Hypocotyl

Hypocotyl measures hypocotyl length from digitized seedling images using deep learning to provide quantitative plant phenotyping.

Key Features:

• Deep Learning Pipeline: Employs convolutional neural networks (CNNs) trained on digitized seedling images to identify and quantify the hypocotyl region, including low-quality images from flatbed scanners or smartphone cameras.
• High Throughput Capability: Automates hypocotyl length measurement to enable processing of large image datasets for high-throughput analysis.
• Adaptability: Demonstrated on Arabidopsis thaliana and adaptable to diverse plant species and datasets.
• Accuracy: Produces hypocotyl length estimates with accuracy comparable to human performance.

Scientific Applications:

• Genetic Studies: Provides quantitative hypocotyl measurements for analysis of genetic effects on seedling growth traits.
• Plant Breeding: Supports high-throughput phenotyping in breeding programs to assess seedling growth characteristics.
• Developmental Biology: Enables measurement of seedling growth for investigations of developmental processes.
• High-Throughput Screening: Facilitates large-scale screens for gene function, stress responses, and growth pattern analysis.

Methodology:

Trains a deep learning model on digitized seedling images and uses convolutional neural networks (CNNs) to identify and quantify the hypocotyl region and output hypocotyl length measurements.