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A simple method for measuring pollen germination rate using machine learning.

Akira Yamazaki1, Ao Takezawa2, Kyoka Nagasaka2

  • 1Faculty of Agriculture, Kindai University, Nara, 631-8505, Japan. yamazaki@nara.kindai.ac.jp.

Plant Reproduction
|June 6, 2023
PubMed
Summary

This study developed an AI model to accurately measure plant pollen germination rates, aiding in understanding plant reproduction and identifying related genes across various species.

Keywords:
Abiotic stressChili pepperMicroscopeObject detectionYolov5

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Area of Science:

  • Plant Biology
  • Computational Biology
  • Genetics

Background:

  • Pollen germination is crucial for plant reproduction, but its measurement is labor-intensive.
  • Abiotic stresses, like high temperatures, negatively impact pollen germination and plant reproductive success.
  • Accurate assessment of pollen viability is essential for plant breeding and agricultural research.

Purpose of the Study:

  • To develop an automated method for measuring pollen germination rates using machine learning.
  • To create a model capable of distinguishing between germinated and non-germinated pollen.
  • To enable high-throughput analysis of pollen germination for genetic studies.

Main Methods:

  • Utilized the Yolov5 machine learning package for transfer learning.
  • Trained a model on chili pepper (Capsicum annuum) pollen images, comparing 640px and 320px image resolutions.
  • Validated the model's accuracy in estimating pollen germination rates and detecting associated genetic regions.

Main Results:

  • A Yolov5-based model accurately detected germinated and non-germinated pollen, with 640px images yielding higher accuracy.
  • The model successfully estimated the pollen germination rate in a previously studied F2 population of C. chinense.
  • Associated gene regions identified through genome-wide association studies were re-detected using the model's predicted trait.
  • The model demonstrated similar accuracy for pollen from rose, tomato, radish, and strawberry, indicating broad applicability.

Conclusions:

  • An AI-driven approach significantly improves the efficiency and accuracy of pollen germination rate measurement.
  • This model facilitates genetic analyses to identify genes controlling pollen germination across diverse plant species.
  • The findings have implications for plant breeding programs aiming to enhance reproductive success under stress conditions.