Guo Wei, Fukatsu Tokihiro, Ninomiya Seishi
Institute for Sustainable Agro-ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1. Midori-cho, Nishi-Tokyo, Tokyo 188-0002 Japan.
National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8666 Japan.
Plant Methods. 2015 Feb 13;11:7. doi: 10.1186/s13007-015-0047-9. eCollection 2015.
Flowering (spikelet anthesis) is one of the most important phenotypic characteristics of paddy rice, and researchers expend efforts to observe flowering timing. Observing flowering is very time-consuming and labor-intensive, because it is still visually performed by humans. An image-based method that automatically detects the flowering of paddy rice is highly desirable. However, varying illumination, diversity of appearance of the flowering parts of the panicles, shape deformation, partial occlusion, and complex background make the development of such a method challenging.
We developed a method for detecting flowering panicles of rice in RGB images using scale-invariant feature transform descriptors, bag of visual words, and a machine learning method, support vector machine. Applying the method to time-series images, we estimated the number of flowering panicles and the diurnal peak of flowering on each day. The method accurately detected the flowering parts of panicles during the flowering period and quantified the daily and diurnal flowering pattern.
A powerful method for automatically detecting flowering panicles of paddy rice in time-series RGB images taken under natural field conditions is described. The method can automatically count flowering panicles. In application to time-series images, the proposed method can well quantify the daily amount and the diurnal changes of flowering during the flowering period and identify daily peaks of flowering.
开花（小穗开花）是水稻最重要的表型特征之一，研究人员致力于观察开花时间。观察开花非常耗时且费力，因为目前仍需人工肉眼进行观察。因此，非常需要一种基于图像的自动检测水稻开花的方法。然而，光照变化、稻穗开花部分外观的多样性、形状变形、部分遮挡以及复杂的背景使得开发这样的方法具有挑战性。
我们开发了一种利用尺度不变特征变换描述符、视觉词袋和机器学习方法（支持向量机）来检测RGB图像中水稻开花稻穗的方法。将该方法应用于时间序列图像，我们估计了每天开花稻穗的数量和开花的日峰值。该方法在开花期准确检测到了稻穗的开花部分，并量化了每日和昼夜的开花模式。
本文描述了一种在自然田间条件下拍摄的时间序列RGB图像中自动检测水稻开花稻穗的有效方法。该方法可以自动计数开花稻穗。在应用于时间序列图像时，该方法能够很好地量化开花期的每日开花量和昼夜变化，并识别每日开花峰值。
