RT Journal Article
SR Electronic
T1 A deep learning-based approach for high-throughput hypocotyl phenotyping
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 651729
DO 10.1101/651729
A1 Orsolya Dobos
A1 Peter Horvath
A1 Ferenc Nagy
A1 Tivadar Danka
A1 András Viczián
YR 2019
UL http://biorxiv.org/content/early/2019/05/27/651729.abstract
AB Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has been developed from using rulers and millimeter papers to the assessment of digitized images, yet it remained a labour-intensive, monotonous and time consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low quality images, produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets, not restricted to Arabidopsis thaliana. Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at https://github.com/biomag-lab/hypocotyl-UNet, but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user.One-sentence summary A deep learning-based algorithm, providing an adaptable tool for determining hypocotyl or coleoptile length of different plant species.