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Related Experiment Video

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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TrichomeYOLO: A Neural Network for Automatic Maize Trichome Counting.

Jie Xu^1,2, Jia Yao³, Hang Zhai^1,2

¹Maize Research Institute, Sichuan Agricultural University, Wenjiang 611130, Sichuan, China.

Plant Phenomics (Washington, D.C.)

|March 17, 2023

Summary

Researchers developed TrichomeYOLO, an automated deep learning method for counting and measuring maize trichomes from images. This tool significantly improves efficiency and accuracy in plant phenotyping, aiding genetic research.

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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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Published on: March 28, 2025

Area of Science:

Plant biology
Computational biology
Agricultural science

Background:

Plant trichomes are crucial epidermal structures involved in plant development and stress responses.
Manual phenotyping of trichomes is laborious and hinders genetic research, particularly gene cloning.
Existing automated methods for identifying maize trichomes are lacking.

Purpose of the Study:

To introduce TrichomeYOLO, an automated deep convolutional neural network for identifying maize trichomes.
To enable accurate counting and measurement of trichome density and length from scanning electron microscopy images.
To facilitate efficient trichome identification for maize research.

Main Methods:

Development of TrichomeYOLO, a deep convolutional neural network model.
Application of the model to scanning electron microscopy images of maize leaves.
Comparison of TrichomeYOLO's performance against five mainstream object detection models.

Main Results:

TrichomeYOLO achieved 92.1% identification accuracy on maize leaf micrographs.
Outperformed Faster R-CNN, YOLOv3, YOLOv5, DETR, and Cascade R-CNN in accuracy.
Successfully applied to investigate trichome variations in a natural maize population.

Conclusions:

TrichomeYOLO provides a robust and efficient automated method for maize trichome identification.
The developed method significantly accelerates research progress in plant phenotyping and gene cloning.
The method and pretrained model are open-access, promoting further research on maize trichomes.