A Deep Learning-Based Approach for High-Throughput Hypocotyl Phenotyping. Issue 4 (21st October 2019)

• Record Type: Journal Article
• Authors: Dobos, Orsolya; Horvath, Peter; Nagy, Ferenc; Danka, Tivadar; Viczián, András
• Is Part Of: Plant physiology. Volume 181:Issue 4(2019)
• Page Start: 1415
• Page End: 1424
• Publication Date: 2019-10-21
• Subjects: Plant physiology -- Periodicals; Botany -- Periodicals; Periodicals; Electronic journals; 571.2
• DOI: 10.1104/pp.19.00728
• Languages: English
• ISSNs: 0032-0889

Abstract

Abstract : A deep learning-based algorithm provides an adaptable tool for determining hypocotyl or coleoptile length of different plant species. Abstract: Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has advanced from using rulers and millimeter papers to assessing digitized images but remains a labor-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 ( 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.