DESCRIPTION
This technology is a cross-platform, open-source software tool that leverages deep learning and machine learning algorithms to perform quantitative, objective, and reproducible assessment of freeze damage in plant tissues using both RGB and hyperspectral imagery. This tool addresses the critical need for standardization and automation in freeze damage scoring plant phenotyping, especially for crops such as strawberry and camelina.
It is one of the few tools that supports both RGB and hyperspectral imagery for assessing freeze damage. Enabling flexible deployment across imaging platforms. Also integrates a suite of cutting-edge semantic segmentation models (e.g., UperDenseNet, UperResNet, UperConvNeXt), allowing users to select models based on accuracy or computational efficiency. Additionally, it uses AI algorithms with a graphical interface that is accessible to researchers without programming expertise.
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 Freeze Damage Quantification Computer Vision Software
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Benefits
PhytoScore provides precise, consistent, and automated assessment of freeze damage in crops, delivering faster and objective results that improve breeding and crop management decisions.
Patents
This technology has a Patent pending and is available for licensing/partnering opportunities.
