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.
