在农业3D点云分类中弥合领域差距,使用对抗领域适应
Zainab Fatima1,2, Shehnila Zardari3, Muhammad Hassan Tanveer4
1Department of Robotics and Mechatronics Engineering, Kennesaw State University, Marietta, GA, 30060, USA. zfatima@kennesaw.edu.
Scientific reports
|November 25, 2025
概括
本研究介绍了使用点云进行植物分类的3D域适应方法. 该方法在现实世界农业环境中达到97%的准确性,改善了精准农业.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 传统的二维图像限制了农业中的三维传感.
- 在应用3D技术用于植物监测和分类方面存在差距.
- 域移动挑战了在受控数据与现实数据上训练的模型.
研究的目的:
- 为农业3D点云分类开发一个对抗性无监督域适应框架.
- 为了解决控制 (Crops3D) 和现实 (Pheno4D) 数据集之间的领域转移.
- 使用3D传感实现强大的植物表型.
主要方法:
- 使用基于PointNet的功能提取器.
- 采用了一个带有梯度反转层 (GRL) 的域区分器.
- 纳入了一个缩最小化目标,用于对未标记的目标数据进行有信心的预测.
主要成果:
- 在目标域 (Pheno4D) 上实现了97%的分类准确度.
- 尽管有显著的数据集差异,但每类F1得分都很强.
- 评估实时性能和边缘设备部署可行性.
结论:
- 拟议的3D领域适应框架显著提高了精密农业中的植物分类.
- 该方法显示了创造更可通用的植物表型模型的潜力.
- 3D域调整对于推进农业监测技术至关重要.
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