GCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes
5 affiliations
- 1College of Electrical and Mechanical Engineering, Hunan Agricultural University
- 2Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics
- 3Hunan Provincial Key Laboratory of Intelligent Agricultural Machinery Equipment
- 4Department of Computer and Electrical Engineering, Mid Sweden University
- 5College of Intelligent Science and Engineering, Beijing University of Agriculture
Abstract
Precise identification of chili flowers and buds is essential for enabling intelligent robotic pollination, continuous crop monitoring, and early yield prediction in protected horticulture. Reliable visual sensing remains challenging because chili targets are extremely small, densely distributed, and frequently occluded by foliage, while greenhouse environments introduce strong illumination variations and background reflections. These factors often lead to insufficient feature extraction and unstable detection accuracy in existing models, limiting their practical deployment in automated monitoring systems. To address these challenges, this study proposes GCP-YOLO, a lightweight yet high-performance detection framework built upon the YOLOv11n architecture. The model enhances small-target perception through three key improvements. First, a redesigned Generalized Feature Pyramid Network (GFPN) strengthens cross-scale feature interaction, improving the fusion of fine-grained texture cues and deep semantic information. Second, a C2CGA context-guided attention module is introduced to emphasize floral structural features while suppressing background noise caused by reflections and canopy clutter. Third, extended multi-scale detection heads (P2-P6) incorporate broader contextual information to reduce missed detections and false positives in dense planting scenarios. Experimental results on a custom chili flower dataset show that the proposed method achieves 92.8% precision, 83.7% recall, 90.8% mAP50, and 72.7% mAP50-95, improving upon the YOLOv11n baseline by 2.1, 1.3, 3.9, and 6.6 percentage points, respectively. Deployment on an NVIDIA Jetson AGX Orin edge platform demonstrates real-time inference at 97.9 FPS, confirming its suitability for on-device phenotyping. Overall, the proposed approach significantly improves detection robustness under complex greenhouse conditions, providing an effective visual sensing methodology for automated crop monitoring and data-driven yield estimation.
Related
- Chili-Seg: a high-precision detection and instance segmentation model for chili flowers in complex environments
- Stage-wise collaborative feature enhancement for accurate maize insect pest detection in complex agricultural scenes
- FSF-RTDETR: a frequency–spatial feature fusion network for eggplant fruit disease and pest detection in complex agricultural scenes
- BSS-YOLO: honey peach ripeness detection in complex orchard scenes under simulated adverse-weather conditions and its implications for fruit industry management
- Cross-level attention and lightweight feature extraction for improved tomato ripeness detection in complex greenhouse environments
BibTeX
@article{kuang2026gcpyolo,
title = {{GCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes}},
author = {Minqiu Kuang and Yushi Wang and Xiaojian Li and Xiangjun Zou and Dawei Liu and Yang Xiang and Sebastian Bader and Fangping XIE and Yuxuan Zhang and Xu Li},
journal = {Frontiers in Plant Science},
year = {2026},
month = jul,
doi = {10.3389/fpls.2026.1879612},
url = {https://doi.org/10.3389/fpls.2026.1879612},
note = {CC BY},
}
RIS
TY - JOUR
TI - GCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes
AU - Minqiu Kuang
AU - Yushi Wang
AU - Xiaojian Li
AU - Xiangjun Zou
AU - Dawei Liu
AU - Yang Xiang
AU - Sebastian Bader
AU - Fangping XIE
AU - Yuxuan Zhang
AU - Xu Li
PY - 2026
DA - 2026/07/06
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1879612
UR - https://doi.org/10.3389/fpls.2026.1879612
AB - Precise identification of chili flowers and buds is essential for enabling intelligent robotic pollination, continuous crop monitoring, and early yield prediction in protected horticulture. Reliable visual sensing remains challenging because chili targets are extremely small, densely distributed, and frequently occluded by foliage, while greenhouse environments introduce strong illumination variations and background reflections. These factors often lead to insufficient feature extraction and unstable detection accuracy in existing models, limiting their practical deployment in automated monitoring systems. To address these challenges, this study proposes GCP-YOLO, a lightweight yet high-performance detection framework built upon the YOLOv11n architecture. The model enhances small-target perception through three key improvements. First, a redesigned Generalized Feature Pyramid Network (GFPN) strengthens cross-scale feature interaction, improving the fusion of fine-grained texture cues and deep semantic information. Second, a C2CGA context-guided attention module is introduced to emphasize floral structural features while suppressing background noise caused by reflections and canopy clutter. Third, extended multi-scale detection heads (P2-P6) incorporate broader contextual information to reduce missed detections and false positives in dense planting scenarios. Experimental results on a custom chili flower dataset show that the proposed method achieves 92.8% precision, 83.7% recall, 90.8% mAP50, and 72.7% mAP50-95, improving upon the YOLOv11n baseline by 2.1, 1.3, 3.9, and 6.6 percentage points, respectively. Deployment on an NVIDIA Jetson AGX Orin edge platform demonstrates real-time inference at 97.9 FPS, confirming its suitability for on-device phenotyping. Overall, the proposed approach significantly improves detection robustness under complex greenhouse conditions, providing an effective visual sensing methodology for automated crop monitoring and data-driven yield estimation.
SN - 1664-462X
ER -