Abstract
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.
Footnotes
Funding Information O.D., A.V.and F.N. were supported by grants from the Economic Development and Innovation Operative Program (GINOP-2.3.2-15-2016-00001 and GINOP-2.3.2-15-2016-00015).
T.D. and P.H. acknowledge support from the HAS-LENDULET-BIOMAG and from the European Union and the European Regional Development Fund
GINOP-2.3.2-15-2016-00026.