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.
- computer vision
- image analysis
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Plant Science