The quest for understanding phenotypic variation via integrated approaches in the field environment

Abstract

Establishing the connection between genotype and phenotype is currently one of the most significant challenges facing modern plant biology. Although spectacular advances in next-generation DNA sequencing have allowed genomic data to become commonplace throughout biology, progress has been much slower in translating this discrete DNA base pair information into an accurate description of phenotypic variation. The limiting factor for quantifying phenotypes, particularly in the context of agricultural and native plant populations, has been the lack of phenotypic information of a scale, density, and accuracy comparable to DNA sequencing data. The extensive collection of phenotypic data for many physiological and developmental traits from many individuals remains onerous (Furbank and Tester, 2011). As a result, for large populations, there is often a focus on traits that are easy or inexpensive to measure, while more costly or difficult-to-score phenotypes are studied in only a few individuals. This is especially true for traits that are complex in nature, meaning that they have polygenic inheritance and varying responses to the environment. Complex traits are of primary interest not only because they represent the majority of important agronomic crop traits but also because they govern key biological processes that influence overall plant productivity and adaptability in plant populations (Lynch and Walsh, 1998). Success in deciphering these processes could translate into increased genetic gains in plant breeding, elucidation of the mechanisms impacting important ecophysiological traits, and improved crop management decisions to further maximize yield and quality. In light of these potential benefits, the aim of this Update article is to present the basic principles of phenomics, summarize the current state of field-based phenotyping, and highlight key challenges and limitations. In addition, the areas of data collection and management, environmental characterization, and crop growth models (CGMs) are presented as topics where further consideration is needed to capitalize on advancements in phenotyping technologies. Phenomics, or high-throughput phenotyping, which emerged in recent years in response to limited phenotyping capacity, is the use of sensor and imaging technologies that permits the rapid, low-cost measurement of many phenotypes across time and space with less labor; it can include laboratory, greenhouse, and field-based applications. In model plant species with small physical stature, such as Arabidopsis (Arabidopsis thaliana), large populations can be evaluated under controlled environmental conditions. However, the use of controlled environmental systems is not scalable for many areas of interest. Native species often need to be evaluated in their natural environment and over a broad geographic and climatic distribution, and agricultural crop trials must simultaneously evaluate thousands of potential cultivars. Furthermore, these controlled systems are unable to replicate the environmental variables of a field environment that influence complex traits such as grain yield or drought tolerance. Therefore, field-based, high-throughput phenotyping (FB-HTP) capacity is desperately needed to understand phenotypic variation relevant to a broad range of research areas such as food and nutritional security, anthropogenic effects on the environment, and ecological community interactions. Plants are intrinsically related to their environment, and observed phenotypes are a direct product of this interaction. Therefore, the ability to study and quantify phenotypes under real-world conditions is essential to the basic understanding, as well as improvement, of ecophysiological traits. Recent technological developments have enabled progress in plant phenotyping, but areas such as root phenotyping are still lacking the needed instrumentation in order to capitalize on these developments. Extraction of high-dimensional phenotype data from images is becoming more commonplace with advancements in image-processing software. This is leading to the discovery of novel phenotypes not identified previously but that are more related to underlying physiological processes. Developments in envirotyping and crop growth modeling can provide a useful framework for understanding plant development. The physical basis for most nondestructive, proximal sensing systems is the quantification of absorption, transmission, or reflectance characteristics of the electromagnetic radiation (EM) spectrum’s interaction with the plant canopy surface (Mulla, 2013; Araus and Cairns, 2014). The EM spectrum, specifically the wavelengths between 400 and 2,500 nm, can be broken down into three major parts that offer information about plant status, structural properties, and biochemical composition (Fig. 1). These three subregions are composed of (1) the photosynthetically active region (400–700 nm PAR), in which photosynthetic pigments, namely chlorophylls a and b, strongly absorb light; (2) the near-infrared region (700–1,400 nm), in which healthy plant tissue is highly reflective; and (3) the shortwave infrared region (1,400–2,500 nm), in which water and biomolecules contribute to reflectance characteristics (Jones and Vaughan, 2010; Homolová et al., 2013). In addition to these regions, thermal infrared, typically 8 to 13 μm when used for remote sensing, can provide information about canopy temperature (Jones, 2004). The variation present in these spectral traits give rise to ecological, species-, and genotype-specific phenotypes. Typical spectral reflectance curve for healthy vegetation. The unique spectral signature of vegetation in the wavelength range of 350 to 2,500 nm allows it to be differentiated from other types of land features. The shape of the reflectance spectrum is influenced by the chlorophyll content, health, water content, and biochemical composition of the vegetation (Curran, 1989; Jones and Vaughan, 2010), which then can be used to help identify the type of vegetation and diagnose its status. Thermal sensing of vegetation is valuable for the detection of drought stress and closure of stomata. There are five common types of sensors that are used to measure spectral variation, with differences among them in the specific targeted wavelengths. The inset image depicts the spectra underlying solar-induced chlorophyll fluorescence based on application of the Fraunhofer line discrimination principle using three spectral bands (FLD3). Measurements of chlorophyll fluorescence can be used to detect the early stages of biotic or abiotic stress before the appearance of visible symptoms. NIR, Near-infrared region. Robust sensors mounted on a field-deployable vehicle are imperative for FB-HTP. Although the aim of this review is not to summarize specific sensor technologies (for summary, see Jones and Vaughan, 2010; Sankaran et al., 2015), a brief list is provided for orientation. The most common types of canopy sensors include digital imaging via red-green-blue cameras; multispectral, including color-infrared modified digital cameras; hyperspectral; thermal; fluorescence; and three-dimensional (3D; time-of-flight and stereo cameras as well as light detection and ranging). The choice of vehicle for positioning sensors directly impacts the scale of research that can be as well as the sensor and (for review of and see et al., Sankaran et al., phenotyping large ecological and the only and et al., but small systems are a for areas et al., 2013). for field vehicle include and small in addition to 2010; et al., 2013; et al., et al., et al., reflectance can provide into overall plant as well as specific physiological processes. most and remote sensing have on using to overall plant (for see et al., et al., with vegetation the most well Although these can be they use less of spectra and lack the ability to give information on physiological processes. 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Plant phenotyping relevance

植物フェノミクスとフィールド高スループット表現型解析の原理、センサー・画像技術、データ処理、課題を中心に扱うレビューであり、方法論が主題。

Code and data availability

This is a review/update article on field-based high-throughput phenotyping. The supplied blocks contain no paper-specific phenotype datasets, images, analysis code, models, or supplements; all URLs mentioned (iNaturalist, mobile apps, MIAPPE site, Genomes to Fields, IPPN) are external community resources or cited prior

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