Graduate School, University of Tokyo, Tokyo 113-8657, Japan.
Sensors (Basel). 2019 Jan 20;19(2):413. doi: 10.3390/s19020413.
Image analysis is widely used for accurate and efficient plant monitoring. Plants have complex three-dimensional (3D) structures; hence, 3D image acquisition and analysis is useful for determining the status of plants. Here, 3D images of plants were reconstructed using a photogrammetric approach, called "structure from motion". Chlorophyll content is an important parameter that determines the status of plants. Chlorophyll content was estimated from 3D images of plants with color information. To observe changes in the chlorophyll content and plant structure, a potted plant was kept for five days under a water stress condition and its 3D images were taken once a day. As a result, the normalized Red value and the chlorophyll content were correlated; a high R² value (0.81) was obtained. The absolute error of the chlorophyll content estimation in cross-validation studies was 4.0 × 10 μg/mm². At the same time, the structural parameters (i.e., the leaf inclination angle and the azimuthal angle) were calculated by simultaneously monitoring the changes in the plant's status in terms of its chlorophyll content and structural parameters. By combining these parameters related to plant information in plant image analysis, early detection of plant stressors, such as water stress, becomes possible.
图像分析广泛用于精确和高效的植物监测。植物具有复杂的三维(3D)结构;因此,3D 图像采集和分析对于确定植物的状态很有用。在这里,使用称为“运动结构”的摄影测量方法来重建植物的 3D 图像。叶绿素含量是决定植物状态的重要参数。利用具有颜色信息的植物 3D 图像来估算叶绿素含量。为了观察叶绿素含量和植物结构的变化,将盆栽植物在缺水条件下放置五天,并每天拍摄一次其 3D 图像。结果,归一化的红色值与叶绿素含量相关;得到了高 R² 值(0.81)。在交叉验证研究中,叶绿素含量估计的绝对误差为 4.0×10μg/mm²。同时,通过同时监测植物叶绿素含量和结构参数的变化,计算出结构参数(即叶片倾斜角和方位角)。通过在植物图像分析中结合与植物信息相关的这些参数,可以实现对植物胁迫(如水胁迫)的早期检测。