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Ginger Ninja – Fusarium predictor multi-expert training data collection

This project aims to increase the seed ginger training dataset for improving the performance of the AI-based Fusarium Predictor prototype developed in the Ginger Ninja pilot project. The pilot study was trained using one expert seed ginger grader. It is proposed to replicate the Ginger Ninja AI-based Fusarium Predictor prototype and distribute them to a series of farms to allow experienced ginger graders to scan and classify seed ginger during the seed cutting season. This data will be collected over four-weeks during seed ginger cutting and then merged with the initial pilot project data to retrain the AI-based classifier and assess this multiple “expert” trained performance to the original system.

Project date

27 Jul 2022-12 Mar 2023
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Principal investigator

Matthew Dunbabin

Project funded by

Multiple industries
Alternative protein Aquaculture Cereal grains Cross industry Essential oils Fruits Game Honey bees Nuts Other rural industries Pasture, fodder & feed Poultry Pulse grains

AgriFutures Australia

AgriFutures Australia proudly focuses on building a rich future for Australian agriculture. We live and work in the regions and …
  • Location


  • Organisation type

    Research funding body


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  • Project date

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  • Research organisation

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