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PollenDetect: An Open-Source Pollen Viability Status Recognition System Based on Deep Learning Neural Networks.

Zhihao Tan1, Jing Yang1,2, Qingyuan Li3

  • 1National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.

International Journal of Molecular Sciences
|November 11, 2022
PubMed
Summary

An automated tool, PollenDetect, rapidly and accurately assesses plant pollen viability using AI. This innovation aids in breeding stress-resistant crops by speeding up viability testing and identifying key genetic traits.

Keywords:
computer visiondeep learninghigh temperature stressopen-sourcepollen viability

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Area of Science:

  • Plant reproductive biology
  • Agricultural technology
  • Bioinformatics

Background:

  • Pollen viability is crucial for plant reproduction and crop yield.
  • Manual pollen viability assessment is time-consuming and limits high-throughput screening.
  • Plant stresses can negatively impact pollen viability, affecting crop production.

Purpose of the Study:

  • To develop an automated tool for rapid and accurate pollen viability detection.
  • To overcome the limitations of manual pollen viability assessment.
  • To aid in the genetic breeding of stress-resistant crop varieties.

Main Methods:

  • Development of PollenDetect, an automated detection tool utilizing the YOLOv5 neural network.
  • Adjustment of the YOLOv5 model for efficient small target detection.
  • Validation of PollenDetect's accuracy and speed against manual methods and traditional quantification.

Main Results:

  • PollenDetect significantly reduces detection time from approximately 3 minutes to 1 second per image.
  • The tool achieves high detection accuracy, demonstrated by 99% accuracy in cotton pollen viability testing.
  • Results indicate high temperatures reduce cotton pollen viability, consistent with traditional quantification methods.

Conclusions:

  • PollenDetect provides a rapid and accurate system for assessing pollen viability.
  • The open-source software can be adapted for various pollen types, supporting diverse research.
  • This tool is valuable for screening stress-resistant crops and identifying relevant genes in breeding programs.