Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield‐limiting factors in wheat

Abstract

Hyperspectral instruments acquire spectral information in many narrow, contiguous bands throughout the visible, near-infrared and shortwave regions of the electromagnetic spectrum. Hyperspectral techniques are becoming very powerful tools for characterizing plants and nondestructively quantifying their chemical and physical properties because of their ability to provide layered trait information within the same spectral region. However, to effectively make use of hyperspectral sensing, an understanding of the theory behind these techniques, the power, and the limitations of the resulting data is required. This article presents an overview of hyperspectral sensing in regard to principles, instrumentation, processing methods, and current applications, specifically focusing on the quantification of yield-limiting factors in wheat (Triticum aestivum L.). The spectral properties of plants across the electromagnetic spectrum are first described to achieve a better understanding of plant-light interactions. Basic information about different imaging approaches is provided as are the necessary considerations for the analysis of hyperspectral data. Some of the major technical challenges associated with hyperspectral imaging as well as future directions are discussed. Finally, as an example crop, the use of hyperspectral techniques for quantifying yield-limiting factors in wheat is presented.

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

“…As a result, their advantages have not been shown in this study, and slop corrections were not required for the study data. As a result, FDR, which is effective in enhancing resolution, as well as correcting baseline shifts [91], but is influenced by slop errors, also showed high performances for chl a, b and their ratios. CR was effective in estimating the car-related parameters (i.e., car, chl a:car, and chl:car).…”

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

“…Different hyperspectral imaging technologies take different approaches when addressing this question, including providing their own light source with known properties, using standard panels with known reflective properties in images, or including a second sensor facing the opposite direction of the main sensor to directly measure the intensity of the incoming light at different wavelengths directly. The technical details of how hyperspectral measurements can be made are beyond the scope of this review, but have been well explained elsewhere ( Bruning et al., 2020 ).…”

Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement

“…Other indices are being developed and explored as well that correlate with shoot nitrogen uptake, leaf nitrogen content, and yield components in wheat [41]. Further work on modelling relationships between SVIs and mineral macronutrient (N, P, K, S) and micronutrient (such as Zn, Fe, and B) concentrations is underway [42]. Currently available hyperspectral devices are capable of assaying spectral regions that enable users to carry out research and breeding in wheat using these SVIs and others.…”