Synthetic plants used to train models

There were many great articles concerning cross-disciplinary research at the interface between plant biology, mathematics and computer science before the launch in silico Plants (isP). We are excited for isP to be home to these types of articles in the future.

Plant models need high quality data for calibration and validation. Machine learning techniques are expected to take a prominent role in providing high quality image-based phenotyping data in the future. Yet machine learning typically requires large and diverse datasets to learn generalizable models and available datasets are often small and the costs associated with generating new data are high. Ubbens and coauthors address this problem using data from synthetic plants.

Synthetic rosettes generated by the L-system and real rosettes from the public dataset.
Synthetic rosettes (left) generated by the L-system and real rosettes (right) from the public dataset. Image: Ubbens et al. 2018.

The authors demonstrate that machine learning models can be augmented using training data derived from rendered images of synthetic plants. Combining real with synthetic plant images as training data reduced mean absolute count error compared to using only images of real plants. Moreover, models completely trained only on synthetic rosettes were successfully applied to count leaves in real rosettes.

Rendered images of Arabidopsis rosettes were computer-generated from a descriptive model using L-systems that reproduced early developmental stages of the plant shoot based on direct observations and measurements.

The machine learning model used in this study was a platform for image-based plant phenotyping called Deep Plant Phenomics, which implements deep convolutional neural networks for of plant phenotyping, to count leaves (Ubbens and Stavness, 2017).

With the advancements made in this study, the next application could be modeling of entire plots of crops. β€œA simulated plot of plants could potentially make it possible to train algorithms for detecting biologically meaningful traits such as flowering time or response to stress with a reduced number of real (annotated) crop images.”

Rachel Shekar

Rachel (she/her) is a Founding and Managing Editor of in silico Plants. She has a Master’s Degree in Plant Biology from the University of Illinois. She has over 15 years of academic journal editorial experience, including the founding of GCB Bioenergy and the management of Global Change Biology. Rachel has overseen the social media development that has been a major part of promotion of both journals.

Read this in your language

The Week in Botany

On Monday mornings we send out a newsletter of the links that have been catching the attention of our readers on Twitter and beyond. You can sign up to receive it below.

@BotanyOne on Mastodon

Loading Mastodon feed...