Xie Pengyao, Du Ruiming, Ma Zhihong, Cen Haiyan
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
Plant Phenomics. 2023;5:0040. doi: 10.34133/plantphenomics.0040. Epub 2023 Apr 3.
Accurate and high-throughput plant phenotyping is important for accelerating crop breeding. Spectral imaging that can acquire both spectral and spatial information of plants related to structural, biochemical, and physiological traits becomes one of the popular phenotyping techniques. However, close-range spectral imaging of plants could be highly affected by the complex plant structure and illumination conditions, which becomes one of the main challenges for close-range plant phenotyping. In this study, we proposed a new method for generating high-quality plant 3-dimensional multispectral point clouds. Speeded-Up Robust Features and Demons was used for fusing depth and snapshot spectral images acquired at close range. A reflectance correction method for plant spectral images based on hemisphere references combined with artificial neural network was developed for eliminating the illumination effects. The proposed Speeded-Up Robust Features and Demons achieved an average structural similarity index measure of 0.931, outperforming the classic approaches with an average structural similarity index measure of 0.889 in RGB and snapshot spectral image registration. The distribution of digital number values of the references at different positions and orientations was simulated using artificial neural network with the determination coefficient ( ) of 0.962 and root mean squared error of 0.036. Compared with the ground truth measured by ASD spectrometer, the average root mean squared error of the reflectance spectra before and after reflectance correction at different leaf positions decreased by 78.0%. For the same leaf position, the average Euclidean distances between the multiview reflectance spectra decreased by 60.7%. Our results indicate that the proposed method achieves a good performance in generating plant 3-dimensional multispectral point clouds, which is promising for close-range plant phenotyping.
准确且高通量的植物表型分析对于加速作物育种至关重要。能够获取与植物结构、生化和生理特性相关的光谱及空间信息的光谱成像，成为了流行的表型分析技术之一。然而，植物的近距离光谱成像可能会受到复杂植物结构和光照条件的严重影响，这成为近距离植物表型分析的主要挑战之一。在本研究中，我们提出了一种生成高质量植物三维多光谱点云的新方法。使用加速稳健特征（Speeded-Up Robust Features）和恶魔算法（Demons）来融合近距离获取的深度图像和快照光谱图像。开发了一种基于半球参考结合人工神经网络的植物光谱图像反射率校正方法，以消除光照影响。所提出的加速稳健特征和恶魔算法在RGB和快照光谱图像配准中，平均结构相似性指数测量值达到0.931，优于经典方法的0.889。利用人工神经网络模拟了参考物在不同位置和方向上数字值的分布，决定系数（ ）为0.96²，均方根误差为0.036。与ASD光谱仪测量的地面真值相比，不同叶片位置反射率校正前后反射光谱的平均均方根误差降低了78.0%。对于同一叶片位置，多视图反射光谱之间的平均欧几里得距离降低了60.7%。我们的结果表明，所提出的方法在生成植物三维多光谱点云方面具有良好性能，这对于近距离植物表型分析具有广阔前景。
