ReviewClose range hyperspectral imaging of plants: A review
Introduction
To increase the agriculture productivity, it is important to develop high-yielding crops which can adapt to future climatic conditions (Furbank & Tester, 2011). Moreover, the assessment of existing and to be developed high-yielding crop plants is crucial to understand how the detrimental effects of surrounding environment limit their growth and yield, further supporting the development of optimal plant varieties. Traditional methods used for plant assessment are still time-consuming, labour intensive and destructive in nature (Busemeyer et al., 2013).
The need for fast, non-destructive and high throughput alternative technologies for plant assessment has lead to the development of new and the re-use of different existing sensor technologies from various scientific domains (Li, Zhang, & Huang, 2014). One such emerging sensor technology for non-destructive, rapid and automated assessment of plants is the hyperspectral imaging (HSI) (Matsuda, Tanaka, Fujita, & Iba, 2012). The application of HSI can be found in diverse domains of research such as remote sensing (Blackburn, 2007), foods (Mishra et al., 2015, Mishra et al., 2016, Wu and Sun, 2013), microbiology (Gowen, Feng, Gaston, & Valdramidis, 2015) and pharmaceutical sciences (Gendrin, Roggo, & Collet, 2008). In particular, in remote sensing, vegetation monitoring has been studied using HSI for many years (Blackburn, 2007), and has motivated the use of HSI for exploring plants at close range.
A HSI system integrates a spectrograph that records reflectance in a wide range of the spectrum, including the ultraviolet (UV), visible (VIS) and near-infrared (NIR) into a digital sensor (Bock, Poole, Parker, & Gottwald, 2010). Data is generated in the form of a 3D spatial map of spectral variation: the first two dimensions provide the spatial information and a third dimension accounts for the spectral information. Being an integration of imaging and conventional spectroscopy, HSI can obtain complementary information from both domains. While point spectroscopy gathers information to understand the physiology of the plants (Montes, Melchinger, & Reif, 2007), the information from imaging technology is used to understand the structural dynamics (Apelt et al., 2015, Bucksch et al., 2014, Dhondt et al., 2014). In combination, HSI has the potential to extract integrated spatial and spectral information related to the plant's functional dynamics regarding both structure and physiology (Bergsträsser et al., 2015, Kuska et al., 2015, Mahlein et al., 2012a, Rascher et al., 2011, Ustin and Gamon, 2010).
Various emerging applications of HSI related to plants biochemistry estimation (Vigneau, Ecarnot, Rabatel, & Roumet, 2011), stress detection (Rumpf et al., 2010, Mahlein et al., 2010, Mahlein et al., 2013), species identification (Kumar, Skidmore, & Mutanga, 2010) and Phenotyping (Leucker et al., 2017, Wahabzada et al., 2016) have gained the interest of plant biologists and agronomist all over the globe, covering the need for a fast, non-destructive and visually interpretable technology (Fiorani et al., 2012, Rascher et al., 2011). Furthermore, HSI has been increasingly integrated with high-throughput phenotyping platforms (HTPPs) for controlled and automated assessment of plants (Busemeyer et al., 2013). HTPPs perform qualitative and quantitative determination of functional plant traits, such as plant growth and biomass yield, resulting from the interaction of the genetic characteristics of the plant with surrounding controlled environmental conditions (Furbank & Tester, 2011). These functional traits are reflected into different regions of the reflectance spectrum and can be estimated through HSI.
The HSI can be used to image the complete plants, as is required in a monitoring experiment, as well as to image plucked leaves from the plants to perform quantitative or qualitative analysis of interest. Individual plant pixels captured by HSI provide spectral information related to the chemical composition of the plant, i.e. its physiological status (Behmann, Mahlein, Rumpf, Römer, & Plümer, 2015). To understand the effects of artificial or natural environmental factors, the plant spectra can be explored and used for model development. For example, in the case of stress related studies, stress resulting from a drought induces degradation of the leaf chlorophyll content leading to an increase in reflectance over the whole VIS spectrum (400–700 nm). Similar information generated by HSI can be used for identification, quantification and spatial representation of nutrient deficiencies, diseases, and drought under controlled experiments in a greenhouse or under uncontrolled conditions in an agriculture field (Sankaran, Mishra, Ehsani, & Davis, 2010).
The aim of the paper is to provide the reader with an overview of the principles, instrumentation and data handling of close range HSI of plants, followed by a review of recent applications of plant assessment using HSI. Finally, the major challenges associated with the implementation of close range HSI for plants are discussed, and the paper is concluded with some future directions. All the applications reviewed in the paper were studies performed after the year 2000 and were selected from six different domains related to exploration of plants, i.e. foliar biochemistry estimation, leaf monitoring, species identification, stress detection, phenotyping and HTPPs.
Section snippets
Interaction of plants with light
The interaction of light (electromagnetic radiation (EMR)) with plants differs according to the light frequencies. Since the leaves are mainly responsible for the photosynthetic activity, the interaction of light with the leaves is of particular interest (Jacquemoud & Ustin, 2001). For green leaves, the relevant regions of EMR are the VIS region (400–700 nm), responsible for absorption of light by photosynthetic pigments; the NIR (700–1100 nm), dominated by absorption by dry matter; and the
Imaging sensor
A HSI setup consists four main parts: the light source, the objective lenses, the imaging spectrograph and the area detector. The selection of the light source is crucial to ensure good performance and reliability of any optical inspection system. Typically, halogen lamps are most widely used in HSI systems for indoor applications related to plants (Behmann et al., 2014b, Matsuda et al., 2012, Yu et al., 2014). Halogen lamps are broadband illumination sources covering the visible and NIR
Recent applications
Recent applications of HSI, ranging from simple biochemical content estimation to monitoring of plant growth under controlled environment (Phenotyping) are presented in Table 1. The major applications were related to foliar content estimation, disease detection, variety identification, leaves monitoring, stress related studies, phenotyping and adoption of HSI in HTPPs. These applications are further discussed in the following subsections.
Technical challenges
In the last decade, HSI is increasingly being applied for assessment of functional plant traits in close range settings. Nevertheless, as an emerging application, it faces some major challenges limiting its full potential. Many of the limitations are either related to the technical complexity of the imaging setup or the nature of the samples to be imaged.
To date, the most common imaging setup used for close range HSI acquisition is based on a push-broom movement of the sensor. In this
Conclusion
The ability of HSI to provide integrated spatial and spectral information offers a perfect combination of rapid non-destructive assessment of plant traits. In the VIS region of the electromagnetic spectrum, the spectral information is majorly dominated by pigments while in the NIR, the information is related to different biochemical constituents such as water, starch, protein etc. A wide range of emerging applications of HSI, on whole plants or on plucked leaves, such as foliar content
Acknowledgement
Puneet Mishra acknowledges the financial support from INDIA4EU2 project of Erasmus Mundus during his stay at Technical University of Madrid, Spain (http://www.india4eu.eu/). Mohd Shahrimie M. A. acknowledges the support of the Academic Staff Training Scheme (ASTS) of the Universiti Sains Malaysia and the Ministry of Higher Education of Malaysia.
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