Mapping and quantifying unique branching structures in lentil (Lens culinaris Medik.)

Area of Science:

• Agricultural Science
• Plant Biology
• Computational Biology

Background:

• Lentil (Lens culinaris Medik.) is a vital global crop, with expansion efforts into new regions like Western Australia and New South Wales.
• Plant architecture is crucial for yield stability, especially in newly cultivated areas.
• High-throughput phenomics offers advanced tools for studying plant development, architecture, and genetics.

Purpose of the Study:

• To develop and validate a novel method for mapping and quantifying individual branch structures in lentil plants.
• To utilize image-based phenomics for detailed analysis of lentil plant architecture.
• To enable improved understanding of lentil growth and trait genetics for enhanced crop adaptation.

Main Methods:

• A LemnaTec Scanalyser 3D platform captured side-view RGB images of glasshouse-grown lentil plants.
• A Python-based algorithm analyzed morphological skeletons to quantify branch number, angle, and length.
• The algorithm was integrated into an open-source image analysis pipeline (PlantCV).

Main Results:

• The algorithm accurately quantified branching in immature lentil plants, with accuracy decreasing in mature plants due to occlusion.
• Branch counts achieved 77.9% absolute accuracy at 22 days after sowing (DAS), improving to 97.6% with a ±1 branch tolerance.
• Branch length and angle metrics (splay, angle-difference) were significantly influenced by genotype, DAS, and salt treatment, demonstrating utility in distinguishing between lentil lines under stress.

Conclusions:

• The developed methodology accurately quantifies key individual branch parameters in lentil plants.
• This image analysis approach can be applied to assess plant architecture in other dicotyledonous species.
• The findings support the use of high-throughput phenomics for lentil breeding and adaptation studies.