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Related Experiment Video

Updated: Nov 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Defect Classification of Green Plums Based on Deep Learning.

Haiyan Zhou¹, Zilong Zhuang¹, Ying Liu¹

¹College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)

|December 10, 2020

Summary

This study developed a deep learning model for green plum defect detection, achieving 93.8% accuracy. This automated system improves quality control for green plums, enhancing their economic value.

Keywords:

classification deep learning defects green plum

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Area of Science:

Agricultural Science
Computer Vision
Artificial Intelligence

Background:

Green plums possess high nutritional and medicinal value but are prone to defects affecting quality and economic worth.
Manual defect detection in green plums is labor-intensive, costly, and lacks accuracy, hindering production efficiency.
Automating green plum defect classification is crucial for improving product value and production intelligence.

Purpose of the Study:

To develop an automated system for efficient and accurate classification of green plum defects using deep learning.
To enhance the economic value and marketability of green plum products through intelligent quality control.
To establish a robust green plum defect detection network model.

Main Methods:

Collected 1240 RGB images of green plums under controlled lighting conditions.
Developed a five-category classification standard: rot, spot, scar, crack, and normal.
Utilized a modified VGG convolutional neural network with stochastic weight averaging (SWA) optimizer and w-softmax loss function for defect detection.

Main Results:

Achieved an average green plum defect recognition accuracy of 93.8%.
The model demonstrated a rapid test time of 84.69 ms per image.
High recognition rates were observed for decay defects (99.25%) and normal green plums (95.65%).

Conclusions:

The developed deep learning model significantly outperforms traditional methods and other network architectures in green plum defect classification.
The system offers a cost-effective and intelligent solution for automated quality assessment in the green plum industry.
This advancement contributes to improving the overall quality and economic value of green plum products.