SeedGerm

SeedGerm automates high-throughput imaging and machine-learning phenotypic analysis to quantify seed germination timing and rates for crop research.

Key Features:

• Automated Imaging System: Employs cost-effective hardware to capture high-resolution images and process multiple image series simultaneously.
• Machine-Learning-Based Analysis: Uses open-source machine learning algorithms to extract phenotypic traits related to seed germination and establishment.
• Germination Curve Generation: Produces germination curves based on actual seed-level timing and rates rather than fitted models.
• Output Formats: Exports results as comma-separated values (CSV) and processed images (PNG).
• Species Versatility: Validated across tomato, pepper, Brassica, barley, and maize.
• Radicle Emergence Scoring Accuracy: Scores radicle emergence with performance matching human specialists.

Scientific Applications:

• Large-scale seed phenotyping: Enables high-throughput quantification of germination traits for experimental and testing workflows.
• Genetic research on ABA signaling: Supports identification of germination-related traits and has implicated a gene involved in abscisic acid (ABA) signaling within seeds.
• Seed technology and breeding: Applicable to routine seed technology applications and to inform crop breeding and development decisions.

Methodology:

Automated image acquisition is followed by image-based analysis using open-source machine learning algorithms to score radicle emergence, derive seed-level timing, generate germination curves, and export results in CSV and PNG formats.