一种计算机视觉方法来量化玉米 (Zea mays L.) 的叶形状,并模拟其对光线拦截的影响
Dina Otto1, Sebastian Munz1, Emir Memic1
1Institute of Crop Science, Agronomy Department, University of Hohenheim, Stuttgart, Germany.
Frontiers in plant science
|July 8, 2025
概括
一种新的摄像头方法准确地测量了玉米叶形状,改进了作物模型. 这种基于图像的方法增强了对光线拦截和农业决策的模拟.
科学领域:
- 植物形态学和生物物理学
- 农业工程和遥感技术
- 计算生物学和作物建模.
背景情况:
- 精确的叶子形状量化对于植物建模和理解光拦截至关重要.
- 传统的手动叶子测量是艰苦的,容易出错,对于大,波状的玉米叶子来说具有挑战性.
研究的目的:
- 开发和验证一种新的,基于图像的计算机视觉方法,用于精确的玉米叶形状分析.
- 克服手工测量的局限性,以确定功能结构植物模型 (FSPM) 的叶子形状参数.
主要方法:
- 一个新的摄像头系统 (GoPro Hero8 Black与LI-3100C面积计集成) 捕获了高分辨率的叶子视频.
- 开发了一种半自动化软件,用于对象检测,轮提取和叶子宽度确定.
- 验证包括像素计数,对比度分析和与标准手动测量进行比较.
主要成果:
- 摄像机方法在确定叶子形状参数方面证明了准确性和可靠性.
- 在玉米品种和叶子等级之间发现了叶子形状参数 (alpha,a) 的显著差异 (p < 0.01).
- 模拟显示,叶子形状的变化可以影响光线拦截率高达7%.
结论:
- 开发的摄像头方法为玉米叶形状分析提供了准确和高效的替代手工测量方法.
- 精确的叶形状数据对于改善作物生长模型和FSPMs的参数化至关重要.
- 这种方法有助于未来研究依赖于等级的叶形状效应,增强树冠代表性和农业决策.
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