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Updated: Jan 17, 2026

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Published on: November 17, 2016

Automated seed counting using image processing and deep learning.

Qiuyu Zu^1,2, Teng Liu², Wenpeng Zhu²

¹School of Agriculture, Jilin Agricultural Science and Technology College, Jilin City, China.

Frontiers in Plant Science

|September 15, 2025

Summary

This summary is machine-generated.

Automated seed counting using mobile app technology offers a faster, more efficient alternative to manual methods in agriculture. Image processing and deep learning approaches provide practical solutions for crop research and breeding, reducing labor and time.

Keywords:

YOLOv5 deep learning image processing mobile app seed counting

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Area of Science:

Agricultural Science
Computer Vision
Biotechnology

Background:

Manual seed counting is crucial for agricultural research but is inefficient and prone to errors.
Micro-sized seeds present particular challenges for traditional counting methods.

Purpose of the Study:

To develop and evaluate automated computer vision methods for seed counting.
To integrate these methods into a user-friendly mobile application.

Main Methods:

Developed two automated approaches: image processing (IP) and deep learning (DL).
Integrated IP and DL methods into a mobile application for seed counting.

Main Results:

The IP method achieved high accuracy and time savings but requires controlled lighting.
The DL method offered rapid processing (0.33s/image) but had inconsistent accuracy with complex seed clusters.
Both methods significantly improved efficiency over manual counting.

Conclusions:

Automated seed counting via mobile app enhances efficiency for laboratory and field applications.
These technologies streamline seed counting for crop breeding, production, and research, reducing manual effort.