In recent years, plant phenotyping technologies have been widely applied to evaluate complex plant traits such as morphology, physiology, ecology, biochemistry, tolerance, growth and yield. Hyperspectral/multispectral cameras, artificial lighting sources, mechanisms and computers together capture images of different species of plants. Due to the non-uniform intensity of lighting sources in different wavelengths, raw images need to be calibrated using white references. Flat white panels are typically scanned as a white reference. However, geometrical factors such as leaf tilt angles cannot be calibrated by flat white references. In this publication, the effectiveness of using angled white reference to calibrate corresponding raw images was first demonstrated. Furthermore, a 3D white referencing library integrating different angles and spatial positions in the system of a hyperspectral camera and a Kinect V2 depth sensor was created. Thus, a pixel on the leaf surface can be calibrated by a point with the nearest tilt angle and spatial position in the 3D referencing library. The validating samples for this referencing library were soybean leaves grown in a greenhouse. The results showed that the reflectance spectra after 3D calibration were closer to the standard calibration (flat leaf calibrated by flat white reference) than the conventional flat white referencing calibration. Furthermore, the pixel-level normalized difference vegetation index (NDVI) distribution over the soybean leaf surface after 3D calibration was also closer to the standard calibration. This proposed 3D white referencing method had the potential to improve calibration quality of plant images. Integrating with LiDAR sensors, this new approach has an opportunity to be applied in field environments.

中文翻译：

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基于高光谱图像与3D点云融合的大豆叶片3D白标方法

近年来，植物表型技术被广泛应用于评价植物形态、生理、生态、生物化学、耐受性、生长和产量等复杂性状。高光谱/多光谱相机、人工光源、机械装置和计算机共同捕捉不同种类植物的图像。由于不同波长的光源强度不均匀，原始图像需要使用白色参考进行校准。平面白色面板通常作为白色参考进行扫描。然而，诸如叶片倾斜角之类的几何因素无法通过平坦的白色参考进行校准。在该出版物中，首次展示了使用倾斜白色参考校准相应原始图像的有效性。此外，在高光谱相机和 Kinect V2 深度传感器的系统中集成了不同角度和空间位置的 3D 白色参考库被创建。因此，叶子表面上的像素可以通过 3D 参考库中具有最近倾斜角和空间位置的点进行校准。该参考文库的验证样品是在温室中生长的大豆叶。结果表明，与传统的平白参考校准相比，3D校准后的反射光谱更接近标准校准（由平白参考校准的平叶）。此外，3D校准后大豆叶面的像素级归一化植被指数（NDVI）分布也更接近标准校准。这种提出的 3D 白色参考方法有可能提高植物图像的校准质量。与 LiDAR 传感器集成，这种新方法有机会应用于现场环境。