Kenchanmane Raju Sunil K, Adkins Miles, Enersen Alex, Santana de Carvalho Daniel, Studer Anthony J, Ganapathysubramanian Baskar, Schnable Patrick S, Schnable James C
Center for Plant Science Innovation University of Nebraska-Lincoln Lincoln Nebraska USA.
Present address: Department of Plant Biology Michigan State University East Lansing Michigan USA.
Appl Plant Sci. 2020 Sep 10;8(8):e11385. doi: 10.1002/aps3.11385. eCollection 2020 Aug.
Maize yields have significantly increased over the past half-century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment.
High-throughput time series image data from water-deprived maize ( subsp. ) and sorghum () were obtained using battery-powered time-lapse cameras. A MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions.
Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period.
Automating leaf angle measurements using LAX makes it feasible to perform large-scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations.
在过去的半个世纪里，由于育种和农艺实践的进步，玉米产量显著提高。由于植株结构的变化，植株种植密度越来越高，导致叶片更加直立，从而使光合作用能够更有效地截获光照。利用多个作图群体，已在玉米和高粱中鉴定出叶片角度的自然变异。然而，传统的叶片角度表型分析技术通量低且劳动强度大，因此阻碍了对单叶叶片角度如何随时间响应环境而变化的机制理解。
使用电池供电的延时相机，获取了缺水玉米（亚种）和高粱（）的高通量时间序列图像数据。开发了一个基于MATLAB的图像处理框架，即叶片角度提取器（LAX），用于从干旱条件下玉米和高粱植株的图像中提取和量化叶片角度。
叶片角度测量结果显示了玉米和高粱叶片对干旱的不同响应。每隔一分钟跟踪叶片角度变化，就能区分出在缺水条件下出现萎蔫迹象的叶片与同一植株上在同一时间段内未出现萎蔫的其他叶片。
使用LAX自动测量叶片角度，使得进行大规模实验以评估、理解和利用植物对水分限制的时空变化成为可能。
