AGC-YOLO: adaptive gated channel network for rice leaf disease detection
5 affiliations
- 1Department of Statistics and Data Science, Southern University of Science and Technology
- 2Department of Basic Education, Wuhu Vocational Technical University
- 3Anhui Longding Information Technology Co., Ltd
- 4School of Network Engineering, Wuhu Vocational Technical University
- 5School of Physics and Information Technology, Shaanxi Normal University
Abstract
Accurate detection of rice leaf diseases is critical for intelligent crop protection, yet class imbalance, degradation of multi-scale feature information, and the limited discriminative capability of feature fusion continue to hinder effective monitoring under field conditions. To address these challenges, this study presents AGC-YOLO, an improved detection model based on the YOLOv8s framework. First, a semantic-aware data augmentation network termed SAICP Net is devised to alleviate class imbalance and the scarcity of cross-class co-occurrence samples through a weakly supervised soft spatial prior and semantically constrained cross-category instance pasting. Second, a gated hybrid pooling module named GHP-SPPF is proposed to adaptively integrate max pooling and soft pooling via a learnable gating mechanism, thereby strengthening the discriminative representation of multi-scale lesions. Finally, a channel-adaptive fusion module termed CAFF is constructed to replace standard concatenation with channel-wise learnable weights and gated residual refinement, boosting the discriminative power of cross-scale feature fusion. Experimental results demonstrate that AGC-YOLO achieves an [email protected] of 93.5 ± 0.6% on the rice disease dataset, representing a 3.0 percentage-point improvement over the baseline, and additional experiments on two public datasets further demonstrate the effectiveness of the proposed method for disease detection.
Related
- An adaptive transformer-based model with entropy-driven knowledge distillation for single-channel EEG REM sleep detection
- Spatial organization of voltage-gated ion channel expression across molecularly defined neuronal populations in the mouse mammillary bodies
- A temperature-adaptive logit-standardized knowledge distillation for lightweight network intrusion detection
- EEG-based Alzheimer’s disease detection using two-branch multi-scale deep learning with integrated gradients-guided channel selection
- Characterization of an open-channel structure and lateral conduction pathway in the cation-selective pentameric ligand-gated ion channel, ELIC
BibTeX
@article{wang2026agcyolo,
title = {{AGC-YOLO: adaptive gated channel network for rice leaf disease detection}},
author = {Fei Wang and Yuan Lv and Li Zhao and Jun Zhang and Yang Wen and Guo Li and Xianyao Zhang and Yuxiang Zhang},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1978719},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1978719/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - AGC-YOLO: adaptive gated channel network for rice leaf disease detection
AU - Fei Wang
AU - Yuan Lv
AU - Li Zhao
AU - Jun Zhang
AU - Yang Wen
AU - Guo Li
AU - Xianyao Zhang
AU - Yuxiang Zhang
PY - 2026
DA - 2026/10/15
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1978719
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1978719/full
AB - Accurate detection of rice leaf diseases is critical for intelligent crop protection, yet class imbalance, degradation of multi-scale feature information, and the limited discriminative capability of feature fusion continue to hinder effective monitoring under field conditions. To address these challenges, this study presents AGC-YOLO, an improved detection model based on the YOLOv8s framework. First, a semantic-aware data augmentation network termed SAICP Net is devised to alleviate class imbalance and the scarcity of cross-class co-occurrence samples through a weakly supervised soft spatial prior and semantically constrained cross-category instance pasting. Second, a gated hybrid pooling module named GHP-SPPF is proposed to adaptively integrate max pooling and soft pooling via a learnable gating mechanism, thereby strengthening the discriminative representation of multi-scale lesions. Finally, a channel-adaptive fusion module termed CAFF is constructed to replace standard concatenation with channel-wise learnable weights and gated residual refinement, boosting the discriminative power of cross-scale feature fusion. Experimental results demonstrate that AGC-YOLO achieves an [email protected] of 93.5 ± 0.6% on the rice disease dataset, representing a 3.0 percentage-point improvement over the baseline, and additional experiments on two public datasets further demonstrate the effectiveness of the proposed method for disease detection.
SN - 1664-462X
ER -