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A lightweight intelligent grading method for lychee anthracnose based on improved YOLOv12.

Bing Xu¹, Zejie Ma¹, Xueping Su¹

¹School of Computer, Guangdong University of Petrochemical Technology, Maoming, Guangdong, China.

Frontiers in Plant Science

|January 7, 2026

Summary

This study introduces LycheeGuard-Lite, a lightweight AI model for detecting lychee anthracnose disease. It achieves high accuracy with reduced computational costs, enabling efficient, non-destructive fruit quality grading.

Keywords:

YOLOv12 attention mechanism disease classification lightweight model lychee anthracnose

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Area of Science:

Agricultural Science
Computer Vision
Artificial Intelligence

Background:

Anthracnose significantly degrades lychee quality, necessitating efficient detection methods.
Manual grading is inefficient and subjective, hindering effective quality management.
Developing accurate yet lightweight AI models for disease detection is crucial for automated sorting.

Purpose of the Study:

To develop a lightweight AI model for rapid, non-destructive detection and grading of lychee anthracnose.
To improve upon existing detection frameworks by balancing high accuracy with reduced computational complexity.
To provide a deployable solution for automated post-harvest lychee quality assessment.

Main Methods:

Proposed LycheeGuard-Lite, a lightweight model based on the YOLOv12 framework.
Incorporated C3k2_Light modules with depthwise separable convolutions and a C2PSA attention mechanism.
Utilized a weighted convolution strategy (wConv2D) to enhance feature extraction and reduce complexity.
Evaluated the model on a dataset of 14,576 images across two lychee varieties and three severity levels.

Main Results:

Achieved 99.4% mAP50 detection accuracy for lychee anthracnose.
Reduced model parameters by 12.8% (2.19M total).
Decreased computational cost by 29.3% (4.1 GFLOPs).

Conclusions:

LycheeGuard-Lite offers a highly accurate and computationally efficient solution for automated lychee disease recognition.
The lightweight design makes the model suitable for practical deployment in post-harvest sorting systems.
This research provides a valuable tool for improving lychee quality management and reducing food waste.