Full
ZHANG Jinguo, E-mail: 1432263736@qq.com
Outline
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.
34
9
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
