grasviq

grasviq quantifies and analyzes vein patterns in grass (monocot) leaves from images, providing measurements such as vein density, vein width, interveinal distance, and vein order classification for phenotyping and genetic studies.

Key Features:

• Monocot-focused analysis: Supports parallel venation typical of grasses including Oryza sativa and Zea mays.
• Image input: Operates on images of cleared leaf pieces for vein visualization.
• Segmentation: Uses classical computer vision techniques to segment vein patterns.
• Edge detection and thresholding: Applies thresholding and edge detection methods to delineate veins.
• Vein order classification: Automatically classifies veins into different orders.
• Spatial measurements: Calculates vein width, interveinal distance, and vein density.
• Validation datasets: Demonstrated on maize inbred lines and auxin biosynthesis and transport mutants.
• High-throughput phenotyping: Enables automated, high-throughput quantification with precision comparable to manual methods.

Scientific Applications:

• Grass leaf phenotyping: Quantitative assessment of vein architecture in grasses for trait analysis.
• Genetic experiments and mutant screens: Identification of quantitative differences and vein patterning defects in mutants, including auxin-related lines.
• Plant physiology and agriculture: Support for studies linking vein traits to plant performance, productivity, and ecosystem roles.
• Vein phenomics: Generation of standardized vein trait measurements for comparative and high-throughput studies.

Methodology:

Computational steps explicitly include segmentation of cleared leaf images using classical computer vision, application of thresholding and edge detection to delineate veins, automatic classification of vein orders, and calculation of spatial parameters such as vein width, interveinal distance, and vein density.