Leaf Angle eXtractor: A high-throughput image processing framework for leaf angle measurements in maize and sorghum

Area of Science:

• Plant science
• Agronomy
• Computational biology

Background:

• Maize yields have increased due to breeding and higher planting densities.
• Upright plant architecture improves light interception but requires efficient phenotyping for leaf angle.
• Conventional leaf angle phenotyping is slow and labor-intensive, limiting mechanistic studies.

Purpose of the Study:

• To develop a high-throughput method for quantifying leaf angle dynamics in maize and sorghum.
• To analyze plant responses to water deprivation using automated image analysis.
• To enable large-scale studies on plant adaptation to water limitations.

Main Methods:

• Acquired high-throughput time-series image data of maize and sorghum under drought.
• Developed a MATLAB-based image processing framework named Leaf Angle eXtractor (LAX).
• Quantified leaf angles from images to assess temporal changes under water stress.

Main Results:

• Observed differential leaf angle responses to drought in maize and sorghum.
• LAX enabled distinguishing wilting leaves from non-wilting leaves within minutes.
• High-throughput phenotyping revealed temporal variations in leaf angle under water deprivation.

Conclusions:

• Automated leaf angle measurement with LAX facilitates large-scale experiments.
• LAX aids in understanding spatial and temporal variations in plant responses to water stress.
• This technology can be used to improve drought tolerance in crops.