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Maize Kernel Batch Counting System Based on YOLOv8-ByteTrack.

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  • 1School of Engineering, Anhui Agricultural University, Hefei 230036, China.

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Summary
This summary is machine-generated.

This study introduces a real-time maize kernel counting system using deep learning. The novel Convolutional Neural Network (CNN) approach achieves 99% accuracy, overcoming challenges in automated food processing.

Keywords:
ByteTrackYOLOv8deep learningseed count

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Area of Science:

  • Food Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Maize kernel count is vital for crop assessment and yield prediction.
  • Traditional counting methods struggle with high-speed motion, occlusion, and target ID switching.

Purpose of the Study:

  • To develop a real-time kernel falling counting system for maize.
  • To enhance accuracy and robustness in automated food processing environments.

Main Methods:

  • Implemented a Convolutional Neural Network (CNN) based system.
  • Integrated YOLOv8 object detection with ByteTrack multi-object tracking.
  • Utilized high-speed cameras for dynamic video stream capture.

Main Results:

  • Achieved a tracking and counting accuracy of up to 99%.
  • Effectively overcame counting errors from high-speed motion and object occlusion.
  • Demonstrated enhanced system robustness in complex conditions.

Conclusions:

  • The developed system offers intelligent and precise kernel counting.
  • Provides reliable technical support for automated quality monitoring and yield estimation.
  • Shows significant application value for food processing production lines.