在恶劣天气条件下以边缘设备为导向的番茄果实稀释和收获模型
Yalu Cai1, Maolan Lin1, Zhenchang Gao2
1Department of Physics, School of Science, Jimei University, Xiamen, Fujian, China.
Physiologia plantarum
|January 27, 2026
概括
这项研究介绍了Edge-YOLO-Tomato,这是一种强大的AI模型,用于在恶劣天气中检测番茄的成熟度和大小. 它为精密农业应用实现了高精度和高效率.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 农业技术 农业技术
背景情况:
- 准确的番茄检测对于自动收获和稀释至关重要.
- 目前的系统与恶劣的天气,尺寸估计和边缘设备限制作斗争.
研究的目的:
- 开发一种强大而高效的AI模型,用于在多样化和不利的天气条件下检测番茄.
- 解决边缘设备对实时农业应用的计算限制.
主要方法:
- 介绍了TIDAW数据集,使用物理接地大气散射模型合成生成.
- 开发了Edge-YOLO-Tomato,这是一个基于YOLOv8的架构,具有物理意识的散射模块,扩展卷积,先前嵌入的Wise-IoU损失和压缩框架.
- 利用神经架构搜索模型压缩,结合大小和分层修剪.
主要成果:
- 在TIDAW数据集上,Edge-YOLO-Tomato实现了93.3%的mAP50和74.3%的mAP50:95.
- 性能优于现有的YOLOv8,YOLOv11,更快的R-CNN和RT-DETR等模型.
- 压缩模型的尺寸为4.7373 MB,精度损失最小,CPU延迟减少50%.
结论:
- 开发的系统可以在不受控制的环境中实时,强大的水果检测.
- 通过数据合成和物理意识的建模,为基于视觉的精密农业建立了一个新的范式.
- 边缘-YOLO-番茄模型为机器人农业的挑战提供了实际的解决方案.
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