Komyshev Evgenii, Genaev Mikhail, Afonnikov Dmitry
Laboratory of Evolutionary Bioinformatics and Theoretical Genetics, Department of Systems Biology, Institute of Cytology and Genetics Siberian Branch of Russian Academy of Sciences (SB RAS) Novosibirsk, Russia.
Chair of Informational Biology, Novosibirsk State University Novosibirsk, Russia.
Front Plant Sci. 2017 Jan 4;7:1990. doi: 10.3389/fpls.2016.01990. eCollection 2016.
Grain morphometry in cereals is an important step in selecting new high-yielding plants. Manual assessment of parameters such as the number of grains per ear and grain size is laborious. One solution to this problem is image-based analysis that can be performed using a desktop PC. Furthermore, the effectiveness of analysis performed in the field can be improved through the use of mobile devices. In this paper, we propose a method for the automated evaluation of phenotypic parameters of grains using mobile devices running the Android operational system. The experimental results show that this approach is efficient and sufficiently accurate for the large-scale analysis of phenotypic characteristics in wheat grains. Evaluation of our application under six different lighting conditions and three mobile devices demonstrated that the lighting of the paper has significant influence on the accuracy of our method, unlike the smartphone type.
谷物形态测量是谷物中选择新的高产植物的重要步骤。手动评估诸如每穗粒数和籽粒大小等参数非常费力。解决此问题的一种方法是基于图像的分析，可使用台式计算机执行。此外，通过使用移动设备可以提高在田间进行分析的效率。在本文中，我们提出了一种使用运行安卓操作系统的移动设备自动评估籽粒表型参数的方法。实验结果表明，该方法对于小麦籽粒表型特征的大规模分析是高效且足够准确的。在六种不同光照条件和三款移动设备下对我们的应用程序进行评估表明，纸张的光照对我们方法的准确性有显著影响，但智能手机类型则不然。
