使用以实例为基础的数据生成和叶子层面的结构分析,对受干旱影响的树苗的表型化
Lei Zhou1,2, Huichun Zhang1,2, Liming Bian3
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, P. R. China.
Plant phenomics (Washington, D.C.)
|July 30, 2024
概括
计算机视觉和深度学习通过分析叶子姿势,准确地识别了树苗的干旱压力. 这种表型化方法有助于开发耐旱品种,并优化灌,以获得更好的作物产量.
科学领域:
- 植物科学 植物科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 干旱压力严重威胁树的生长和产量.
- 高通量植物表型化提供了植物健康的快速,非破坏性分析.
- 准确的表型定型对于识别抗旱作物至关重要.
研究的目的:
- 开发和评估计算机视觉和深度学习方法,用于干旱压力树幼苗的表型.
- 为了能够准确计算叶子姿势和识别干旱压力.
- 评估多任务学习模型的性能,以同时确定多样性和压力水平.
主要方法:
- 实例细分被用来从彩色图像中提取叶子,树和中脉区域.
- 开发了一种数据集增强技术,以最大限度地减少手动注释的努力.
- 树枝和中脉的水平角度被计算为叶子姿势数字化.
- 包括MobileNet在内的多任务学习模型被用于压力水平和品种分类.
主要成果:
- 叶子的姿势通过计算树的水平角度 (MAE 10.7°) 和中脉 (MAE 8.2°) 来进行数字化.
- 发现干旱压力会增加树叶的水平角度.
- 多任务MobileNet模型实现了高精度 (99%的多样性,76%的压力水平),优于单任务模型.
结论:
- 拟议的计算机视觉和深度学习方法有效地使树苗的干旱压力表型化成为可能.
- 这种方法可用于选耐旱品种.
- 这些发现支持精确的灌决策,以改善作物管理.
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