Plant phenotype is a key link in understanding gene function and environmental effects, and with the demand of further research of plant function genomics, the traditional phenotypic observation has become the main bottleneck. High-throughput plant phenome analysis technology such as image processing is an effective way to solve this problem. In this paper, a maize plant drought stress phenotype testing method was proposed based on the time-series images. Firstly, an acquisition system of time-series images was developed. System hardware included the mvc3000 high speed industrial camera (the maximum resolution 1600×1200 pixels, 24 FPS(frames per second)), the Pentax len (8.5 mm, f/1.5), the storage card (32G), the holder and the background. Software development environment included Win7 operating system, Microsoft Visual Studio 2010 Professional, and OpenCV2.4.3. Maize plant time-series images were acquired from 08:00 to 17:00 every 10 mins, andvarious natural light conditions strongly affected the profile of the crop images taken outdoors.This often made it rather difficult for vegetation parts segmented from the background parts in an image. We adopted theK-means and SVM (support vector machine) algorithm to create a classifier used in rough segmentation, and the classifier was composed of 24 dimension feature vectors including color features, texture features, and gradient features. The result refinement was implemented with a non-local mean (NLM) filter, and actually the anisotropic structural-aware filter was adopted to implement the NLM function, in which the pixels were reinforced with each other in the same structure and even if they were not the first-order neighboring pixels. Secondly, the plant organs such as stem and leaf were segmented in binary image with a scan line method, which used the plant morphology characteristics such as alternate phyllotaxis, curved blade, and leaf margin in folds as prior knowledge. The method denoted the pixels according to the number of neighborhoods. Specifically, each row of pixels in the scan line was marked as a candidate region, and the candidate regions were labeled as the original organ or new organ according to the number of neighborhoods. When all of the candidate regions were labeled, a cluster of regions which presented rectangular shape were defined as plant stem. A labeled cluster of leaf would be merged to the other cluster if they shared neighborhood regions up to 70%. Finally, the ratio SOP1/SOP2 was used to characterize the drought stress response.SOP is the intersection point of stem and leaf, SOP is a point in stem line, LEN is the length of leaf tip to stem line, SOP1 is a point in leaf curve with length 1/4LEN, SOP2 is a point in leaf curve with length 2/3 LEN. SOP1/SOP2 represented the degree of leaf bending. It was comparable even between the different leaf orders, and had strong robustness to environmental factor disturbance. The field tests showed that the SOP1/SOP2 of drought-resistant genetically-modified maize presented an ascending trend before midday and a descending trend after then, which could be caused by transpiration leading to leaf water loss. The change trend of environmental factors such as air relative humidity against SOP1/SOP2 showed that under drought stress condition, genetically-modified maize was more sensitive to moisture content of the atmosphere, and the test on conventional maize plant showed that the SOP1/SOP2 lacked the regularity with the time.%为了快速准确获取植物表型数据，该文提出了一种基于时间序列多图像的玉米植株干旱胁迫表型检测方法。首先，以固定时间间隔获取干旱胁迫条件下玉米植株一天内的图像数据，采用统计学习方法对图像序列进行初始背景分割，非局部均值（non-local mean，NLM）滤波对初始背景分割进一步求精，去除变化光照条件对图像序列分割的影响。在植株器官分割阶段，融合植株形态先验知识，通过逐行扫描和邻域数量判断拆分植株器官像素。最后，提出了叶片角度比值的干旱胁迫表型参数计算方法。结果显示，耐旱植株的叶片角度比值一天内从0.63上升到0.70后再降到0.65，而非耐旱植株的叶片角度比值维持在0.65左右，随时间变化不大。研究结果为阐明玉米植株耐旱表型参数与环境参数的协同变化关系，以及不同基因型玉米植株环境应激反应的细微形态变化提供方法和技术解决方案。
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