农业机械学报2026，Vol.57Issue(3)：67-76,10.DOI:10.6041/j.issn.1000-1298.2026.03.007
基于YOLO-DRR与实时处理FPGA边缘计算平台的低空视角下柑橘冠层分割
Implementation of Low-altitude Citrus Canopy Segmentation Based on YOLO-DRR and Real-time FPGA Edge Computing
摘要
Abstract
The canopy is the primary source of photosynthesis in citrus fruit trees,and it has a direct impact on the growth,yield,and fruit quality of the trees.It is the foundation for healthy and productive fruit trees,and efficient and accurate monitoring of canopy growth is especially important.Monitoring the canopy structure allows planting management measures such as pruning,irrigation,and fertilization to be adjusted promptly,optimizing the internal environment of the canopy and promoting healthy fruit tree growth and development.A dataset of citrus fruit tree canopies in a natural environment was created and a lightweight YOLO-DRR segmentation model(YOLO v5s-seg-DSConv-RFEM-RIME)was proposed to address the issues of dense planting in citrus orchards and overlap shading between canopies,which affect fruit tree growth efficiency and yield quality.Meanwhile,the model was deployed on a portable edge computing platform to improve real-time performance,reduce power consumption,and make it easier to use in inter-orchard scenarios.Firstly,the segmentation accuracy of multi-scale targets was improved based on the YOLO v5s-seg model by using the scale-aware RFE module(RFEM)for backbone networks.Secondly,the use of the distribution shifting convolution module(DSConv)to replace the C3 module in the neck network reduced memory usage in the convolutional kernel,thereby increasing the speed of operations.Thirdly,the rime optimization algorithm(RIME)was used to optimize the hyperparameters of the YOLO-DRR model,and the iterative mechanism of swarm intelligence was utilized to further improve the model performance.Finally,the YOLO-DRR model was transplanted and implemented on the FPGA edge computing platform.The FPGA device was highly environmentally adaptable and can operate reliably in a wide range of temperature and humidity conditions,ensuring the device's dependability and stability in the complex and changing environment of the orchard.Simultaneously,the powerful edge computing capability of FPGA was used to ensure real-time data processing,more efficient use of hardware resources,reduction of power consumption and heat dissipation issues,and the realization of the requirement for long-term real-time segmentation monitoring of citrus fruit tree canopies in complex environments.The experimental results showed that the YOLO-DRR model segmented the canopy with 86.34%precision,88.68%recall,93.41%mAP@0.5,and 63.13%mAP@0.5:0.95.After porting it to the edge computing platform,the detection speed was increased to 19 f/s while consuming only 22 W of power.This suggested that the model proposed was capable of segmenting the canopy of a citrus fruit tree in the complex context of real-time canopy segmentation,which can meet the demand for real-time monitoring of the canopy growth environment in the orchard.关键词
柑橘果树冠层/分割/YOLO-DRR/超参数优化/FPGAKey words
canopy of citrus trees/segment/YOLO-DRR/hyperparameter optimization/FPGA分类
信息技术与安全科学引用本文复制引用
吕石磊,陈洁瑜,高鹏,李震,刘雪雅,李子杰,陈嘉鸿,高松茂,陈毅聪..基于YOLO-DRR与实时处理FPGA边缘计算平台的低空视角下柑橘冠层分割[J].农业机械学报,2026,57(3):67-76,10.基金项目
国家自然科学基金项目(32271997、31971797)、广州市重点研发计划项目(2024B03J1309)和国家现代农业产业技术体系项目(CARS-26) （32271997、31971797）
