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Predicting Green Bean Harvest and Yield

Using predictive models to predict green bean harvest, yield and quality to better align supply and demand.

In Partnership With:

Mulgowie Fresh Pty Ltd
Queensland University of Technology (QUT)
QLD Government

Predicting Green Bean Harvest and Yield

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.

Planning the supply of fresh produce to align with demand and market expectations is complicated, and with variable data can be costly in term of profitability. Variations in forecast yield can result in supply shortages or excess.

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.

An accurate predictive growth model that can predict harvest timing, yield and quality will enhance production and sales throughout the entire business, ensuring customer satisfaction.

The Solution

This research project will use data science and real-world testing to create accurate predictive models that will help the green bean industry to meet market demand.

The project team will create a dedicated green bean modelling tool for Mulgowie Farming Company by identifying and building on existing crop planting, yield, and location data. The model will be tested in the ‘real’ world to ensure accuracy and reliability and to further refine predictive capacity.

A user interface tool will be created for use by Mulgowie’s production and sales staff. The model and minimum viable product (MVP) user interface prediction tool is designed to allow easy adoption and uptake throughout the business.

The predictive model will help Mulgowie Farming Company and the green bean industry to make better production and sales decisions based on harvest timing, yield, and quality, while supporting marketing, promotion and pricing.

Outcomes

  • Support green bean sales and producers in predictive forecasting.
  • Help producers and the green bean industry to ensure profitability and productivity based on quality data.
  • Green bean modelling tool that is accurate and reliable in forecasting yield.
  • Fresh produce sales and production teams will be able to make informed decisions from standardised and quality data.
  • Improved decision-making based on robust data.
  • The value to the green bean industry could be between $2.5 - $5 million per year by 2021.

meet the team

Majella Nolan

Innovation Manager, Food Agility CRC

David Carey

Senior Horticulturist, Department of Agriculture and Fisheries Queensland

Associate Professor Paul Corry

Associate Professor in Operations Research, Science and Engineering Faculty, School of Mathematical Sciences, Queensland University of Technology

Amanda Woods

Project Lead, Mulgowie Farming Company

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