适应对称自匹配为3D点云完成复杂的天花板环境中封闭的西红水果的复杂的天花板环境
Wenqin Wang1, Chengda Lin1, Haiyu Shui1
1College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China.
Plants (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一个自适应对称自我匹配 (ASSM) 算法,以从不完整的点云数据准确地重建3D番茄水果形态. ASSM方法提高了智能农业系统的精度.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 植物科学 植物科学
背景情况:
- 优化番茄产量和质量对于全球粮食安全至关重要.
- 由于封闭,设施农业在获得完整的3D水果数据方面面临着挑战.
- 传统方法在不完整的数据上扎,阻碍了准确的果实形态恢复.
研究的目的:
- 从不完整的点云数据开发一种新的算法,用于准确的3D水果重建.
- 克服依赖于数据完整性的传统方法的局限性.
- 为了在智能农业系统中实现精确的植物表型.
主要方法:
- 提出了一个自适应对称自我匹配 (ASSM) 算法.
- 基于实时缺陷检测的动态调整对称平面.
- 在多对称性约束和三直角对称平面系统下实现点云完成.
主要成果:
- 取得了高精度 (R2:0.9914长,0.9880宽,0.9349高) 的番茄果实与5 - 70%的遮.
- 与圆形适配相比,根平均平方误差 (RMSE) 减少了23.51-56.10%,比圆形适配.
- 经过对茄子水果的验证,证明了跨作物适应性.
结论:
- 在ASSM算法有效地重建3D水果形态,尽管显著的数据封闭.
- 这种方法克服了传统技术对数据完整性的依赖.
- 提供高精度的3D数据,对于智能农业中的植物生长监测和表型化至关重要.
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