Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale

Abstract

Background The use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up. Results This review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices. Conclusions This publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

“…Changes in the reflectance spectra of plants can be detected by hyper-and multispectral cameras, which are tools for hyperspectral and multispectral imaging, respectively. In accordance with Paulus and Mahlein [44] and Katsoulas et al [22], a camera measuring more than 20 wavelengths can be classified as a hyperspectral camera; in contrast, a camera measuring less than 20 wavelengths is a multispectral camera. Hyperspectral data can be presented as a set of frames ("hypercube"), where each frame shows the spatial reflectance distribution at a specific wavelength; as a result, the whole spectrum of the reflected light in each point of the investigated object can be analyzed.…”

“…Finally, the spectral analysis of the reflected light on the basis of hyper-and multispectral imaging is a very promising method of plant remote sensing [33,44]. It is based on strong relations between the spectra of the reflected light and growth, physiological, and biochemical parameters of plants [7,12,13].…”

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

“…The red-edge region resides between the red and the near-infrared (NIR) regions, which is correlated with internal leaf structure and chlorophyll absorptions ( Clevers et al, 2002 ; Clark et al, 2005 ; Liu C. et al, 2021 ). The NIR plateau (780–1327 nm) is another important hyperspectral reflectance region that is dominated by the amount and interaction of water and air within the intercellular spaces ( Hennessy et al, 2020 ; Paulus and Mahlein, 2020 ; Okubo, 2021 ).…”

A phytopathometry glossary for the twenty-first century: towards consistency and precision in intra- and inter-disciplinary dialogues

“…The terms and concepts defined subsequently are from a broad range of sources, but most notably, they stem from those initially defined by Nutter et al (1991) and from Nutter (2001), D'Arcy et al (2001, Madden et al (2007), Bock et al (2010), andBock et al (2016). Other sentinel references are included (Everitt 1998;Nutter et al 2006;McRoberts et al 2003;Behmann et al 2015;Del Ponte et al 2017;Paulus and Mahlein 2020;Paulus 2019). The Special Topic article of Nutter et al (1991) was the outcome of a subcommittee that was appointed by the Plant Disease Losses Committee of the American Phytopathological Society.…”