TY - JOUR
T1 - MyROOT 2.0
T2 - An automatic tool for high throughput and accurate primary root length measurement
AU - González, Alejandro
AU - Sevillano, Xavier
AU - Betegón-Putze, Isabel
AU - Blasco-Escámez, David
AU - Ferrer, Marc
AU - Caño-Delgado, Ana I.
N1 - Funding Information:
A.I.C-D. is a recipient of a BIO2016-78955 grants from the Spanish Ministry of Economy and Competitiveness and a European Research Council, ERC Consolidator Grant (ERC-2015-CoG - 683163). I.B-P. is funded by the FPU15/02822 grant from the Spanish Ministry of Education, Culture and Sport. D.B-E. is contracted with the BIO2016-78955 grant in the A.I.C-D laboratory. CRAG is funded by “Severo Ochoa Programme” from Centers of Excellence in R&D 2016–2019 (SEV-2015-485 0533).
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/1
Y1 - 2020/1
N2 - The automatic measurement of external physical traits (i.e. phenotyping) of plant organs, such as root length –which is highly correlated with plant viability– is one of the current bottlenecks in academic and agricultural research. Although many root length measurement software tools are available to the community, plant scientists often find their usability is limited, the measurements they provide are not accurate enough or they are too limited to specific image characteristics. In response to that, this work describes MyROOT 2.0, an automatic software tool jointly developed by plant scientists and computer vision engineers to create a high throughput root length measurement tool that reduces user intervention to a minimum. Using Arabidopsis thaliana seedlings grown on agar plates as a case study, MyROOT 2.0 is capable of detecting the root regions of interest in a fully automatic manner with an accuracy of 98%. Furthermore, this work also presents previously unreported experiments to evaluate several constituting modules of MyROOT 2.0, such as the ability to determine image scale automatically with subpixel accuracy, or the influence of training the hypocotyl detector using wildtype or mutant samples. Finally, when compared to state-of-the-art root length measurement software tools, MyROOT 2.0 achieves the highest root detection rate, obtaining measurements which are four times more accurate than its competitors. This makes MyROOT 2.0 an attractive tool for high throughput root phenotyping.
AB - The automatic measurement of external physical traits (i.e. phenotyping) of plant organs, such as root length –which is highly correlated with plant viability– is one of the current bottlenecks in academic and agricultural research. Although many root length measurement software tools are available to the community, plant scientists often find their usability is limited, the measurements they provide are not accurate enough or they are too limited to specific image characteristics. In response to that, this work describes MyROOT 2.0, an automatic software tool jointly developed by plant scientists and computer vision engineers to create a high throughput root length measurement tool that reduces user intervention to a minimum. Using Arabidopsis thaliana seedlings grown on agar plates as a case study, MyROOT 2.0 is capable of detecting the root regions of interest in a fully automatic manner with an accuracy of 98%. Furthermore, this work also presents previously unreported experiments to evaluate several constituting modules of MyROOT 2.0, such as the ability to determine image scale automatically with subpixel accuracy, or the influence of training the hypocotyl detector using wildtype or mutant samples. Finally, when compared to state-of-the-art root length measurement software tools, MyROOT 2.0 achieves the highest root detection rate, obtaining measurements which are four times more accurate than its competitors. This makes MyROOT 2.0 an attractive tool for high throughput root phenotyping.
KW - High throughput Root phenotyping
KW - Imaging tools
KW - Plant phenotyping
KW - Plant science
KW - Root length measurement
UR - http://www.scopus.com/inward/record.url?scp=85076056531&partnerID=8YFLogxK
U2 - 10.1016/j.compag.2019.105125
DO - 10.1016/j.compag.2019.105125
M3 - Article
AN - SCOPUS:85076056531
SN - 0168-1699
VL - 168
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 105125
ER -