智能化农业装备学报（中英文）2025，Vol.6Issue(2)：35-43,9.DOI:10.12398/j.issn.2096-7217.2025.02.003
果园作业机器人自主导航多任务联合感知方法研究
Multi-task joint perception framework for autonomous navigation in orchard robotics
摘要
Abstract
Orchard operational scenarios present significant challenges for visual perception,including high vegetation heterogeneity,dynamic lighting variations,and diverse target morphologies.Traditional single-task visual perception models suffer from low feature reusability and high computational redundancy,thereby inadequately addressing real-time environmental perception demands for agricultural robots.This study proposes AgriYOLOP,a lightweight multi-task collaborative perception framework specifically designed for orchard environments.Through a systematic reconstruction of the YOLOP architecture,AgriYOLOP incorporates an efficient backbone network,enhanced anchor-free detection techniques,feature pyramid networks(FPN),path aggregation networks(PAN),and task-adaptive loss function weighting strategies.This framework faciliates parallel collaborative processing of three critical percetption tasks:trunk detection,obstacle recognition,and traversable region segmentation.The proposed framework was validated on a self-constructed orchard dataset comprising 4 765 images(1 280 pixels×720 pixels),captured across diverse seasons,lighting conditions,and vegetation growth stages.Experimental results demonstrate that AgriYOLOP achieves 92.7%precision,94.6%recall,and 96.7%mAP50 in object detection tasks,along with 98.3%recall,99.1 F1 score,and 98.1%mIoU in traversable region segmentation.Deployed on an NVIDIA RTX 4060 platform,the model attains 69 f/s real-time inference speed with only 14 M parameters.Comparative experiments reveal that the multi-task collaborative architecture significantly enhances feature-sharing efficiency,reducing inference latency by 32.6%compared to single-task models while improving robustness to illumination and seasonal variations.This approach effectively mitigates the conventional trade-off between target detection accuracy and semantic segmentation efficiency encountered in real-time agricultural robotic applications.The study provides a high-precision,low-latency real-time perception solution for autonomous orchard robot navigation.关键词
多任务学习/果园环境感知/目标检测/语义分割/农业机器人Key words
multi-task learning/orchard environment perception/target detection/semantic segmentation/agricultural robot分类
农业科技引用本文复制引用
张津国,蔡建峰,姜蓉蓉,余山山,王蓬勃..果园作业机器人自主导航多任务联合感知方法研究[J].智能化农业装备学报（中英文）,2025,6(2):35-43,9.基金项目
国家重点研发计划项目(2022YFB4702202) （2022YFB4702202）
江苏省农业农村厅农机研发制造推广应用一体化试点项目(JSYTH07) National Key Research and Development Program of China(2022YFB4702202) （JSYTH07）
Integrated Pilot Project for Agricultural Machinery Research,Development,Manufacturing,Promotion and Application of Jiangsu Provincial Depart-ment of Agriculture and Rural Affairs(JSYTH07) （JSYTH07）
