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Related Experiment Video

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Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

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Published on: April 12, 2024

Classification of Fluorescently Labelled Maize Kernels Using Convolutional Neural Networks.

Zilong Wang¹, Ben Guan^1,2,3, Wenbo Tang¹

¹School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

A new machine vision system accurately classifies fluorescent maize kernels in real-time using a YOLOv5s convolutional neural network (CNN). This technology enhances maize breeding by enabling precise identification of fluorescently labelled seeds.

Keywords:

deep learning fluorescent protein gene machine vision maize kernel sorting system

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Last Updated: Aug 7, 2025

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

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Published on: April 12, 2024

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Published on: September 15, 2023

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

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Published on: September 22, 2023

Area of Science:

Agricultural Science
Biotechnology
Computer Vision

Background:

Accurate real-time classification of fluorescently labelled maize kernels is crucial for advanced breeding techniques in agriculture.
Existing methods may lack the precision or speed required for industrial applications.

Purpose of the Study:

To design a real-time classification device and recognition algorithm for fluorescently labelled maize kernels.
To develop a high-precision machine vision system for optimal detection.

Main Methods:

A machine vision (MV) system was designed with a fluorescent protein excitation light source and a specific filter.
A YOLOv5s convolutional neural network (CNN) was developed and improved for high-precision identification.
The performance of the improved YOLOv5s model was compared against other YOLO models for kernel sorting.

Main Results:

Optimal detection was achieved using a yellow LED light source and an industrial camera filter with a 645 nm central wavelength.
The improved YOLOv5s algorithm increased the recognition accuracy of fluorescent maize kernels to 96%.
The study demonstrated the effectiveness of the developed system for real-time classification.

Conclusions:

The study provides a feasible technical solution for high-precision, real-time classification of fluorescent maize kernels.
The developed machine vision system and algorithm have universal technical value for identifying various fluorescently labelled plant seeds.
This advancement supports efficient and accurate seed classification in plant breeding programs.