通过机器学习预测Platanus × hispanica在树枝修剪后的再发芽
Qiguan Shu1, Hadi Yazdi1, Thomas Rötzer2
1Professorship for Green Technologies in Landscape Architecture, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany.
Frontiers in plant science
|March 22, 2024
概括
机器学习准确地预测了树木在修剪后重新发芽,在识别新发芽位置时达到90%以上的准确性. 这项研究表明了人工智能在园艺预测中的潜力.
科学领域:
- 园艺园艺 园艺园艺
- 计算机科学 计算机科学
- 植物科学 植物科学
背景情况:
- 重生是树木在因自然事件或修剪而失去树枝后的重要生存机制.
- 从生理上预测重新发芽是复杂的,但经验丰富的园丁依靠经验知识.
- 这项研究探讨了机器学习的应用,用于预测树木在修剪后重新生长的情况.
研究的目的:
- 探索机器学习模型在预测树木在修剪后重新生长的有效性.
- 根据树拓和拍摄数据量化这些预测的准确性.
主要方法:
- 在两年内对每年修剪的Platanus × hispanica树进行LiDAR扫描.
- 树木拓结构的抽象使用气配件.
- 开发和测试二进制和多类分类模型,以预测新芽的位置和数量.
主要成果:
- 该LGBMC分类器在预测气上新芽的存在方面达到90.8%的准确性.
- 预测新生苗的平衡准确率为80.3%.
- 高斯NB模型在预测新发芽的确切数量方面显示了82.1%的准确率,均衡准确率为42.9%.
结论:
- 这项研究验证了使用机器学习用于在修剪后重新生长预测的可行性.
- 进一步的研究应该探索不同的树种,形式和额外的变量.
- 这种方法为传统园艺专业知识提供了数据驱动的替代方案.
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