hypocotyl-UNet

hypocotyl-UNet automates measurement of seedling hypocotyl length from digitized images to enable high-throughput plant phenotyping.

Key Features:

• Automation and Efficiency: Automates hypocotyl detection and length measurement to increase throughput in phenotyping studies.
• Adaptability to Low-Quality Images: Operates on low-quality images acquired with flatbed scanners or smartphone cameras.
• Versatility Across Species: Applicable to Arabidopsis thaliana and adaptable to a diverse range of other plant species.
• High Accuracy: Produces measurements with accuracy reported to be comparable to human performance.
• Customizability: Supports training on custom datasets tailored to specific experimental and imaging setups.

Scientific Applications:

• High-throughput genetic screens: Enables rapid phenotypic measurement of hypocotyl length across large mutant or genotype collections.
• Environmental response assessment: Quantifies hypocotyl growth responses to environmental treatments or conditions.
• Developmental biology studies: Facilitates measurement of seedling developmental traits involving hypocotyl elongation.
• Population-level trait screening: Supports screening of large plant populations for trait selection or phenotypic variation.

Methodology:

Processes digitized seedling images using a deep learning pipeline that leverages convolutional neural networks (CNNs) to extract features and measure hypocotyl dimensions.