Fusion of 3D and MS imaging data has a great potential for high-throughput plant phenotyping of structural and biochemical as well as physiological traits simultaneously, which is important for decision support in agriculture and for crop breeders in selecting the best genotypes. However, lacking of 3D data integrity of various plant canopy structures and low-quality of MS images caused by the complex illumination effects make a great challenge, especially at the proximal imaging scale. Therefore, this study proposed a novel approach for adaptive data acquisition and radiometric calibration to generate high-quality 3DMPCs of plants. An efficient NBV planning method based on an UGV plant phenotyping system with a multi-sensor-equipped robotic arm was proposed to achieve adaptive data acquisition. The NeREF was employed to predict the DN values of the hemispherical reference for radiometric calibration. For NBV planning, the average total time for single plant at a joint speed of 1.55 rad/s was about 62.8 s, with an average reduction of 18.0% compared to the unplanned. The integrity of the whole-plant data was improved by an average of 23.6% compared to the fixed viewpoints alone. Compared with the ASD measurements, the RMSE of the reflectance spectra obtained from 3DMPCs at different regions of interest was 0.08 with an average decrease of 58.93% compared to the results obtained from the single-frame of MS images without 3D radiometric calibration. The 3D-calibrated plant 3DMPCs improved the predictive accuracy of PLSR for chlorophyll content, with an average increase of 0.07 in R2 and an average decrease of 21.25% in RMSE. Our approach introduced a fresh perspective on generating high-quality 3DMPCs of plants under the natural light condition, enabling more precise analysis of plant morphological and physiological parameters.

翻译：摘要：三维与多光谱成像数据的融合在同时高通量获取植物结构、生化及生理性状方面具有巨大潜力，这对于农业决策支持及作物育种者筛选最优基因型至关重要。然而，多种植物冠层结构的三维数据完整性缺失，以及复杂光照效应导致的多光谱图像质量低下，特别是在近距成像尺度上，构成了重大挑战。为此，本研究提出了一种自适应数据采集与辐射校正新方法，以生成高质量植物三维多光谱点云（3DMPC）。基于配备多传感器机械臂的UGV植物表型系统，提出了一种高效的下一个最佳视角（NBV）规划方法以实现自适应数据采集，并采用NeREF预测半球参考的像元值（DN）进行辐射校正。在NBV规划中，单株植物在关节转速1.55 rad/s下的平均总耗时约为62.8秒，较无规划方法平均减少18.0%。与固定视角相比，全株数据完整性平均提升23.6%。与ASD测量结果相比，经3DMPC获取的不同感兴趣区域反射光谱均方根误差（RMSE）为0.08，较未进行三维辐射校正的单帧多光谱图像结果平均降低58.93%。经三维辐射校正的植物3DMPC使叶绿素含量的偏最小二乘回归（PLSR）预测精度提升，决定系数（R²）平均提高0.07，RMSE平均降低21.25%。本研究为自然光照条件下生成高质量植物3DMPC提供了新视角，从而实现了对植物形态与生理参数的更精准分析。