Enhancing Rice Provenance and Quality Prediction 

‍Sunrice will use its central position in the Australian rice industry to deliver a three-stage quality and prediction program for their national network of growers.  

The Challenge:  

Australia’s leading rice processor and global retailer, Sunrice, has a problem: during processing, rice grains can crack, reducing the amount and quality of product available to sell. Sunrice hopes that by collecting data on the cracking problem and sharing the findings with its network of Australian growers, it can help growers choose rice varieties that are less prone to breakage. The stakes for paving over the ‘cracks’ in the rice supply chain are high: even the smallest change could be worth millions of dollars to local growers.  

The Solution:  

This project aims to reduce the amount of rice cracked during processing by delivering a digital ‘Product Quality and Prediction’ program in three stages:  

1. Rice Data Trust (RDT) to record the provenance, quality, genetics, grain moisture levels at harvest, harvest procedures, and growing conditions of each delivery.  

2. Grower Benchmarking Program (GBP) to provide growers with real-time feedback about grain quality, empower the community of growers to learn from each other, and update guides for growing different varieties.  

3. Grain Quality Prediction Models (GQPM) developed by Charles Sturt University, which will predict whole grain yields and expected quality, helping growers plan their harvest seasons and expected returns.  

Impact:  

• Demonstrating how shared data can add value to the Australia rice growing community 
• Empowering rice growers with an industry benchmark, real-time quality feedback, and shared information about different varieties, environmental conditions, and farming practices  
• Enabling SunRice to accurately grade and segregate rice at the delivery point  

Get involved:  

Please contact us at projects@foodagility.com or Project Lead Russel Ford rford@sunrice.com.au  

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