TY - JOUR
T1 - Leaf Angle eXtractor
T2 - A high-throughput image processing framework for leaf angle measurements in maize and sorghum
AU - Kenchanmane Raju, Sunil K.
AU - Adkins, Miles
AU - Enersen, Alex
AU - Santana de Carvalho, Daniel
AU - Studer, Anthony J.
AU - Ganapathysubramanian, Baskar
AU - Schnable, Patrick S.
AU - Schnable, James C.
N1 - Funding Information:
This study was supported by a Science without Borders scholarship (214038/2014‐9) to D.S.C., by the USDA National Institute of Food and Agriculture (award 2016‐67013‐24613) to J.C.S., and by the National Science Foundation (grant no. OIA‐1557417).
Publisher Copyright:
© 2020 Kenchanmane Raju et al. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America
PY - 2020/8/1
Y1 - 2020/8/1
N2 - Premise: Maize yields have significantly increased over the past half-century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment. Methods: High-throughput time series image data from water-deprived maize (Zea mays subsp. mays) and sorghum (Sorghum bicolor) were obtained using battery-powered time-lapse cameras. A MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions. Results: Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period. Discussion: Automating leaf angle measurements using LAX makes it feasible to perform large-scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations.
AB - Premise: Maize yields have significantly increased over the past half-century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment. Methods: High-throughput time series image data from water-deprived maize (Zea mays subsp. mays) and sorghum (Sorghum bicolor) were obtained using battery-powered time-lapse cameras. A MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions. Results: Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period. Discussion: Automating leaf angle measurements using LAX makes it feasible to perform large-scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations.
KW - computer vision
KW - drought
KW - image analysis
KW - maize
KW - phenotyping
UR - http://www.scopus.com/inward/record.url?scp=85090007221&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090007221&partnerID=8YFLogxK
U2 - 10.1002/aps3.11385
DO - 10.1002/aps3.11385
M3 - Article
C2 - 32999772
AN - SCOPUS:85090007221
SN - 2168-0450
VL - 8
JO - Applications in Plant Sciences
JF - Applications in Plant Sciences
IS - 8
M1 - e11385
ER -