providers have commercialized methods for the estimation of grain crop yields, utilizing remote sensing inputs. Barchart (Chicago, IL, USA) ([[https://www.barchart.com/cmdty/markets/agriculture](https://www.barchart.com/cmdty/markets/agriculture)]([https://www.barchart.com/cmdty/markets/agriculture](https://www.barchart.com/cmdty/markets/agriculture))) (accessed on 13 July 2021) produces yield forecasts every day from June to the end of harvest based on vegetation indices derived from satellite imagery and recent weather data of each growing area. This private provider forecast extends the traditional yield forecasts for the major grain crops of wheat, corn and soybean based on the USDA [107] objective yield surveys. The objective yield surveys involve more than 100'scouts' that sample crops on set survey routes across the main production regions at several stages during each crop. For tree fruit crops, several of the yield forecasting technologies mentioned in the above review have advanced to the point where commercial adoption is nascent. Examples include:  
Pronofrut (San Fernando, Chile) ([[https://pronofrut.cl/en](https://pronofrut.cl/en)]([https://pronofrut.cl/en](https://pronofrut.cl/en))) (accessed on 6 May 2021) provides a yield estimation service using multistage systematic designs and manual sampling for bud, flower and fruit counts and sizing. Estimation errors of 10% or less at the orchard scale are claimed for estimates of fruit, nuts, berries, vegetables, seeds, and leaf crops. The sampling is directed using a dedicated smartphone app. For tree fruit yield estimation, the selection of trees for counting based on ancillary variables that are correlated with fruit loading and/or quality is also claimed, using software for the detection of tree canopies and extraction of canopy vegetation indices and characteristics, such as crown area from high-resolution UAV images ([PERSON] et al. [108]). Several machine vision-based systems for tree crop load have been developed. Intelligent Fruit Vision (Long Bennington, UK) ([[http://www.intelligentfruitvision.com/](http://www.intelligentfruitvision.com/)]([http://www.intelligentfruitvision.com/](http://www.intelligentfruitvision.com/))) (accessed on 1 March 2021) offers a vehicle-mounted multi view machine vision system for apple fruit count and sizing. Geolocation data are recorded, enabling the production of fruit density maps. Fruitometry (Bay of Plenty, New Zealand) ([[https://fruitometry.com/](https://fruitometry.com/)]([https://fruitometry.com/](https://fruitometry.com/))) (accessed on 1 March 2021) provides a vehicle-mounted machine vision system for the detection of kiwifruit buds, flowers, fruit and canopy characteristics. A minimum imaging rate of 3 hectare/hour is claimed. Green Atlas (Sydney, Australia) ([[https://greenatlas.com.au/](https://greenatlas.com.au/)]([https://greenatlas.com.au/](https://greenatlas.com.au/))) (accessed on 1 March 2021) offers a farm vehicle-mounted machine vision service for the detection and mapping of fruits and flowers for apples, almonds, cherries, kiwifruit and wine grapes. The system also utilizes LiDAR to provide additional measurements of tree attributes (canopy area, tree height, etc.). An imaging rate of 8 hectares an hour is claimed. Adroit Robotics (Sao Paulo, Brazil) ([[https://adroitrobotics.com](https://adroitrobotics.com)]([https://adroitrobotics.com](https://adroitrobotics.com))) (accessed on 1 March 2021) offers an RGB camera system that can be mounted to a farm tractor. The system has been used in citrus orchards for fruit counting, sizing and maturity evaluation based on color, and detection of pests and diseases on leaves.