Leaf-rolling in maize crops: from leaf scoring to canopy-level measurements for phenotyping

Area of Science:

• Plant Physiology
• Agricultural Science
• Remote Sensing

Background:

• Leaf rolling in maize is a key indicator of water stress, crucial for crop phenotyping.
• Current visual scoring methods lack the high-throughput capabilities required for efficient breeding programs.
• Investigating remote sensing techniques to quantify leaf rolling and its impact on canopy structure.

Purpose of the Study:

• To explore the correlation between visual leaf-rolling scores and canopy structure changes detectable by remote sensing.
• To develop a high-throughput method for assessing water stress in maize through canopy analysis.
• To evaluate the repeatability of different leaf-rolling metrics derived from canopy structure data.

Main Methods:

• Conducted experiments on maize genotypes under water stress in 2015 and 2016.
• Visually scored leaf rolling and captured digital hemispherical photographs to assess canopy structure.
• Calculated the fraction of intercepted diffuse photosynthetically active radiation (FIPARdif) and developed a canopy-level index of rolling (CLIR).

Main Results:

• Leaf rolling initiated around 09:00 h and peaked at 15:00 h under water stress, unlike well-watered controls.
• A strong correlation (r²=0.86) was found between leaf-level rolling scores and canopy structure changes measured by CLIR.
• The amplitude of CLIR variation demonstrated higher repeatability (ρ=0.62) across years compared to rate and timing metrics.

Conclusions:

• Remote sensing techniques, particularly canopy structure analysis, can effectively quantify diurnal leaf-rolling patterns in maize.
• The proposed CLIR offers a repeatable and high-throughput method for assessing water stress responses in maize breeding.
• Findings support the development of drone-based systems for rapid, large-scale phenotyping of water stress in crops.