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A transformer and large-kernel convolution-based detection model for Red Turpentine Beetle infestation in pine trees.

Manxin Chao^1,2,3, Can Peng^1,2, Junfeng Peng^2,4

¹The School of Information, Yunnan Normal University, Kunming, 650500, Yunnan, China.

Scientific Reports

|July 1, 2026

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This summary is machine-generated.

A new method, TLK-YOLO, effectively detects Red Turpentine Beetle infestations in pine forests using drone imagery. This advanced algorithm improves detection accuracy and efficiency for crucial forest health monitoring.

Area of Science:

Forestry Science
Computer Vision
Ecological Monitoring

Background:

Red Turpentine Beetle infestations threaten pine forest ecological stability.
Traditional manual inspection methods are inefficient and subjective.
UAV-based detection offers improved efficiency but faces challenges with small targets and complex imagery.

Purpose of the Study:

To develop an efficient and accurate UAV-based method for detecting Red Turpentine Beetle infestations in pine forests.
To address the challenges of small object detection, blurred details, and complex backgrounds in UAV imagery.

Main Methods:

Proposed TLK-YOLO, integrating Transformer mechanisms and large-kernel selection strategies.
Introduced Local Window Cross-Attention (LWCA) for enhanced small-object detail modeling.

Keywords:

Large-kernel convolution Red Turpentine Beetle Transformer UAV YOLO

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Implemented a Dynamic Combined Large Selective Kernel (DCLSK) module for adaptive receptive field adjustment.

Redesigned network architecture (TLK-NA) to optimize feature propagation and reduce redundancy.

Main Results:

TLK-YOLO achieved a remarkable mAP50 of 89.3% in detecting Red Turpentine Beetle infestations.
Demonstrated significant improvements over baseline models: precision increased by 4.4%, recall by 9.6%.
mAP50 and mAP50-95 improved by 7.9% and 8.3% respectively, with low computational overhead.

Conclusions:

TLK-YOLO provides an efficient and reliable technical foundation for UAV-based forestry pest and disease monitoring.
The method effectively overcomes challenges in detecting small, detailed targets in complex UAV imagery.
Offers a significant advancement in automated forest health assessment and pest management strategies.