Semi-automated high content analysis of pollen performance using tubetracker

Area of Science:

• Plant reproductive biology
• Agricultural science
• Bioimaging analysis

Background:

• Pollen function is vital for plant reproduction and crop yields.
• Quantitative analysis of pollen performance is needed to improve reproductive resilience.
• Existing methods for pollen analysis can be labor-intensive and require specialized equipment.

Purpose of the Study:

• To introduce TubeTracker, a novel method for partially automating pollen tube growth analysis.
• To quantify key pollen performance parameters including germination time, tube tip velocity, and tube integrity.
• To provide an accessible tool for researchers studying plant reproduction.

Main Methods:

• TubeTracker integrates manual and automatic image processing routines.
• A graphical interface allows user interaction for manual correction of automated steps.
• The method does not require training data sets, enabling immediate implementation with standard imaging systems.

Main Results:

• TubeTracker accurately measures pollen tube germination and pollen tube tip elongation.
• The software was validated across multiple tomato cultivars.
• It provides essential data for advancing AI-based pollen analysis methods.

Conclusions:

• TubeTracker offers a practical and accessible method for analyzing pollen tube growth.
• The tool enhances the ability to quantitatively assess pollen performance.
• It supports efforts to improve plant reproductive resilience and crop productivity.