SGM-DETR: Semantic-Guided and Feature-Refined Transformer for Pine Wilt Disease Detection in Satellite Imagery

Chen, Xixin; Wu, Zidi; Wu, Zhuangci; Tan, Xiaobo; Xue, Yongfei; Luo, Yuanhan; Wang, Peng; Huang, Wenjing; He, Jianhua; Zhang, Jie; Yi, Jizheng

doi:10.3390/plants15131959

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Open AccessArticle

SGM-DETR: Semantic-Guided and Feature-Refined Transformer for Pine Wilt Disease Detection in Satellite Imagery

by

Xixin Chen

,

Zidi Wu

,

Zhuangci Wu

,

Xiaobo Tan

,

Yongfei Xue

^*,

Yuanhan Luo

^*,

Peng Wang

,

Wenjing Huang

,

Jianhua He

,

Jie Zhang

and

Jizheng Yi

College of Computer and Mathematics, Central South University of Forestry and Technology, Changsha 410004, China

^*

Authors to whom correspondence should be addressed.

Plants 2026, 15(13), 1959; https://doi.org/10.3390/plants15131959 (registering DOI)

Submission received: 22 April 2026 / Revised: 11 June 2026 / Accepted: 17 June 2026 / Published: 25 June 2026

(This article belongs to the Special Issue Advances in Artificial Intelligence for Plant Research—2nd Edition)

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Abstract

Pine wilt disease (PWD) can spread rapidly after the disease occurs and often causes large-scale death of the pine. Therefore, the timely identification of infected trees is critical for forest conservation and effective disease management. However, early infected trees are difficult to distinguish in satellite remote sensing images. Their visual differences from healthy trees and complex background features are often subtle, and existing image-processing methods do not fully exploit heterogeneous information. To address this problem, we constructed the Naro dataset for satellite-based PWD detection and proposed SGM-RTDETR based on Real-Time Detection Transformer (RT-DETR). The proposed model consists of a Semantic–Visual Fusion Module (SVFM) and a Disease Feature Refinement Module (DFRM). In SVFM, ExG, VARI, and GLI are concatenated with RGB imagery to form a six-channel visual input, which enhances the spectral differences between diseased and non-diseased targets. In addition, textual prior knowledge is introduced into the decoder input through a Stackelberg game-based visual–text fusion strategy. This strategy helps the encoded memory features maintain clearer disease-related semantics in complex backgrounds. DFRM then performs channel recalibration, feature refinement, and residual enhancement on the fused memory features to better extract fine-grained disease cues in remote sensing scenes. Experiments on the Naro dataset show that SGM-RTDETR achieves 80.75% mAP@0.5 and 35.43% mAP@0.5:0.95, which is 2.74 percentage points higher than RT-DETR-L on mAP@0.5:0.95. Overall, the results indicate that the dual-module structure improves the precision and robustness of PWD detection in satellite remote sensing images.

Keywords: pine wilt disease; satellite remote sensing; semantic–visual fusion; vegetation index; RT-DETR; object detection; Stackelberg game optimization

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MDPI and ACS Style

Chen, X.; Wu, Z.; Wu, Z.; Tan, X.; Xue, Y.; Luo, Y.; Wang, P.; Huang, W.; He, J.; Zhang, J.; et al. SGM-DETR: Semantic-Guided and Feature-Refined Transformer for Pine Wilt Disease Detection in Satellite Imagery. Plants 2026, 15, 1959. https://doi.org/10.3390/plants15131959

AMA Style

Chen X, Wu Z, Wu Z, Tan X, Xue Y, Luo Y, Wang P, Huang W, He J, Zhang J, et al. SGM-DETR: Semantic-Guided and Feature-Refined Transformer for Pine Wilt Disease Detection in Satellite Imagery. Plants. 2026; 15(13):1959. https://doi.org/10.3390/plants15131959

Chicago/Turabian Style

Chen, Xixin, Zidi Wu, Zhuangci Wu, Xiaobo Tan, Yongfei Xue, Yuanhan Luo, Peng Wang, Wenjing Huang, Jianhua He, Jie Zhang, and et al. 2026. "SGM-DETR: Semantic-Guided and Feature-Refined Transformer for Pine Wilt Disease Detection in Satellite Imagery" Plants 15, no. 13: 1959. https://doi.org/10.3390/plants15131959

APA Style

Chen, X., Wu, Z., Wu, Z., Tan, X., Xue, Y., Luo, Y., Wang, P., Huang, W., He, J., Zhang, J., & Yi, J. (2026). SGM-DETR: Semantic-Guided and Feature-Refined Transformer for Pine Wilt Disease Detection in Satellite Imagery. Plants, 15(13), 1959. https://doi.org/10.3390/plants15131959

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SGM-DETR: Semantic-Guided and Feature-Refined Transformer for Pine Wilt Disease Detection in Satellite Imagery

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