Sensors for Summerfruit

Deploying Real-Time Sensors to Meet Summerfruit Export Requirements for China

Strategic Imperatives:

Produce the right thing
Leverage brand Australia
Improve access to finance
Build a digitally capable workforce

Project Meta:

Completed:
Duration:
2.5 Years
Technologies:
Bistatic LiDAR, Green Atlas Cartographer, Rubens Fluorescence Spectrometer, Machine Learning and Artificial Intelligence, IoT

In Partnership With:

Royal Melbourne Institute of Technology
Vic Government
Royal Melbourne Institute of Technology
Summerfruit Australia
Agriculture Victoria
Green Atlas
Rubens Technologies

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The Challenge:  

When Australian Summerfruit (peach, nectarine, plum, and apricot) arrive in the valuable Chinese export market, they must meet strict quality conditions or risk being downgraded, sold for a lower price, or rejected. Long-term, this has a significant impact on repeat purchase, price, and the reputation of Australian Summerfruit.  

To meet market preferences, Australian Summerfruit growers must be able to predict whether the fruit they produce will satisfy consumer preference for size, taste, colour and texture; meet export market access protocols for air and sea freight to China; and be able to survive short periods of sub-optimal storage.

This will require technology that can track and predict harvest timing, fruit quality attributes (sweetness, firmness, size, colour, grade and internal disorders) and yield.  

The Solution:  

State of the art sensing meets premium exports in this collaborative research project to help the Australian Summerfruit industry meet the demands of the valuable Chinese market.  

The project team will trial three main sensors, the Rubens™ fluorescence spectrometer, the Green Atlas Cartographer and a RMIT University’s Bistatic LiDAR. Once they have been calibrated and validated at Agriculture Victoria’s Tatura SmartFarm in the Goulburn Valley, the sensors will be road-testing in commercial orchards and packhouses across major growing regions in Victoria and interstate.  

The team will trial whether these sensors are capable of cost-effectively:  

  • Measuring and reporting the spatial distribution of tree foliage extent and density, and fruit number, maturity, size and colour that will inform stone fruit orchard management and lead to improved prediction of harvest timing and yield.
  • And predicting storage disorders in fruit and measuring fruit firmness, sweetness and maturity in the value chain that will assist in determining market destination and shelf life.

Identifying applicable sensors will enable the future development of predictive models for growers and packhouses to make earlier and smarter business decisions around harvest quality, timing, labour, market destination and promotion.

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