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

Mikolaj Cieslak1, Nazifa Khan2, Pascal Ferraro1, Raju Soolanayakanahally3, Stephen J. Robinson3, Isobel Parkin3, Ian McQuillan2, Przemyslaw Prusinkiewicz1
1 University of Calgary
2 University of Saskatchewan
3 Agriculture and Agri-Food Canada


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.


Mikolaj Cieslak, Nazifa Khan, Pascal Ferraro, Raju Soolanayakanahally, Stephen J. Robinson, Isobel Parkin, Ian McQuillan, Przemyslaw Prusinkiewicz. L-system models for image-based phenomics: case studies of maize and canola. in silico Plants, Volume 4, Issue 1, 2022, diab039, https://doi.org/10.1093/insilicoplants/diab039.

Download the paper from the publisher's site or here (PDF, 6.4 MB).

Download Movie 1 -- Calibration of a maize model here (MP4, 11.1 MB)
Download Movie 2 -- Calibration of a canola model here (MP4, 9.9 MB)

Download the maize model here (TGZ archive, 22.2 MB)
Download the canola model here (TGZ archive, 31.4 MB)

You will need VLAB to run the models. They were tested using VLAB verison 4.5.1-3582 on macOS 10.13 (High Sierra).

Watch related talks presented at the Seminarios del Insituto de Biologia (UNAM) here and Canola Innovation Day of Canola Week 2021 here.