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首页> 外文期刊>Turkish Journal of Agriculture & Forestry >Predicting germination of Medicago sativa and Onobrychis viciifolia seedsby using image analysis
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Predicting germination of Medicago sativa and Onobrychis viciifolia seedsby using image analysis

机译：利用图像分析预测紫花苜蓿和野菜种子的萌发

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Image analysis is an accessible method that can convert qualitative variables to quantitative ones. Computer imaging has been used in seed biology in various ways, including seed vigor testing and seed identification. In this paper, the seeds of 2 species, Medicago sativa and Onobrychis viciifolia^ were studied. Laboratory tests and a computerized experiment were conducted to evaluate the effects of accelerated aging on the seed vigor of both species. We measured the rate of germination using a factorial and completely randomized design, with10 treatment combinations replicated 3 times. The main factors were accelerated aging (6,12,18， 24, and 30 h) and species (Medicago sativa and Onobrychis viciifolia). A CCD color camera and microscope were usedto record images of seeds in top views. The images were processed by a computer to generate numerical red-green-blue (RGB) density values. The density value of image analysis was significantly correlated with germination and the results could be .used as a measure of seed vigor. Different statistics (root mean square error, coefficient of residual mass, model efficiency, and coefficient of correlation) indicated that selective models did a fair jobimaged seeds are nondestructive, practical, and accurate determinants of M. sativa and O. viciifolia seed quality and can distinguish between high- and poor-quality see
机译：图像分析是一种可以将定性变量转换为定量变量的简便方法。计算机成像已以各种方式用于种子生物学，包括种子活力测试和种子鉴定。本文研究了紫花苜蓿和野豌豆（Onobrychis viciifolia）2种种子。进行了实验室测试和计算机化实验，以评估加速老化对两种物种种子活力的影响。我们使用阶乘和完全随机设计测量了发芽率，重复了10次处理组合3次。主要因素是加速衰老（6、12、18、24和30 h）和物种（紫花苜蓿和野豌豆Onobrychis viciifolia）。 CCD彩色照相机和显微镜用于在顶视图中记录种子的图像。图像由计算机处理以生成数字红绿蓝（RGB）密度值。图像分析的密度值与发芽率显着相关，其结果可用于衡量种子活力。不同的统计数据（均方根误差，残留质量系数，模型效率和相关系数）表明，选择的模型做的很好，成像的种子是苜蓿和小叶锦葵种子质量的无损，实用和准确的决定因素，并且可以区分高品质和低品质See

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