L-system models for image-based phenomics: case studies of maize and canola

Abstract Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Real...

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Published in:in silico plants 2022-01, Vol.4 (1)
Main Authors: Cieslak, Mikolaj, Khan, Nazifa, Ferraro, Pascal, Soolanayakanahally, Raju, Robinson, Stephen J, Parkin, Isobel, McQuillan, Ian, Prusinkiewicz, Przemyslaw
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creator Cieslak, Mikolaj
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description Abstract Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Realistic plant models, which can be annotated automatically, thus present an attractive alternative to real plant images for training purposes. Here we show how such models can be constructed and calibrated quickly, using maize and canola as case studies.
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title L-system models for image-based phenomics: case studies of maize and canola
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