Predicting Green Bean Harvest and Yield

“The dedicated Grena-Bena prediction tool supports Mulgowie to consistently deliver quality fresh green beans. The tools developed within this project will support our farm, agronomy and wider supply chain team members to make good decisions, quickly and effectively, based on the best available data.

‍Shane Quinn, Mulgowie Farming Company

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⬇️Download project explainer (.pdf)

The Challenge

Suppliers of fresh produce need to make fast, significant, and complex decisions.

There are many complexities in supply and production planning in fresh produce systems with many variables to consider for accurate forecasting of yield, including changing weather patterns.

Australian green bean growers need to know when beans can be harvested and what is the likely yield. Managing green bean supply continuity is also important for enhancing brand reputation, value, and growth potential.

The Solution

The project team created a dedicated green bean modelling tool called 'Grena-Bena' for Mulgowie Farming Company by identifying and building on existing crop planting, yield, and location data. The tool was then commercially tested and further refined throughout the 12-month hyper-care period.

The new online Grena-Bena harvest date and yield prediction tool allows instant updating of every crop planting and location. Weather variability that impacts potential yield and harvest date is immediately visible to the agronomy crop management team. This has enhanced communication and oversight throughout the entire production line allowing staff to minimise any supply disruption impacts and maximise sales.

Outcomes

• The research used data science and real-world testing to create an accurate predictive model, the Grena-Bena tool which is supporting one of Australia’s largest vegetable producers, Mulgowie Farming Company by improving green bean supply forecasting.
• Improved knowledge and supply forecasting drives efficiency gains from planting all the way through to the retail shelf.
• The infrastructure, methods and processes developed in this project provide a valuable platform and example for ongoing improvement and development of underlying models and algorithms in the crop growing and broader agricultural sector.

Project Updates

November 2023 - Food Agility Excellence Awards: Graduate of Merit, Dr Jing (Roy) Yang

January 2023 - Final Report: Green Bean Harvest and Yield

November 2020 - Announcement: Predicting green bean yield using the power of predictive models