Precision summerfruit orchards (SF23000)

What is it all about?

The project is developing profitable management strategies for summerfruit growers associated with temporal and spatial variation in crop load, fruit quality (fruit number per tree, fruit size and colour), and labour use efficiency. 

Through trials at the Tatura SmartFarm, this project will demonstrate the value of precision crop information and data-driven spatial ‘zonal’ crop load management (chemical, mechanical and hand thinning) geared to tree size to provide fruit growers with objective data for decision-making. Scans of commercial orchard blocks will provide ‘real world’ examples of spatial data on crop load, fruit size, fruit colour, yield and tree vigour and size.

The project will expand on the crop load management principles from the previous summerfruit levy fund project Experimental summerfruit orchard – phase II (SF17006), incorporate economic analysis into crop load management practices and initiate new research on establishing block-specific relationships between fruiting level, tree size and fruit size.

The researchers will evaluate strategies to thin fruit and flowers so that growers can better target optimal crop loads and stabilise high levels of marketable yield. Management options for thinning (e.g., manual, chemical thinners, Darwin flower thinner) and colour development (pruning) will be a focus, including spatial zonal management and the associated financial benefits and costs.

The following activities will take place at the experimental stone fruit orchards at the Tatura SmartFarm and in commercial orchard blocks:

• Compare the accuracy, precision, reliability and utility of commercial precision agriculture sensing platforms and control systems (e.g., Cartographer, drone, smartphone) that measure and map fruit attributes and tree canopy size.
• Determine the efficacy and efficiency of matching variable rate thinning technologies (spray and mechanical) with spatially sensed data and compare this to current management practice.
• Develop an automated system to determine block-specific crop load, fruit size and tree size relationships from spatially sensed data.
• Assess spatial (winter and summer) pruning strategies to maximise fruit quality.
• Develop guidelines to interpret and act on spatial maps of tree, flower and fruit parameters.
• Test the accuracy of pre-harvest estimates of fruit packout yield using Cartographer and drone platforms.
• Demonstrate the application of precision crop load management for the Summerfruit industry.
• Document the economic benefits and costs of collecting and using crop sensor data in summerfruit orchards.

The project will increase profitability, efficiency and sustainability through innovative research and development and a targeted communication program for the summerfruit industry. Specifically, the project will enhance domestic and international market access, increase potential exports, provide sustainable production practices that optimise orchard systems and labour efficiencies, increase crop uniformity by adopting variable thinning (chemical, mechanical and hand) and pruning, accelerate the adoption of sensor technology for increased orchard efficiency and sustainability, provide a clean-and-green recognition (i.e., resource use efficiency) by importers and consumers, enhance food security and improve networks and cross-industry collaboration.