在基于深度学习的封闭条件下完成植物叶子的点云
Haibo Chen1,2, Shengbo Liu2,3, Congyue Wang2,3
1Experimental Basis and Practical Training Center, South China Agricultural University, Guangzhou, China.
Plant phenomics (Washington, D.C.)
|January 19, 2024
概括
这项研究引入了一种新的深度学习方法,以补充3D植物点云中缺少的数据,显著提高了开花的中国白菜的植物表型和叶面积估计的准确性.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物生物学 植物生物学
背景情况:
- 3D点云技术对于非侵入性植物表型化至关重要,但由于传感器限制和叶子封闭,它经常遭受不完整数据的困扰.
- 不准确的表型参数提取阻碍了植物育种,农业和研究的进步.
研究的目的:
- 开发和验证基于深度学习的解决方案,以完成不完整的开花的中国白菜点云.
- 为了提高植物表型参数提取的准确性,特别是叶面积,使用3D点云数据.
- 从单视图RGB-D图像进行3D工厂重建的新框架.
主要方法:
- 利用点断层网络技术处理和完成开花的中国白菜叶的不完整点云.
- 为网络培训和验证,构建了开花的中国白菜叶的点云数据集.
- 开发了使用单视图RGB-D (红色,绿色,蓝色和深度) 图像和深度学习来处理阻塞的3D植物重建的新框架.
主要成果:
- 拟议的网络在完成各种叶点云形态和各种缺失数据场景方面表现出强度.
- 在点云完成后,叶面积估计的准确性显著提高:R2从0.9162增加到0.9637,RMSE从15.88cm2减少到6.79cm2,平均相对误差从22.11%减少到8.82%.
- 该方法能够有效和准确地检索表型参数,增强非破坏性植物表型.
结论:
- 基于点断层网络的方法有效地解决了不完整的3D植物点云的挑战.
- 这种深度学习框架显著提高了叶面积估计和其他表型参数的准确性.
- 这项研究为使用3D成像技术进行非破坏性植物表型化提供了一个有希望的新方向.
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