Wageningen University & Research, Food and Biobased Research, Bornse Weilanden 9, P.O. Box 17, 6700AA, Wageningen, the Netherlands.
Anal Chim Acta. 2021 Dec 1;1187:339154. doi: 10.1016/j.aca.2021.339154. Epub 2021 Oct 7.
Visible and near-infrared (Vis-NIR) spectral imaging is appearing as a potential tool to support high-throughput digital agricultural plant phenotyping. One of the uses of spectral imaging is to predict non-destructively the chemical constituents in the plants such as nitrogen content which can be related to the functional status of plants. However, before using high-throughput spectral imaging, it requires extensive calibration, just as needed for any other spectral sensor. Calibrating the high-throughput spectral imaging setup can be a challenging task as the resources needed to run experiments in high-throughput setups are far more than performing measurements with point spectrometers. Hence, to supply a resource-efficient approach to calibrate spectral cameras integrated with high-throughput plant phenotyping setups, this study proposes the use of chemometric calibration transfer (CT) and model update. The main idea was to use a point spectrometer to develop the primary model and transfer it to the spectral cameras integrated into the high-throughput setups. The potential of the approach was showed using a real Vis-NIR dataset related to nitrogen prediction in wheat plants measured with point spectrometer, tabletop spectral cameras and spectral cameras integrated with a high-throughput plant phenotyping setup. For CT and model update, direct standardization and parameter-free calibration enhancement approaches were explored. A key aim of this study was to only use and compare techniques that does not require any further optimization as they can be easily implemented by the plant biologist in future applications. The proposed approach based on the transfer of point spectroscopy models to spectral cameras in a high-throughput setup can allow spectral calibrations to be sharable and widely applicable, thus helping the global digital plant phenotyping community.
可见近红外（Vis-NIR）光谱成像是一种支持高通量数字农业植物表型分析的潜在工具。光谱成像的用途之一是无损预测植物中的化学成分，如氮含量，这可以与植物的功能状态相关联。然而，在使用高通量光谱成像之前，它需要广泛的校准，就像任何其他光谱传感器一样。由于在高通量设置中运行实验所需的资源远远超过使用点光谱仪进行测量，因此校准高通量光谱成像设置可能是一项具有挑战性的任务。因此，为了提供一种资源高效的方法来校准集成高通量植物表型设置的光谱相机，本研究提出了使用化学计量学校准转移（CT）和模型更新。主要思想是使用点光谱仪开发主模型，并将其转移到集成到高通量设置中的光谱相机中。本研究使用与使用点光谱仪测量的小麦植株氮预测相关的真实 Vis-NIR 数据集，台式光谱相机和集成到高通量植物表型设置中的光谱相机，展示了该方法的潜力。对于 CT 和模型更新，探索了直接标准化和无参数校准增强方法。本研究的一个关键目标是仅使用和比较不需要进一步优化的技术，因为它们可以在未来的应用中由植物生物学家轻松实现。该方法基于将点光谱模型从高通量设置中的点光谱仪转移到光谱相机，可以实现光谱校准的可共享性和广泛适用性，从而帮助全球数字植物表型社区。
