The leaf rolling index (LRI) is a phenotype with significant physiological implications under drought stress. However, research on the quantification of the cotton LRI is lacking, limiting its application in drought diagnosis, irrigation guidance, and physiological assessments. This study conducted a 3D reconstruction of cotton using Structure from Motion (SFM) and Multi-View Stereo (MVS). Algorithms for leaf point cloud preprocessing and phenotype extraction were developed using the PCL point cloud library and integrated into software to calculate the leaf area and perimeter. The LRI was quantified in 3D space based on the point cloud area ratio. On this basis, we analyze the relationships between LRI and leaf physiological indicators such as leaf water potential (LWP), relative water content (RWC), stomatal conductance (gs), and electron transport rate (ETR) at the seedling and flowering stages. The results indicate that the cotton LRI provides a stable indicator of drought stress, which is mainly reflected in the stable correlation between the LRI and water physiological parameters (LWP, and RWC), with coefficients of determination (R²) exceeding 0.70. Furthermore, the correlation between the LRI and the ETR suggests that the LRI could be used to assess photosynthetic efficiency under drought stress. This study demonstrates that LRI based on 3D vision in cotton may serve as a reliable morphological indicator for indicating drought stress and evaluating photosynthetic efficiency.

中文翻译：

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一种新的基于 3D 视觉的叶片滚动指数 （LRI） 及其作为棉花干旱胁迫稳定指标的应用


卷叶指数 （LRI） 是一种在干旱胁迫下具有显著生理意义的表型。然而，缺乏对棉花 LRI 定量的研究，限制了其在干旱诊断、灌溉指导和生理评估中的应用。本研究使用运动结构 （SFM） 和多视图立体 （MVS） 对棉花进行了 3D 重建。使用 PCL 点云库开发了叶点云预处理和表型提取算法，并将其集成到软件中以计算叶面积和周长。LRI 在 3D 空间中根据点云面积比进行量化。在此基础上，我们分析了 LRI 与幼苗和开花期叶片水势 （LWP） 、相对含水量 （RWC）、气孔导度 （gs） 和电子传递速率 （ETR） 等生理指标之间的关系。结果表明，棉花LRI提供了稳定的干旱胁迫指标，主要体现在LRI与水生理参数（LWP和RWC）之间的稳定相关性，决定系数（R²）超过0.70。此外，LRI 和 ETR 之间的相关性表明，LRI 可用于评估干旱胁迫下的光合效率。这项研究表明，基于棉花 3D 视觉的 LRI 可以作为指示干旱胁迫和评估光合效率的可靠形态指标。