Generating 3D Multispectral Point Clouds of Plants with Fusion of Snapshot Spectral and RGB-D Images

作者：Pengyao Xie, Ruiming Du, Zhihong Ma, Haiyan Cen · 发表于：Plant Phenomics · 年份：2023 · DOI：10.34133/plantphenomics.0040 · 被引用次数：38 · 研究领域：Remote Sensing in Agriculture、Smart Agriculture and AI、Remote Sensing and LiDAR Applications

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 (R 2 ) 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 erro...