最近来自比利时和马来西亚的植物科学家利用高通量植物表型平台所集成的高光谱成像模块发表了题为Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform的论文，文章发表在知名期刊ISPRS Journal of Photogrammetry and Remote Sensing 138: 121–138上。高光谱成像模块的集成和分析是个世界性难题，目前世界上能整个该系统的厂家非常有限，对厂家的自动化集成能力和数据分析能力提出了巨大的挑战。
作为全球一家将大规模自动化理念和工业级零件和设备整合入植物成像系统的厂家，SMO公司在植物表型成像分析领域处于全球领先的技术前列，大面积叶绿素荧光成像系统、高光谱成像模块、激光3D多光谱成像系统使WIWAM成为植物表型分析与功能成像领域最为先进的仪器设备，植物生长、胁迫响应等测量参数达几百个。工业级部件品质使系统非常耐用，基本免维护，与同类产品相比，特点突出。目前WIWAM植物表型平台分为WIWAM XY，WIWAM Line、WIWAM Conveyor、WIWAM Imaging Box以及WIWAM FIELD等几个系列，涵盖从室内表型成像到室外表型成像各个领域，多维度、多类型传感器和设备的应用为植物表型科研提供了很好的借鉴思路。
Mohd Asaari MS, Mishra P, Mertens S, Dhondt S, Inzé D, Wuyts N, Scheunders P (2018) Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform. ISPRS Journal of Photogrammetry and Remote Sensing 138: 121–138
Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform
Mohd Asaari, Mohd Shahrimie ,Mishra, Puneet ,Mertens, Stien ,Dhondt, Stijn ,Inzé, Dirk ,Wuyts, Nathalie,Scheunders, Paul
The potential of close-range hyperspectral imaging (HSI) as a tool for detecting early drought stress responses in plants grown in a high-throughput plant phenotyping platform (HTPPP) was explored. Reflectance spectra from leaves in close-range imaging are highly influenced by plant geometry and its specific alignment towards the imaging system. This induces high uninformative variability in the recorded signals, whereas the spectral signature informing on plant biological traits remains undisclosed. A linear reflectance model that describes the effect of the distance and orientation of each pixel of a plant with respect to the imaging system was applied. By solving this model for the linear coefficients, the spectra were corrected for the uninformative illumination effects. This approach, however, was constrained by the requirement of a reference spectrum, which was difficult to obtain. As an alternative, the standard normal variate (SNV) normalisation method was applied to reduce this uninformative variability.
Once the envisioned illumination effects were eliminated, the remaining differences in plant spectra were assumed to be related to changes in plant traits. To distinguish the stress-related phenomena from regular growth dynamics, a spectral analysis procedure was developed based on clustering, a supervised band selection, and a direct calculation of a spectral similarity measure against a reference. To test the significance of the discrimination between healthy and stressed plants, a statistical test was conducted using a one-way analysis of variance (ANOVA) technique.
The proposed analysis techniques was validated with HSI data of maize plants (Zea mays L.) acquired in a HTPPP for early detection of drought stress in maize plant. Results showed that the pre-processing of reflectance spectra with the SNV effectively reduces the variability due to the expected illumination effects. The proposed spectral analysis method on the normalized spectra successfully detected drought stress from the third day of drought induction, confirming the potential of HSI for drought stress detection studies and further supporting its adoption in HTPPP.
ISPRS Journal of Photogrammetry and Remote Sensing
Close-range hyperspectral imaging;
Linear reflectance model;
Standard normal variate;
Spectral similarity measure;