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Related Experiment Video

Updated: Aug 23, 2025

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions
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Deep-learning-based automatic evaluation of rice seed germination rate.

Jinfeng Zhao1, Yan Ma1, Kaicheng Yong2

  • 1College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.

Journal of the Science of Food and Agriculture
|November 6, 2022
PubMed
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A new convolutional neural network, YOLO-r, accurately detects rice seed germination status and rate. This automated method overcomes challenges posed by small, densely packed seeds, offering real-time analysis.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Genetics and Breeding

Background:

  • Rice is a globally significant food crop and a model organism for research.
  • Seed germination rate is a critical performance indicator for rice.
  • Automated detection of rice seed germination faces challenges due to seed size and density.

Purpose of the Study:

  • To develop an automated system for detecting rice seed germination status and rate.
  • To improve the accuracy and efficiency of rice seed germination detection using image processing.

Main Methods:

  • Development of a convolutional neural network named YOLO-r.
  • Incorporation of image partition, Transformer encoder, small target detection layer, and CDIoU loss into YOLO-r.
  • Collection and analysis of 21,429 rice seeds with diverse phenotypic characteristics.
Keywords:
YOLOgermination rateimage partitioningneural network

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Main Results:

  • YOLO-r achieved a mean average precision of 0.9539, outperforming other models.
  • The system demonstrated robust detection in complex conditions (water stains, impurities, etc.).
  • Average detection time per image was 0.011 seconds, meeting real-time requirements.
  • Mean absolute error for predicted germination rate was within 0.1.

Conclusions:

  • YOLO-r provides a fast, easy, and accurate method for predicting rice germination rate.
  • The developed model is effective for automated rice seed germination analysis.