Synergistic spatial-sequential modeling for enhanced Rosa roxburghii disease detection
- 1Engineering Research Center of Micro-Nano and Intelligent Manufacturing, Ministry of Education, Kaili University, Kaili, Guizhou, China
- 2College of Microelectronics and Artificial Intelligence, Kaili University, Kaili, Guizhou, China
Abstract
The rapid advancement of smart agricultural technologies has enabled deep learning–based approaches to markedly enhance the efficiency of crop disease detection and quality monitoring. To overcome the challenges of low detection efficiency and high false-positive rates in large-scale cultivation of the characteristic economic crop Rosa roxburghii, this study introduces a synergistic spatial–sequential modeling framework. This synergy is achieved via a gated fusion mechanism that bidirectionally enhances CNN-extracted local spatial features and Mamba-derived global sequential features through adaptive re-weighting, forming a mutual refinement loop instead of a simple cascade. Built upon YOLOv8, the model integrates the Mamba module from state space models (SSMs) to develop a hybrid feature extraction framework, jointly optimizing detection accuracy and inference efficiency. A selective feature enhancement strategy further strengthens the network’s ability to characterize subtle lesions on the surface of Rosa roxburghii by adaptively amplifying informative responses through channel-wise recalibration and spatial refinement. The backbone network also incorporates the selective scan for 2D data (SS2D) module to capture long-range dependencies beyond conventional convolution. Experimental validation conducted on our self-constructed dataset, which consists of field-captured images of Rosa roxburghii plants manually collected and annotated by the research team, demonstrates that the proposed model delivers notable performance gains, achieving mAP@50 improvements of 6%, 2.87%, 8.38%, 7.08%, 16.27%, and 6.55% over YOLOv5-N, YOLOv8-N, YOLOv10-N, YOLOv11-N, YOLOv12-N, and MambaYOLO-T, respectively. Concerning mean average precision (mAP)@ 50–95, the model attains corresponding gains of 6.85%, 0.27%, 6.99%, 5.19%, 13.66%, and 2.57%, all while maintaining comparable FLOPs (G) and parameter counts. These results highlight the model’s excellent performance, confirming its effectiveness for agricultural visual inspection and suggesting a promising approach for intelligent disease monitoring in mountainous specialty crop production.
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BibTeX
@article{zhang2026synergistic,
title = {{Synergistic spatial-sequential modeling for enhanced Rosa roxburghii disease detection}},
author = {Xude Zhang and Houbing Tang and Xiaoping Wu and Jianghai Wang},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0358868},
url = {https://doi.org/10.1371/journal.pone.0358868},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Synergistic spatial-sequential modeling for enhanced Rosa roxburghii disease detection
AU - Xude Zhang
AU - Houbing Tang
AU - Xiaoping Wu
AU - Jianghai Wang
PY - 2026
DA - 2026/10/06
JO - PLOS One
DO - 10.1371/journal.pone.0358868
UR - https://doi.org/10.1371/journal.pone.0358868
AB - The rapid advancement of smart agricultural technologies has enabled deep learning–based approaches to markedly enhance the efficiency of crop disease detection and quality monitoring. To overcome the challenges of low detection efficiency and high false-positive rates in large-scale cultivation of the characteristic economic crop Rosa roxburghii, this study introduces a synergistic spatial–sequential modeling framework. This synergy is achieved via a gated fusion mechanism that bidirectionally enhances CNN-extracted local spatial features and Mamba-derived global sequential features through adaptive re-weighting, forming a mutual refinement loop instead of a simple cascade. Built upon YOLOv8, the model integrates the Mamba module from state space models (SSMs) to develop a hybrid feature extraction framework, jointly optimizing detection accuracy and inference efficiency. A selective feature enhancement strategy further strengthens the network’s ability to characterize subtle lesions on the surface of Rosa roxburghii by adaptively amplifying informative responses through channel-wise recalibration and spatial refinement. The backbone network also incorporates the selective scan for 2D data (SS2D) module to capture long-range dependencies beyond conventional convolution. Experimental validation conducted on our self-constructed dataset, which consists of field-captured images of Rosa roxburghii plants manually collected and annotated by the research team, demonstrates that the proposed model delivers notable performance gains, achieving mAP@50 improvements of 6%, 2.87%, 8.38%, 7.08%, 16.27%, and 6.55% over YOLOv5-N, YOLOv8-N, YOLOv10-N, YOLOv11-N, YOLOv12-N, and MambaYOLO-T, respectively. Concerning mean average precision (mAP)@ 50–95, the model attains corresponding gains of 6.85%, 0.27%, 6.99%, 5.19%, 13.66%, and 2.57%, all while maintaining comparable FLOPs (G) and parameter counts. These results highlight the model’s excellent performance, confirming its effectiveness for agricultural visual inspection and suggesting a promising approach for intelligent disease monitoring in mountainous specialty crop production.
SN - 1932-6203
ER -