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

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External defect detection of Orah mandarin based on a non-brightness correction algorithm.

Panfei Li^1,2, Xiaoxiao Jiang^1,3, Yuhao Wu^1,2

¹Guangxi Key Lab of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China.

Frontiers in Plant Science

|September 29, 2025

Summary

This summary is machine-generated.

This study introduces a fast and accurate non-brightness correction algorithm for Orah mandarin external defect detection. The novel method achieves high recognition rates, improving citrus grading efficiency.

Keywords:

defect detection histogram statistics morphological operations non-brightness correction sliding window

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Area of Science:

Agricultural Engineering
Computer Vision
Image Processing

Background:

Orah mandarin grading requires external defect detection.
Existing methods struggle with uneven brightness and high computational costs.
Traditional brightness correction algorithms are slow and inaccurate.

Purpose of the Study:

To develop a non-brightness correction algorithm for faster and more accurate Orah mandarin defect detection.
To overcome limitations of existing defect detection methods.
To enhance citrus grading efficiency.

Main Methods:

Image processing with a sliding window (100x100 pixels) for sequential threshold segmentation.
Histogram analysis to categorize image regions.
Adaptive thresholding, region merging, and exclusion of fruit stem areas using circularity and hue features.
Morphological operations for noise elimination and final defect segmentation.

Main Results:

Achieved rapid defect detection at 85.3 ms per fruit.
Reached a high defect recognition rate of 97.5%.
Demonstrated effectiveness for point-like defects (thrips scarring, canker spots) and moderate effectiveness for blocky defects (sunburn).

Conclusions:

The proposed algorithm offers a novel, efficient, and accurate approach for Orah mandarin external defect detection.
It significantly improves upon traditional methods by eliminating brightness correction steps.
Further refinement may be needed for complex or overlapping defects.