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A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model.

Xiu'yan Zhao¹, Yu'xiang He², Hong'tao Zhang²

¹College of Information Science and Engineering, Shandong Agricultural University, Taian, China.

Scientific Reports

|February 20, 2024

Summary

This study introduces a new deep learning (DL) method for grading fresh tea leaves, improving accuracy and reducing damage. The enhanced YOLOv8x model accurately classifies tea quality using image recognition.

Keywords:

CBAM Fresh tea leaves Grade discrimination Improve YOLOv8x Target detection

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Area of Science:

Agricultural Engineering
Computer Vision
Artificial Intelligence

Background:

Current mechanical grading of fresh tea leaves suffers from high damage rates and low accuracy.
Existing machine vision and machine learning (ML) algorithms offer limited precision in tea leaf grading.

Purpose of the Study:

To develop an innovative approach for accurately classifying the quality grade of fresh tea leaves.
To enhance tea leaf grading precision by integrating image recognition and deep learning (DL).

Main Methods:

Acquisition of images for both scattered and stacked fresh tea leaves.
Application of data augmentation techniques (rotation, flipping, contrast adjustment) to create datasets.
Enhancement of the YOLOv8x model with Space Pyramid Pooling Improvements (SPPCSPC) and Concentration-based Attention Module (CBAM), creating the YOLOv8x-SPPCSPC-CBAM model.

Main Results:

The YOLOv8x-SPPCSPC-CBAM model outperformed Faster R-CNN, YOLOv5x, and YOLOv8x in tea leaf quality classification.
Achieved high performance metrics: mean average precision of 98.2% for scattered leaves and 99.1% for stacked leaves.
Demonstrated high precision (95.8% scattered, 99.1% stacked) and recall (96.7% scattered, 97.7% stacked) rates.

Conclusions:

The developed YOLOv8x-SPPCSPC-CBAM model provides a robust and accurate framework for fresh tea leaf quality classification.
This deep learning approach significantly improves upon existing methods for tea leaf grading, addressing accuracy and damage concerns.