The quest for understanding phenotypic variation via integrated ...€¦ · 4 87 where further consideration is needed to capitalize on advancements in phenotyping 88 technologies

The quest for understanding phenotypic variation via integrated approaches 1 in the field environment 2 3 Duke Pauli1 Scott C Chapman2 Rebecca Bart3 Christopher N Topp3 Carolyn J Lawrence-4 Dill4 Jesse Poland5 and Michael A Gore1 5 6 1Plant Breeding and Genetics Section School of Integrative Plant Science Cornell University 7 Ithaca NY 14853 USA 8 2CSIRO Agriculture and Queensland Alliance for Agriculture and Food Innovation (QAAFI) 9 The University of Queensland St Lucia QLD 4067 Australia 10 3Donald Danforth Plant Science Center St Louis MO 63132 USA 11 4Department of Genetics Development and Cell Biology Iowa State University Ames IA 12 50011 USA and Department of Agronomy Iowa State University Ames IA 50011 USA 13 5Wheat Genetics Resource Center Department of Plant Pathology and Department of 14 Agronomy Kansas State University Manhattan KS 66506 USA 15 16 Corresponding author 17 Michael A Gore 18 310 Bradfield Hall Cornell University Ithaca NY 14853 19 Tel +1 (607) 255-5492 20 E-mail mag87cornelledu 21 22 E-mail addresses 23 DP wdp46cornelledu SCC ScottChapmancsiroau RB RBartdanforthcenterorg CNT 24 CToppdanforthcenterorg CJL-D triffidiastateedu JP jpolandksuedu MAG 25 mag87cornelledu 26 27 Journal research area 28 Updates 29 30 Running title 31 Phenotyping complex plant traits 32 33 Author contributions 34 DP and MAG conceived and managed writing of the article DP SCC RB CNT CJL-35 D JP and MAG wrote the article 36 37 Funding Information 38 This work was supported by Cotton Incorporated Fellowship (DP) and Core Project Funds 39 (MAG) Cornell University start-up funds (MAG) National Science Foundation IOS-1238187 40 (JAP and MAG) and the Iowa State University Plant Sciences Institute Faculty Scholars 41 program (CJL-D) 42

Plant Physiology Preview Published on August 1 2016 as DOI101104pp1600592

ADVANCEMENTS 43

Plants are intrinsically related to their environment and observed phenotypes are a direct product 44

of this interaction Therefore the ability to study and quantify phenotypes under real-world 45

conditions is essential to the basic understanding as well as improvement of ecophysiological 46

traits 47

Recent technological developments have enabled progress in plant phenotyping but areas such as 48

root phenotyping are still lacking the needed instrumentation in order to capitalize on these 49

developments 50

Extraction of high dimensional phenotype data from images is becoming more commonplace 51

with advancements in image processing software This is leading to the discovery of novel 52

phenotypes not previously identified but that are more related to underlying physiological 53

processes 54

Developments in envirotyping and crop growth modeling can provide a useful framework for 55

understanding plant development 56

Establishing the connection between genotype and phenotype is currently one of the most 65

significant challenges facing modern plant biology Although spectacular advances in ldquonext 66

generationrdquo DNA sequencing have allowed genomic data to become commonplace throughout 67

biology progress has been much slower in translating this discrete DNA base pair information 68

into an accurate decomposition of phenotypic variation The limiting factor for quantifying 69

phenotypes particularly in the context of agricultural and native plant populations has been the 70

lack of phenotypic information of a scale density and accuracy comparable to DNA sequencing 71

data The extensive collection of phenotypic data for many physiological and developmental 72

traits on many individuals remains onerous (Furbank and Tester 2011) As a result for large 73

populations there is often a focus on traits that are easy or inexpensive to measure while more 74

costly or difficult to score phenotypes are studied only on a few individuals This is especially 75

true for traits that are complex in nature meaning they have polygenic inheritance and varying 76

responses to the environment Complex traits are of primary interest not only because they 77

represent the majority of important agronomic crop traits but because they also govern key 78

biological processes that influence overall plant productivity and adaptability in plant 79

populations (Lynch and Walsh 1998) Success in deciphering these processes could translate 80

into increased genetic gains in plant breeding elucidation of the mechanisms impacting 81

important ecophysiological traits and improved crop management decisions to further maximize 82

yield and quality In light of these potential benefits the aim of this update article is to present 83

the basic principles of phenomics summarize the current state of field-based phenotyping and 84

highlight key challenges and limitations In addition the areas of data collection and 85

management environmental characterization and crop growth models are presented as topics 86

where further consideration is needed to capitalize on advancements in phenotyping 87

technologies 88

Phenomics or high-throughput phenotyping (HTP) which emerged in recent years in 89

response to limited phenotyping capacity is the use of sensor and imaging technologies that 90

permits the rapid low-cost measurement of many phenotypes across time and space with less 91

labor and can include laboratory greenhouse and field-based applications In model plant 92

species with small physical stature such as Arabidopsis large populations can be evaluated under 93

controlled environmental conditions However the use of controlled environmental systems is 94

not scalable for many areas of interest Native species often need to be evaluated in their natural 95

environment and over a broad geographic and climatic distribution and agricultural crop trials 96

must simultaneously evaluate thousands of potential cultivars Furthermore these controlled 97

systems are unable to replicate the environmental variables of a field environment that influence 98

complex traits such as grain yield or drought tolerance Therefore field-based high-throughput 99

phenotyping (FB-HTP) capacity is desperately needed to understand phenotypic variation 100

relevant to a broad range of research areas such as food and nutritional security anthropogenic 101

effects on the environment and ecological community interactions 102

The physical basis for most non-destructive proximal sensing systems is the 103

quantification of absorption transmission or reflectance characteristics of the electromagnetic 104

radiation (EM) spectrumrsquos interaction with the plant canopy surface (Mulla 2013 Araus and 105

Cairns 2014) The EM spectrum specifically the wavelengths between 400 and 2500 nm can 106

be decomposed into three major parts that offer information about plant status structural 107

properties and biochemical composition (Figure 1) These three sub-regions are composed of (a) 108

the photosynthetically active region (PAR 400-700 nm) in which photosynthetic pigments 109

namely chlorophyll a and b strongly absorb light (b) the near-infrared region (NIR 700-1400 110

nm) in which healthy plant tissue is highly reflective and (c) the shortwave infrared region 111

(SWIR 1400-2500 nm) in which water and biomolecules contribute to reflectance 112

characteristics (Jones and Vaughan 2010 Homolovaacute et al 2013) In addition to these regions 113

thermal infrared typically 8-13 micrometers when used for remote sensing can provide 114

information about canopy temperature (Jones 2004) The variation present in these spectral traits 115

give rise to ecological species and genotype specific phenotypes 116

Robust sensors mounted on a field deployable vehicle are imperative for FB-HTP 117

Although the aim of this review is not to summarize specific sensor technologies (please see 118

Jones and Vaughan (2010) and Sankaran et al (2015) for summary) a brief list is provided for 119

orientation The most common types of canopy sensors include digital imaging via red-green-120

blue (RGB) cameras multispectral including color-infrared modified digital cameras 121

hyperspectral thermal fluorescence and 3D (time-of-flight and stereo cameras as well as 122

LIDAR light detection and ranging) The choice of vehicle for positioning sensors directly 123

impacts the scale of research that can be conducted as well as the achievable sensor resolution 124

and associated costs (For a review of vehicles considerations and limitations see White et al 125

(2012) and Sankaran et al (2015)) For phenotyping large ecological systems satellites and 126

manned aircraft remain the only practical option (Asner et al 2012 Kerr and Ostrovsky 2003) 127

but small unmanned aircraft systems (UASs) are now a viable alternative for smaller 128

geographically distinct areas (Kim et al 2013) Finally for small scale experimental field 129

studies several vehicle options include high-clearance tractors cable gantries cranes linear 130

move irrigation systems and small UASs in addition to stationary observation platforms 131

(Andrade-Sanchez et al 2013 Haberland 2010 Chapman et al 2014 Liebisch et al 2015 132

Busemeyer et al 2013a) 133

PLANT CANOPY PHENOTYPING 135

Canopy reflectance can provide insight into overall plant health as well as specific physiological 136

processes To date most FB-HTP and remote sensing applications have focused on using 137

vegetative indices to infer overall plant status (for a comprehensive review please see Bannari et 138

al (1995) Govender et al (2009)) with normalized difference vegetation index (NDVI Tucker 139

(1979)) being the most well-known Although these indices can be generally informative they 140

use less than 1 of available spectra and lack the ability to give detailed information on 141

physiological processes In contrast canopy spectroscopy using either multi or hyperspectral 142

sensors can assess canopy chemistry and composition by capturing more of the spectra Several 143

research groups have demonstrated that hyperspectral data (400-2500 nm) can be utilized for 144

non-destructively inferring leaf chemical properties in various species (Asner et al 2011 Ustin 145

et al 2009 Asner et al 2015) with specific applications including estimation of canopy 146

nitrogen (Martin et al 2008) and lignin content (Wessman et al 1988) Spectral signatures 147

arising from these various leaf biochemical compounds can further give insight into community 148

level phenotypes including species diversity ecosystem composition and threats to native plants 149

by spread of invasive species (Turner et al 2003 Bradley 2014) 150

However spectral signatures of canopies are jointly influenced by environmental factors 151

and the biochemical composition of the plant (Curran 1989 Gates et al 1965) making it 152

difficult to understand how different wavelengths across the measured spectrum translate into 153

biological meaning and function In particular the complex interaction of light with the canopy 154

surface and the associated effects of light scatter pose a significant challenge Attempts have 155

been made to handle this heterogeneity with the seminal SAIL (Scattering by Arbitrarily Inclined 156

Leaves) and PROSPECT (Leaf Optical Properties Spectra) models but each of these canopy 157

reflectance models oversimplify the structural characteristics of leaves into a single parameter 158

(Jacquemoud and Baret 1990 Verhoef 1984) An additional layer of complexity is the 159

corresponding geometry and position of the sensor itself which can introduce systematic error 160

into the analysis of the spectral data and therefore must be properly accounted for in the model 161

(Lobell et al 2002) Despite these limitations the use of canopy reflectance data at large scales 162

provided by satellite and manned aerial systems has proven valuable However far higher spatial 163

resolution is needed for the study of phenotypes at the experimental plot level 164

With recent technological developments our ability to use smaller more portable sensors 165

in combination with vehicles whether ground or aerially based permits the collection of canopy 166

data with higher spatial resolution In addition most developed FB-HTP platforms incorporate 167

sets of multiple sensors creating complementary streams of data which when combined provide 168

more information than what individual sensors alone can achieve (termed ldquosensor fusionrdquo) In 169

one of the first demonstrations of FB-HTP Montes et al (2011) developed a platform carrying 170

light curtains (measure of canopy height) and spectral reflectance sensors (canopy reflectance) to 171

predict above-ground biomass accumulation in maize Their results showed that the combination 172

of data from both sensors was more predictive than data from either sensor alone Such 173

combined sensor data was successfully used by Busemeyer et al (2013b) to phenotype triticale 174

for harvestable biomass at multiple developmental stages More recently Deery et al (2014) 175

developed an advanced platform combining numerous high precision sensors that are capable of 176

capturing details of plant physical structure such as canopy leaf angle and can produce 3D 177

surface reconstructed images of plants 178

FB-HTP systems have been used to study the temporal nature of trait development and its 179

interaction with environmental effects Historically canopy temperature has been used to assess 180

water status of plants as plants with available soil moisture exhibit decreased leaf surface 181

temperatures relative to atmospheric conditions (Ehrler 1973 Jackson et al 1981) Recently 182

Pauli et al (2016) demonstrated the use of FB-HTP to study the physiological processes 183

underlying heat and drought response in an upland cotton population grown under contrasting 184

irrigation regimes They were able to assess the dynamic nature of multiple canopy traits in 185

response to rapidly changing field conditions within a day and over the growing season The 186

longitudinal collection of phenotypic data enabled the detection of quantitative trait loci (QTL) 187

with temporal expression patterns coinciding with plant growth stages (Figure 2) Further 188

supporting the ability of FB-HTP to monitor plant development under managed stress Sharma 189

and Ritchie (2015) used a similar system to identify differential growth characteristics among 190

cotton cultivars evaluated under varying irrigation levels The novel ability of FB-HTP to capture 191

the temporal changes of a phenotype provides the critical pivot needed to focus on 192

developmental quantitative genetics in order to grasp how trait manifestation is a function of 193

many QTL whose effects fluctuate with time (Wu et al 1999) Because the expression dynamics 194

of individual QTL vary and are further modulated by phenological stage as well as their 195

interaction with the environment it is not possible to capture this dynamism with single time 196

point analyses Therefore recognizing the individual behavior of QTL may permit genotype 197

optimization with respect to target environments or in identifying how ecosystem disturbances 198

impact the genetics of natural populations 199

Although its application in FB-HTP is still in its infancy fluorescence imaging which 200

quantifies the light re-emitted from chlorophyll a molecules via chlorophyll fluorescence can 201

provide information about the photosynthetic apparatus under various abiotic and biotic stresses 202

(Maxwell and Johnson 2000) Furthermore changes in fluorescence readings often precede the 203

manifestation of any visible symptoms (Lichtenthaler and Mieheacute 1997) making it useful for 204

early detection of abiotic and biotic stress (Massacci et al 2008 Jedmowski and Bruumlggemann 205

2015 Ni et al 2015) The use of solar-induced chlorophyll fluorescence based on the 206

Fraunhofer Line Discrimination principle (Plascyk 1975 Meroni et al 2009) has recently been 207

implemented successfully using UAS-based hyperspectral imagery (Zarco-Tejada et al 2013) 208

for the early detection of Verticillium wilt in a production olive orchard (Calderoacuten et al 2013) 209

As an extension of this technology to ecological studies the relationship of chlorophyll 210

fluorescence to gross primary production has enabled the assessment of temporal changes in 211

ecosystem health and productivity in response to climatic change (Damm et al 2015 Yang et 212

al 2015) Although fluorescence imaging is promising it still suffers from the same limitations 213

as other spectral imaging techniques including inconsistent or uneven illumination wind 214

disturbances under field conditions and being unable to discriminate the underlying cause of the 215

stress 216

PLANT DISEASE AND PEST PHENOTYPING 218

The plant canopyrsquos role as a vital above ground interface with the environment makes identifying 219

and quantifying disturbances due to biotic factors a critical aspect of FB-HTP The outcomes of 220

early and accurate detection of plant diseases and pests in the field has a global economic impact 221

The presence of many biotic diseases including those caused by viruses bacteria fungi 222

oomycetes and insect pests can be difficult to detect until disease progression is advanced 223

pathogen load is high and symptoms are abundant At this late stage disease is extremely 224

difficult to control (Fry 1982) Methods for the direct and indirect assessment of pathogen 225

presence in the field have been recently reviewed (Mahlein et al 2012 Mutka and Bart 2015 226

Fang and Ramasamy 2015) and these methods are widely employed (Ward et al 2004 Lievens 227

and Thomma 2005 Mirmajlessi et al 2015) The early detection of plant diseases prior to their 228

spread remains the larger challenge Various techniques such as fluorescence visible and 229

infrared spectroscopy and hyperspectral imaging have been tested for their ability to detect 230

biotic stress with differing levels of success (Sankaran et al 2010 Mutka and Bart 2015) 231

Although assessment of pathogen presence remains the detection method with the highest 232

sensitivity and specificity a robust method for indirect detection of biotic stress in general would 233

provide a complementary early warning system 234

The task of biotic stress identification is conceptually simple identify the plant 235

phenotypic signatures that are specifically displayed during response to biotic stress However 236

symptoms of abiotic and biotic stresses phenotypically overlap thus complicating these analyses 237

Altered water content thermal characteristics and changes in RGB profiles are among the major 238

phenotypic alterations elicited by both biotic and abiotic stress For example vascular bacterial 239

and fungal pathogens can obstruct water transport within the plant leading to symptoms 240

phenotypically similar to drought stress (Yadeta and Thomma 2014) Efforts to identify 241

phenotypic indicators of biotic stress have approached this challenge by using FB-HTP in 242

conjunction with traditional field surveys to locate diseased areas In addition to on ground 243

phenomic methods it may be possible to use data collected from satellites to assess plant health 244

on a larger ecological scale (Zhang et al 2014) These experiments are challenging because of 245

the complexity and unpredictability of field systems but the technology holds great promise for 246

the identification and management of both current and emerging plant diseases 247

Since biotic and abiotic stress induce distinct early events that lead to phenotypic 248

changes research aimed at developing methods of distinguishing the differences between biotic 249

and abiotic stress responses has high potential for success Consequently many researchers are 250

conducting experiments under controlled conditions with known inoculum and an ability to 251

apply additional abiotic andor multiplexed biotic stresses in predictable ways As an example 252

fluorescence spectroscopy was used to distinguish citrus plants experiencing mechanical injury 253

drought variegated chlorosis or bacterial infection (Belasque Jr et al 2008 Bock et al 2008 254

Lins et al 2006 Marcassa et al 2006) Granum et al (2015) investigated the ability to detect 255

early stages of infection of avocado by the fungus Rosellinia necatrix using fluorescence and 256

thermal imaging Additional research within controlled environments to understand these 257

complex interactions and their phenotypic outputs with particular focus on in planta changes 258

that occur during early stages of infection can be expected to eventually translate to in-field 259

early detection systems for plant diseases 260

PLANT ROOT PHENOTYPING 262

High-quality and high-throughput root phenotyping in the field has long been a valuable but 263

elusive target for plant scientists In many ways the trench and monolith excavation methods 264

developed in the early 1900rsquos (eg Weaver (1926)) are still state-of-the art although they are 265

now assisted by modern tools especially digital images (Figure 3) High-throughput soil coring 266

(Wasson et al 2014) root crown excavation (Das et al 2015) and minirhizotron analysis 267

(Maeght et al 2013) are common ways in which roots are studied in the field but each provides 268

limited information relative to the actual root structure Thus there remains a lack of a coherent 269

view of root phenotypes and their genetic and environmental conditioning (Topp et al 2016) 270

An ideal technology would allow explicit in situ visualization of root architecture as it develops 271

through time and likely involve sensors that were either above ground or located belowground 272

with minimum invasiveness The fundamental problem is that roots are largely comprised of 273

water and organic matter which are usually abundant in the soil Furthermore most soils have 274

structural inhomogeneities and other properties such as water content and temperature change 275

over time These factors create significant difficulties for identifying consistent patterns in digital 276

data that can separate root and soil 277

While none of these challenges for root phenotyping will likely be solved in the near 278

term there have been promising advances in sensor development image analysis and signal 279

processing In particular the application of geophysical survey methods that use magnetic and 280

electrical field information for non-destructive analysis of soil features have some promise for 281

root phenotyping Ground penetrating radar (GPR) uses small radio waves (typically 282

microwaves) to discriminate objects based on differences in their electrical permittivity 283

(reviewed in Guo et al (2013)) An antenna is used to transmit and receive EM pulses into the 284

ground which interact with sub-surface objects in ways characteristic to their electrical 285

properties By moving the array across the ground in proscribed transects a reflectance profile of 286

the subsurface called a radargram can be made (Zenone et al 2008) Roots can generate a 287

characteristic hyperbolic pattern which can be used to generate 2D ldquoimagesrdquo or even 3D models 288

of root systems (Zhu et al 2014) 289

There are several limitations to GPR becoming an effective tool for field root 290

phenotyping especially for fine roots (Hirano et al 2009) given the fundamental tradeoff 291

between penetration ability and object resolution Longer wavelengths can penetrate further but 292

can resolve less detail and vice-versa Furthermore electrically conductive materials that have 293

high reflectance to radar such as wet-clay soils greatly reduce root discrimination Soil 294

temperature and amount of non-living organic material are also known to influence GPR signal 295

and therefore extensive calibration must be undertaken especially for non-uniform soils Even in 296

ideal conditions (relatively dry sandy soils) careful comparisons with excavated root systems 297

have not shown strong correlations especially for roots growing downward (Zenone et al 2008 298

Guo et al 2015a) To date GPR has not been shown to be capable of detecting fine (appx lt2 299

mm) roots which may comprise the majority of root length for many plants (Brown et al 2009 300

Pierret et al 2005) GPR may currently be useful as a rough estimate of biomass and root 301

distribution in some scenarios but concern should be taken that many important contexts in 302

which we wish to understand root biology (eg water content root angle variation and 303

interactions with soil structure) also affect our observation ability 304

Electrical resistivity tomography (ERT) uses differential electrical properties of materials 305

to map aspects of the soil ERT has been used to monitor soil water content in a maize field (Beff 306

et al 2013) as well as tree root biomass distribution (Amato et al 2008) but has limited 307

resolution that requires substantial embedded infrastructure and cannot be easily scaled to entire 308

fields Nonetheless ERT has shown some potential to improve signal processing for GPR by 309

mapping background soil properties at sites of interest (Zenone et al 2008) Electrical 310

capacitance has also been exploited to estimate root biomass in plants (reviewed in Dietrich et al 311

(2013) and even for QTL identification in maize (Messmer et al 2011) however these 312

relationships are only sometimes consistent (Dietrich et al 2013) The concept of mapping soil 313

qualities rather than root structure per se (Hartemink and Minasny 2014 Werban et al 2013) 314

provides a potentially powerful ally to direct root detection methods although many fundamental 315

connections between the two have yet to be established As machine learning and other 316

computationally-intensive statistical methods improve so will our ability to accurately classify 317

and quantify biological structures and processes from highly multivariate and noisy data 318

(Aanensen et al 2014 Singh et al 2012 Smith et al 2015) 319

CHALLENGES OF FB-HTP 321

Based on the limited number of success stories presented herein it becomes apparent that FB-322

HTP (and phenomics in general) remains in its infancy with numerous challenges facing it 323

Perhaps the most restrictive factor to widespread implementation of FB-HTP is the lack of any 324

commercially available platforms that offer a ldquoturn-keyrdquo solution to address a plethora of 325

biological questions As the examples referenced within highlight most FB-HTP platforms are 326

constructed by cross-disciplinary research groups consisting of agricultural engineers plant 327

breeders and computer scientists which demonstrates the amount of expertise required to 328

construct even simple platforms which are ldquoone-offrdquo solutions These required resources 329

including large budgets and specialized personnel are generally not available to most researchers 330

thereby putting the needed tools of FB-HTP out of reach for many Additionally these purpose-331

developed platforms are specific to the research question being addressed and therefore they are 332

not commonly transferable to other crops field designs or research settings This poses a 333

significant limitation as it prevents the extensive testing of these types of tools which is desired 334

and needed by the larger community 335

Looking forward it is challenging to predict which type of platform will be best able to 336

serve the needs of the research community Any platform that is to be successful must be easy to 337

use and deploy in the field low-cost utilize robust sensors have a sizable user community and 338

have acceptable technical performance under field conditions The best way to meet these criteria 339

is to develop tools for FB-HTP that are open-source so that the largest number of researchers are 340

able to contribute to the design development and testing of these needed resources Currently 341

UASs embody some of these features because of the extensive research and design invested in 342

them due to their pervasive application outside of basic research but they still have a major 343

limitation in that they are only able to phenotype plant canopies Because of this limitation and 344

others there is still a strong need for ground-based vehicles which can capitalize on their 345

proximity to plants to increase data resolution as well as capture finer scale phenotypes and 346

permit the transition beyond canopy level phenotypes Although the future hardware demands of 347

FB-HTP remain unclear the required tools and resources will need to become universal and the 348

most realistic way to achieve that is through low-cost open-source technology 349

Technical limitations aside the question remains of what value is the vast amount of data 350

that are being generated and how biological meaning will be extracted from these data to answer 351

important questions Such considerations become especially relevant with regard to the large 352

amount of image data being generated (and video in the near future) Although imaging 353

technologies are relatively new to plant biology they have been transformative in other fields 354

including medical and geochemical and have led to substantial new understandings and 355

capabilities This is largely because imaging and sensor data allow the capture of multivariate 356

information which may not be perceived by humans but is nonetheless essential for 357

understanding plant biology This can be exemplified by Chen et al (2014) who used high-358

throughput image analysis to gain insight into drought stress physiology Using an open-source 359

image analysis pipeline the authors were able to extract nearly 400 phenotypic traits from 360

images but more importantly they were able to derive new traits of physiological importance 361

that captured growth dynamics as well as stress responsive traits something not possible with 362

low-throughput phenotyping Examples of this type highlight the potential for high dimensional 363

data to reveal new phenotypes that serve to further the basic understanding of plant biology 364

However as data of these nature are utilized a healthy balance must be struck between 365

validating phenotypic data from FB-HTP with state-of-the-art conventional measurements and 366

also exploring how novel data-derived traits can be of value for uncovering biological 367

phenomena 368

There are still many issues that stand as hurdles to FB-HTP becoming an accepted 369

technology in the plant science field Primary among these challenges are the development of 370

resources to carry out this work and how to leverage this information for insight into plant 371

biology With these considerations in mind three areas important to the success of phenomics 372

and FB-HTP in particular are data management and documentation characterization of the 373

environment in which plant phenotypes arise and most importantly application of these data for 374

understanding the mechanisms governing plant growth and development 375

PLANT PHENOTYPE DATA CAPTURE MANAGEMENT AND UTILIZATION 377

A promising development in FB-HTP is the ability to accurately capture and record phenotype 378

data from field-grown plants using smartphones and tablets The scope of phenotyping with 379

mobile apps can be broadly classified into on-site experiments conducted by scientists and 380

survey-level or crowd-sourced information to gather information on larger ecological or 381

agriculture landscapes The data types computational requirements and image processing 382

challenges for these two purposes are largely similar with minor distinctions in user interface 383

data structure and data organization Plant breeder-led trials generally consist of thousands of 384

distinct entries grown in a common location while farmer surveys or surveys of natural 385

populations target hundreds of locations with limited data collection at each site In order to 386

facilitate data collection and storage a number of software apps have been developed ranging 387

from research specific tools to applications for farm- and landscape-level surveys Field Book is 388

an example of an application developed to specifically meet the requirements of plant breeding 389

programs where detailed data collection is necessary (Rife and Poland 2014) In contrast 390

iNaturalist is an application developed to capitalize on crowdsourcing and is used to catalog 391

biodiversity identify endangered species and monitor invasive species (iNaturalist) For FB-392

HTP improved data management and electronic data capture constitutes a critical foundation 393

and numerous apps have been developed to meet this need (IBreedIT Ltd (Daventi 2011) 394

Phenome Networks 2013 PhenoType 2014 Diversity Arrays Technology Pty Ltd 2015) 395

There is also a growing number of web apps to curate and link data gathered in the field and 396

therefore increase the utility and value of collected data (Aanensen et al 2014) 397

In order to be useful these apps rely on data collection using standard methods 398

protocols and formatting This is due to the fact that regardless of how they are collected 399

phenotype data are inherently complex given that they can range from populations to individual 400

plants to specific tissues and can be recorded on comparative discrete or continuous scales 401

Because the data are essentially infinite in both diversity and scale data documentation 402

integration representation and accessibility are critical aspects that present significant 403

challenges (reviewed in Deans et al (2015)) In order to be broadly useful large-scale high-404

dimensional data sets must be represented in such a way that data can be easily aggregated and 405

extracted to address biologically questions In addition data collection methods must be reported 406

at a level to enable experimental reproducibility Central to these issues is proper reporting of 407

experimental metadata ndash the information that describes experimental design sampling methods 408

environmental conditions instrumentation and analysis (Krajewski et al 2015) To determine 409

which types of metadata should be reported for a given experiment to make it both discoverable 410

and re-usable MIAPPE standards (Minimal Information About a Plant Phenotyping Experiment 411

httpcropnetplphenotypes Krajewski et al (2015)) should be followed and methods used to 412

measure phenotypes should be detailed at a level that enables reproducibility (see Sandve et al 413

(2013)) For example the maize Genomes to Fields initiative has collected phenotype data and 414

has released detailed methods that not only outline how to collect maize phenotypic data under 415

field conditions but also the requisite metadata to enable discovery aggregation analysis and re-416

use (available at httpwwwgenomes2fieldsorg see ldquoAboutrdquo and ldquoProject Overview and 417

Scoperdquo) This information includes detailed descriptions of what traits were measured (plant 418

height ear height stand count etc) units of measurement and protocols for how to measure 419

traits including when the data should be collected so that all of the projectrsquos phenotypic data can 420

be aggregated regardless of where and by whom they were collected 421

To avoid confusion due to simple naming issues and maximize the potential to integrate 422

data across experiments environments even species the use of well-established nomenclatures 423

controlled vocabularies and ontologies for both metadata documentation and phenotypic 424

descriptions is needed Further impetus to report data using these community standards lies in the 425

fact that many online data repositories rely on them for data representation However while 426

mechanisms to report experimental design metadata based on shared vocabularies is 427

comparatively mature methods to accurately describe phenotypic descriptions are just now 428

emerging Oellrich et al (2015) report a proof of concept wherein plant phenotypes for six 429

species were curated as lsquoentity qualityrsquo (EQ) statements to enable analysis by semantic 430

reasoning For example dwarf plants are generally short (due to reduced internode length) with 431

leaves that are both reduced in length and increased in width Using EQ statements the first of 432

these three aspects of the dwarf phenotype would be reported as ldquointernode length reducedrdquo 433

Their analyses of the resulting data demonstrated that once plant phenotype data were 434

represented as ontology-based EQ statements semantic reasoning could be used to discover 435

shared biology and increase analytical power 436

In summary proper metadata documentation accompanying FB-HTP data is critical for 437

data discovery analysis and reuse To enable cooperative plant phenotyping activities the 438

International Plant Phenotyping Network (IPPN httpwwwplant-phenotypingorg) was 439

recently formed The IPPN provides leadership as the foremost body coordinating multi-national 440

efforts for plant phenotyping across many species and represents numerous stakeholders from all 441

sectors of the research community One goal of this organization is to develop and disseminate 442

standards that aid in the distribution and sharing of phenotypic data The IPPN represents a 443

useful starting place to get a foothold on the status of existing plant phenotyping efforts 444

standards and data sets 445

ENVIROTYPING 447

Although it is well known that plants grow in heterogeneous environments analytical 448

methodologies tend to over simplify the contribution of the environment to the phenotype This 449

weakens our understanding of how ecosystems or even individual plants are responding to 450

changes in the environment Furthermore the long term effects of environmental conditions 451

such as temperature and water availability on phenological development are significant 452

environmental conditions greatly influence the initiation and duration of vegetative 453

reproductive and maturation stages of plants (Mickelbart et al 2015 Bahuguna and Jagadish 454

2015) Due to the interrelatedness of physiological traits plant developmental phases and 455

growth conditions there is a need to gain a better understanding of the complexities of genotype 456

by environment (GtimesE) interactions An additional component contributing to these complex 457

interactions are the management practices (GtimesEtimesM genotype by environment by management 458

interaction) employed in both agricultural and natural system settings that can have significant 459

large effects on how plants interact with their environment To this end envirotyping which is 460

the detailed characterization of the environment in which plants are growing (Cooper et al 461

2014) needs to be employed 462

The characterization of atmospheric conditions at local levels has become relatively 463

commonplace with the advent of portable weather stations and interpolation methods using data 464

from regional weather stations However the ability to quantify the soil environment including 465

parameters like pH organic matter and moisture content at high spatial resolution remains 466

challenging given the heterogeneity of soils Grid soil sampling and electromagnetic soil 467

mapping which measure soil conductivity to estimate properties like salinity water content and 468

organic matter have proven useful in characterizing spatial soil variability (Guo et al 2015b 469

Mallarino and Wittry 2004 Brevik et al 2006 Sun et al 2013) These methods are not without 470

their limitations though - both grid soil sampling and electromagnetic mapping are typically 471

performed once per season making it impossible to capture the dynamic nature of the soil-water 472

profile In addition these methods are too invasive for implementation in most natural systems 473

To adequately characterize environmental conditions throughout the course of a season a 474

network of affordable open-source sensors is the best option for field-level environmental data 475

collection Bitella et al (2014) designed a novel low-cost hardware platform that incorporates 476

sensors for recording air temperature and relative humidity as well as soil temperature and 477

moisture content at multiple depths Given the low cost of these sensors they could be spaced at 478

close regular intervals throughout the field providing a high resolution sampling grid To further 479

enhance the capability of stationary sensors the incorporation of wireless networks connected to 480

a central computer (node) could be implemented to synchronize data collection in real-time 481

(Fukatsu and Hirafuji 2005 Hirafuji et al 2004) By incorporating photovoltaic powered 482

sensors and networks envirotyping could be also carried out in remote locations for longer 483

periods of time with minimal disruption to the natural habitat 484

CROP AND PLANT MODELING 486

Since their inception in the 1960s crop growth models (CGMs) have developed into useful tools 487

for assessing the adaptation of cropping systems informing management choices and guiding 488

policy decisions Typically models are run using either historical or predicted weather data 489

(based on unrelated climate models) and are then used to determine lsquooptimalrsquo solutions of input 490

parameters such as planting date or fertilizer application Within the last 20 years models have 491

been increasingly used in crop adaptation and breeding but this requires better modeling of 492

physiological processes (see Holzworth et al (2014) for a summary) In the context of breeding 493

models have been used for two purposes first to lsquoextendrsquo envirotyping beyond the analysis of 494

environment variables per se (ie using the model as an integrator that characterizes the growth 495

environment) and secondly to extend the phenotyping per se at the level of the plot or plant 496

sometimes described as lsquomodel-assisted phenotypingrsquo (MAP) (Luquet et al 2006 Rebolledo et 497

al 2015) A newly sought outcome from applications of CGMs is to combine their outputs of 498

development and growth variables with knowledge from genomics and understanding of genetic 499

architecture in order to predict the trait combinations and eventually the allele combinations 500

that provide improved solutions in breeding (Technow et al 2015) 501

Envirotyping is the generation of environment proxies such as drought indices that then 502

can be utilized in statistical analyses of phenotypes collected in one or more trials on a set of 503

genotypes (Chapman et al 2000 Chenu et al 2011 Cooper et al 2014) For this purpose the 504

models need to be capable of simulating the performance of one or more lsquocheckrsquo genotype(s) 505

with the inputs typically being daily weather data and an accurate lsquotrial-levelrsquo soil 506

characterization The demands on model precision are relatively lower for envirotyping than for 507

other uses ie reasonable prediction of phenology and biomass productionwater use demand 508

can be sufficient to estimate drought patterns (Chapman 2008) or accumulated stresses due to 509

heat (Loumlffler et al 2005 Zheng et al 2015) Similar types of applications of models are made in 510

ecological research to estimate the incidence and intensity of favorable or stressful growing 511

conditions again with a similar need for weather and soil data (Haxeltine and Prentice 1996) 512

While soil and management are perhaps the major spatial variables to consider in agricultural 513

situations spatial modeling of the environment in ecosystems is typically more challenging due 514

to changes in topography species distribution and multi-species canopy all of which effect the 515

resulting micro-climate and the specific influence on plant adaptation 516

Model-assisted phenotyping can be taken to refer to multiple roles of models in relation 517

to plant phenotyping These roles include (1) use of models to decide which traits to measure 518

(2) calculation of lsquoextended phenotypesrsquo using inputs of plant phenotyping (3) estimation of 519

plant growth parameters from integration of multiple data sets from plant phenotyping and (4) 520

feedback from plant phenotyping to improve the physiological basis of models (Figure 4) Until 521

now the most common use of models to assist phenotyping has likely been in association with 522

the first role Many examples have been associated with optimizing the phenology especially the 523

timing of reproductive stages with respect to the historic or predicted future occurrence of 524

different types of stresses (Zheng et al 2015) Most existing models are easily suited to this 525

purpose as long as their estimation of phenology and the damage functions for the stresses are 526

reasonably precise 527

The most desired use of CGMs has been to propose ideotypes of trait combinations that 528

would result in lsquooptimalrsquo adaptation for given combinations of GtimesEtimesM in current or future 529

climate scenarios (Hammer et al 2014 Asseng et al 2015) While it may be that the models 530

used have been well-tested for a range of environments andor genotypes these models are far 531

more convincing and useful in plant phenotyping and breeding if the modelling has been 532

augmented by physiological and genotypic experimentation For example Singh et al (2012) 533

demonstrated that incorporation of QTL effects partially responsible for sorghum root 534

architecture into a CGM showed that narrow root angle would generally improve yield via 535

increased access to water The final and most convincing part of this work was that one of these 536

major QTL was mapped across breeding populations and found to be highly associated with 537

higher yields in trials over multiple genetic backgrounds years and locations This 538

comprehensive approach combines experimental data physiological understanding of a trait 539

from multiple genotypes model simulation and breeding trial validation to determine what traits 540

to measure (Hammer et al 2016) The current cutting edge of phenomicsCGM application in 541

breeding is best described by the CGM-WGP (Crop Growth Model ndash Whole Genome Prediction) 542

approach of Cooper et al (2016) who show how a relatively simple CGM can be lsquoinsertedrsquo 543

between genomic and phenomic data to lsquoimposersquo biological pathway constraints to the statistical 544

prediction models 545

CONCLUSIONS 547

Progress has been made in FB-HTP and more generally phenomics however significant 548

barriers remain preventing its widespread implementation and utilization (see Outstanding 549

Questions) Though early results seem to suggest that FB-HTP will be a valuable technology for 550

the plant sciences questions remain of whether or not the investment in FB-HTP is practical and 551

justified on a larger scale Contemplating the future it is clear that open-source low-cost 552

hardware solutions need to be developed so that more researchers have access to the tools and 553

therefore can test and evaluate them within the context of their respective research programs 554

only this will determine if expenditure on these resources was warranted Parallel to hardware 555

improvement increased research into data management and documentation needs to be 556

implemented so that accurate comparisons can be carried out between different FB-HTP systems 557

further assisting in the maturation of this nascent technology As with all new technologies the 558

initial development phase is often fraught with setbacks challenges and vigorous debate but 559

these are all healthy signs of a technology worth pursuing 560

Advancements in all areas of plant science are beginning to bring field-based research 561

into a new era an era where genomics envirotyping and phenomics coalesce to unravel the 562

complexities of plant biology Continued progress in artificial intelligence coupled with 563

autonomous vehicles will permit the generation of data sets of unprecedented dimensionality as 564

robots begin to collect data nearly around the clock These advancements in combination with 565

aerial phenotyping a rapidly maturing technology and exhaustive envirotyping will create a 566

fully integrated field site whereby trait development and expression can be evaluated in the 567

context of dynamic environmental conditions By achieving this goal the transition to studying 568

developmental quantitative genetics becomes possible thereby allowing the interpretation of 569

genetic effects in relation to the environment and life cycle in a way not previously possible 570

Building on this framework CGMs could incorporate these components into their machinery 571

helping to further the understanding of plant development and its relationship with the 572

environment which will permit a deeper knowledge of the complexities of trait development in 573

response to changing climatic conditions Essential to this will be the continued work to integrate 574

CGMs with whole-genome prediction methods to enable robust prediction of phenotypes in a 575

number of environments including those in which genotypes have not been previously tested 576

This should not only assist in the breeding of superior stress resilient cultivars but also enable 577

the identification of those natural populations that could be at risk from climatic variation and 578

their interaction with other anthropomorphic disturbances 579

ACKNOWLEDGEMENTS 581

The authors wish to thank Daniel Ilut Elodie Gazave Christine Diepenbrock and Maryn 582

Carlson for their expert comments on the manuscript The authors would like to especially thank 583

Trevor Rife for his contributions on mobile field apps We apologize for not citing original work 584

of many colleagues because of space constraints 585

OUTSTANDING QUESTIONS 587

bull There is a need to evaluate whether the data generated by high-throughput phenotyping is 588

capturing true biological signal and if that signal is worth the investment of resources 589

Will it provide better information or deeper knowledge 590

bull Will latent phenotypes be more useful than those defined by humans 591

bull Developmental quantitative genetics needs to become the focus of future field-based 592

research so that geneQTL expression can be understood within the context of both 593

environmental conditions and phenological stages 594

bull Incorporation of crop growth models and envirotyping with high-throughput phenotyping 595

will be crucial to understanding the dynamic interaction between plants and their 596

environment This will help elucidate the physiological mechanisms responsible for 597

observed phenotypes as well as improve the prediction of phenotypes 598

FIGURE LEGENDS 601

Figure 1 Typical spectral reflectance curve for healthy vegetation The unique spectral signature 602

of vegetation in the wavelength range of 350-2500 nm allows it to be differentiated from other 603

types of land features The shape of the reflectance spectrum is influenced by the chlorophyll 604

content health water content and biochemical composition of the vegetation (information from 605

Curran (1989) and Jones and Vaughan (2010)) which can then be used to help identify the type 606

of vegetation and diagnose its status Thermal sensing of vegetation is valuable for the detection 607

of drought stress and closure of stomata There are five common types of sensors that are used to 608

measure spectral variation with differences among them in the specific targeted wavelengths 609

The inset image depicts the spectra underlying solar-induced chlorophyll fluorescence based on 610

application of the Fraunhofer Line Discrimination (FLD) principle using three spectral bands 611

(FLD3) Measurements of chlorophyll fluorescence can be used to detect the early stages of 612

biotic or abiotic stress before the appearance of visible symptoms 613

Figure 2 Relationship of plant growth stages environmental patterns and quantitative trait loci 615

(QTL) expression Field-based high-throughput phenotyping (FB-HTP) facilitates the study of 616

plants at distinct developmental stages or continuously throughout the season thus allowing the 617

logistic growth pattern of plants to be modeled As a result the genetic mapping of QTL can be 618

performed with phenotype data obtained by successive measurement of a trait such as height 619

throughout plant development thereby enabling the expression dynamics of identified QTL to be 620

monitored in the context of changing environmental conditions 621

Figure 3 Adding another dimension to the analysis of field excavated roots Three-dimensional 623

(3D) reconstructions of a maize root crown that was excavated washed and imaged by X-ray 624

computed tomography (XRT) The scan took less than 5 minutes (A) A segmented volume from 625

which root architectural features can be explicitly measured in 3D space such as total root 626

length angles tortuosity branching topology etc (B) A two-dimensional slice of the raw XRT 627

volume showing individual branches from a whorl of nodal roots and information internal to the 628

stem 629

Figure 4 Schematic illustrating the integration of several data types for modeling phenotypes 631

The flow diagram shows the process in which genotype environment (including management 632

practices) and their interaction produce the phenotype and how field-based high-throughput 633

phenotyping (FB-HTP) can be integrated with envirotyping genomic models and crop models 634

to predict phenotypes Envirotyping and FB-HTP in conjunction with results from previous 635

experimentation provide the multivariate data needed to develop genotype-specific crop models 636

that capture the nonlinear dynamics of plant development under variable environmental 637

conditions Combining genomic models which tend to capture additive genetic effects with crop 638

models may provide the ability to better incorporate non-additive genetic effects and genotype 639

by environment (GtimesE) interactions The integration of these two approaches has the potential to 640

significantly enhance the prediction accuracy of modeled phenotypes 641

LITERATURE CITED 643

AANENSEN D M HUNTLEY D M MENEGAZZO M POWELL C I amp SPRATT B G 2014 EpiCollect+ 644

linking smartphones to web applications for complex data collection projects F1000Research 3 645

AMATO M BASSO B CELANO G BITELLA G MORELLI G amp ROSSI R 2008 In situ detection of tree 646

root distribution and biomass by multi-electrode resistivity imaging Tree physiology 28 1441-647

1448 648

ANDRADE-SANCHEZ P GORE M A HEUN J T THORP K R CARMO-SILVA A E FRENCH A N 649

SALVUCCI M E amp WHITE J W 2013 Development and evaluation of a field-based high-650

throughput phenotyping platform Functional Plant Biology 41 68-79 651

ARAUS J L amp CAIRNS J E 2014 Field high-throughput phenotyping the new crop breeding frontier 652

Trends in Plant Science 19 52-61 653

ASNER G P KNAPP D E BOARDMAN J GREEN R O KENNEDY-BOWDOIN T EASTWOOD M 654

MARTIN R E ANDERSON C amp FIELD C B 2012 Carnegie Airborne Observatory-2 Increasing 655

science data dimensionality via high-fidelity multi-sensor fusion Remote Sensing of 656

Environment 124 454-465 657

ASNER G P MARTIN R E ANDERSON C B amp KNAPP D E 2015 Quantifying forest canopy traits 658

Imaging spectroscopy versus field survey Remote Sensing of Environment 158 15-27 659

ASNER G P MARTIN R E KNAPP D E TUPAYACHI R ANDERSON C CARRANZA L MARTINEZ P 660

HOUCHEIME M SINCA F amp WEISS P 2011 Spectroscopy of canopy chemicals in humid 661

tropical forests Remote Sensing of Environment 115 3587-3598 662

ASSENG S EWERT F MARTRE P ROTTER R P LOBELL D B CAMMARANO D KIMBALL B A 663

OTTMAN M J WALL G W WHITE J W REYNOLDS M P ALDERMAN P D PRASAD P V 664

V AGGARWAL P K ANOTHAI J BASSO B BIERNATH C CHALLINOR A J DE SANCTIS G 665

DOLTRA J FERERES E GARCIA-VILA M GAYLER S HOOGENBOOM G HUNT L A 666

IZAURRALDE R C JABLOUN M JONES C D KERSEBAUM K C KOEHLER A K MULLER C 667

NARESH KUMAR S NENDEL C OLEARY G OLESEN J E PALOSUO T PRIESACK E EYSHI 668

REZAEI E RUANE A C SEMENOV M A SHCHERBAK I STOCKLE C STRATONOVITCH P 669

STRECK T SUPIT I TAO F THORBURN P J WAHA K WANG E WALLACH D WOLF J 670

ZHAO Z amp ZHU Y 2015 Rising temperatures reduce global wheat production Nature Clim 671

Change 5 143-147 672

BAHUGUNA R N amp JAGADISH K S V 2015 Temperature regulation of plant phenological 673

development Environmental and Experimental Botany 111 83-90 674

BANNARI A MORIN D BONN F amp HUETE A R 1995 A review of vegetation indices Remote Sensing 675

Reviews 13 95-120 676

BEFF L GUumlNTHER T VANDOORNE B COUVREUR V amp JAVAUX M 2013 Three-dimensional 677

monitoring of soil water content in a maize field using Electrical Resistivity Tomography 678

Hydrology and Earth System Sciences 17 595-609 679

BELASQUE JR J GASPAROTO M amp MARCASSA L G 2008 Detection of mechanical and disease 680

stresses in citrus plants by fluorescence spectroscopy Applied Optics 47 1922-1926 681

BITELLA G ROSSI R BOCHICCHIO R PERNIOLA M amp AMATO M 2014 A Novel Low-Cost Open-682

Hardware Platform for Monitoring Soil Water Content and Multiple Soil-Air-Vegetation 683

Parameters Sensors (Basel Switzerland) 14 19639-19659 684

BOCK C PARKER P COOK A amp GOTTWALD T 2008 Visual rating and the use of image analysis for 685

assessing different symptoms of citrus canker on grapefruit leaves Plant Disease 92 530-541 686

BRADLEY B 2014 Remote detection of invasive plants a review of spectral textural and phenological 687

approaches Biological Invasions 16 1411-1425 688

BREVIK E C FENTON T E amp LAZARI A 2006 Soil electrical conductivity as a function of soil water 689

content and implications for soil mapping Precision Agriculture 7 393-404 690

BROWN A L DAY F P amp STOVER D B 2009 Fine root biomass estimates from minirhizotron imagery 691

in a shrub ecosystem exposed to elevated CO2 Plant and soil 317 145-153 692

BUSEMEYER L MENTRUP D MOumlLLER K WUNDER E ALHEIT K HAHN V MAURER H P REIF J 693

C WUumlRSCHUM T amp MUumlLLER J 2013a BreedvisionmdashA multi-sensor platform for non-694

destructive field-based phenotyping in plant breeding Sensors 13 2830-2847 695

BUSEMEYER L RUCKELSHAUSEN A MOumlLLER K MELCHINGER A E ALHEIT K V MAURER H P 696

HAHN V WEISSMANN E A REIF J C amp WUumlRSCHUM T 2013b Precision phenotyping of 697

biomass accumulation in triticale reveals temporal genetic patterns of regulation Scientific 698

Reports 3 2442 699

CALDEROacuteN R NAVAS-CORTEacuteS J A LUCENA C amp ZARCO-TEJADA P J 2013 High-resolution airborne 700

hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using 701

fluorescence temperature and narrow-band spectral indices Remote Sensing of Environment 702

139 231-245 703

CHAPMAN S C 2008 Use of crop models to understand genotype by environment interactions for 704

drought in real-world and simulated plant breeding trials Euphytica 161 195-208 705

CHAPMAN S C COOPER M HAMMER G L amp BUTLER D G 2000 Genotype by environment 706

interactions affecting grain sorghum II Frequencies of different seasonal patterns of drought 707

stress are related to location effects on hybrid yields Australian Journal of Agricultural Research 708

51 209-222 709

CHAPMAN S C MERZ T CHAN A JACKWAY P HRABAR S DRECCER M F HOLLAND E ZHENG 710

B LING T J amp JIMENEZ-BERNI J 2014 Pheno-copter a low-altitude autonomous remote-711

sensing robotic helicopter for high-throughput field-based phenotyping Agronomy 4 279-301 712

CHEN D NEUMANN K FRIEDEL S KILIAN B CHEN M ALTMANN T amp KLUKAS C 2014 Dissecting 713

the phenotypic components of crop plant growth and drought responses based on high-714

throughput image analysis The Plant Cell 26 4636-4655 715

CHENU K COOPER M HAMMER G L MATHEWS K L DRECCER M F amp CHAPMAN S C 2011 716

Environment characterization as an aid to wheat improvement interpreting genotypendash717

environment interactions by modelling water-deficit patterns in North-Eastern Australia Journal 718

of Experimental Botany 62 1743-1755 719

COOPER M MESSINA C D PODLICH D TOTIR L R BAUMGARTEN A HAUSMANN N J WRIGHT 720

D amp GRAHAM G 2014 Predicting the future of plant breeding complementing empirical 721

evaluation with genetic prediction Crop and Pasture Science 65 311-336 722

COOPER M TECHNOW F MESSINA C GHO C amp TOTIR L R 2016 Use of crop growth models with 723

whole-genome prediction application to a maize multienvironment trial Crop Science 56 1-16 724

CURRAN P J 1989 Remote sensing of foliar chemistry Remote Sensing of Environment 30 271-278 725

DAMM A GUANTER L PAUL-LIMOGES E VAN DER TOL C HUENI A BUCHMANN N EUGSTER W 726

AMMANN C amp SCHAEPMAN M E 2015 Far-red sun-induced chlorophyll fluorescence shows 727

ecosystem-specific relationships to gross primary production An assessment based on 728

observational and modeling approaches Remote Sensing of Environment 166 91-105 729

DAS A SCHNEIDER H BURRIDGE J ASCANIO A K M WOJCIECHOWSKI T TOPP C N LYNCH J P 730

WEITZ J S amp BUCKSCH A 2015 Digital imaging of root traits (DIRT) a high-throughput 731

computing and collaboration platform for field-based root phenomics Plant Methods 11 51 732

DAVENTI 2011 FieldApp httpsitunesapplecomusappfieldappid457953534mt=8ampign-733

mpt=uo3D4 734

DEANS A R LEWIS S E HUALA E ANZALDO S S ASHBURNER M BALHOFF J P BLACKBURN D 735

C BLAKE J A BURLEIGH J G CHANET B COOPER L D COURTOT M CSOumlSZ S CUI H 736

DAHDUL W DAS S DECECCHI T A DETTAI A DIOGO R DRUZINSKY R E DUMONTIER 737

M FRANZ N M FRIEDRICH F GKOUTOS G V HAENDEL M HARMON L J HAYAMIZU T 738

F HE Y HINES H M IBRAHIM N JACKSON L M JAISWAL P JAMES-ZORN C KOumlHLER S 739

LECOINTRE G LAPP H LAWRENCE C J LE NOVEgraveRE N LUNDBERG J G MACKLIN J MAST 740

A R MIDFORD P E MIKOacute I MUNGALL C J OELLRICH A OSUMI-SUTHERLAND D 741

PARKINSON H RAMIacuteREZ M J RICHTER S ROBINSON P N RUTTENBERG A SCHULZ K S 742

SEGERDELL E SELTMANN K C SHARKEY M J SMITH A D SMITH B SPECHT C D 743

SQUIRES R B THACKER R W THESSEN A FERNANDEZ-TRIANA J VIHINEN M VIZE P D 744

VOGT L WALL C E WALLS R L WESTERFELD M WHARTON R A WIRKNER C S 745

WOOLLEY J B YODER M J ZORN A M amp MABEE P 2015 Finding Our Way through 746

Phenotypes PLoS Biol 13 e1002033 747

DEERY D JIMENEZ-BERNI J JONES H SIRAULT X amp FURBANK R 2014 Proximal remote sensing 748

buggies and potential applications for field-based phenotyping Agronomy 4 349-379 749

DIETRICH R BENGOUGH A JONES H amp WHITE P 2013 Can root electrical capacitance be used to 750

predict root mass in soil Annals of Botany 112 457-464 751

DIVERSITY ARRAYS TECHNOLOGY PTY LTD 2015 KDSmart 752

httpsplaygooglecomstoreappsdetailsid=comdiversityarrayskdsmart 753

EHRLER W L 1973 Cotton leaf temperatures as related to soil water depletion and meteorological 754

factors Agronomy Journal 65 404-409 755

FANG Y amp RAMASAMY R P 2015 Current and prospective methods for plant disease detection 756

Biosensors 5 537-561 757

FRY W 1982 Disease management in practice Principles of Plant Disease Management New York 758

Academic Press Inc 759

FUKATSU T amp HIRAFUJI M 2005 Field monitoring using sensor-nodes with a web server Journal of 760

Robotics and Mechatronics 17 164-172 761

FURBANK R T amp TESTER M 2011 Phenomics ndash technologies to relieve the phenotyping bottleneck 762

GATES D M KEEGAN H J SCHLETER J C amp WEIDNER V R 1965 Spectral properties of plants 764

Applied Optics 4 11-20 765

GOVENDER M DYE P WEIERSBYE I WITKOWSKI E amp AHMED F 2009 Review of commonly used 766

remote sensing and ground-based technologies to measure plant water stress Water SA 35 767

741-752 768

GRANUM E PEacuteREZ-BUENO M L CALDEROacuteN C E RAMOS C DE VICENTE A CAZORLA F M amp 769

BAROacuteN M 2015 Metabolic responses of avocado plants to stress induced by Rosellinia necatrix 770

analysed by fluorescence and thermal imaging European Journal of Plant Pathology 142 625-771

632 772

GUO L CHEN J CUI X FAN B amp LIN H 2013 Application of ground penetrating radar for coarse root 773

detection and quantification a review Plant and soil 362 1-23 774

GUO L WU Y CHEN J HIRANO Y TANIKAWA T LI W amp CUI X 2015a Calibrating the impact of 775

root orientation on root quantification using ground-penetrating radar Plant and Soil 395 289-776

305 777

GUO Y HUANG J SHI Z amp LI H 2015b Mapping spatial variability of soil salinity in a coastal paddy 778

field based on electromagnetic sensors PLoS ONE 10 e0127996 779

HABERLAND J A 2010 AgIIS Agricultural Irrigation Imaging System design and application The 780

University of Arizona 781

HAMMER G MESSINA C VAN OOSTEROM E CHAPMAN S SINGH V BORRELL A JORDAN D amp 782

COOPER M 2016 Molecular breeding for complex adaptive traits How integrating crop 783

ecophysiology and modelling can enhance efficiency Crop Systems Biology Springer 784

HAMMER G L MCLEAN G CHAPMAN S ZHENG B DOHERTY A HARRISON M T VAN 785

OOSTEROM E amp JORDAN D 2014 Crop design for specific adaptation in variable dryland 786

production environments Crop and Pasture Science 65 614-626 787

HARTEMINK A E amp MINASNY B 2014 Towards digital soil morphometrics Geoderma 230 305-317 788

HAXELTINE A amp PRENTICE I C 1996 BIOME3 An equilibrium terrestrial biosphere model based on 789

ecophysiological constraints resource availability and competition among plant functional 790

types Global Biogeochemical Cycles 10 693-709 791

HIRAFUJI M FUKATSU T amp HAOMING H Full-wireless field monitoring server for advanced sensor-792

network Proc of AFITAWCCA Joint Congress on IT in Agriculture 2004 692-697 793

HIRANO Y DANNOURA M AONO K IGARASHI T ISHII M YAMASE K MAKITA N amp KANAZAWA 794

Y 2009 Limiting factors in the detection of tree roots using ground-penetrating radar Plant and 795

Soil 319 15-24 796

HOLZWORTH D P HUTH N I DEVOIL P G ZURCHER E J HERRMANN N I MCLEAN G CHENU 797

K VAN OOSTEROM E J SNOW V MURPHY C MOORE A D BROWN H WHISH J P M 798

VERRALL S FAINGES J BELL L W PEAKE A S POULTON P L HOCHMAN Z THORBURN P 799

J GAYDON D S DALGLIESH N P RODRIGUEZ D COX H CHAPMAN S DOHERTY A 800

TEIXEIRA E SHARP J CICHOTA R VOGELER I LI F Y WANG E HAMMER G L 801

ROBERTSON M J DIMES J P WHITBREAD A M HUNT J VAN REES H MCCLELLAND T 802

CARBERRY P S HARGREAVES J N G MACLEOD N MCDONALD C HARSDORF J 803

WEDGWOOD S amp KEATING B A 2014 APSIM ndash Evolution towards a new generation of 804

agricultural systems simulation Environmental Modelling amp Software 62 327-350 805

HOMOLOVAacute L MALENOVSKYacute Z CLEVERS J G P W GARCIacuteA-SANTOS G amp SCHAEPMAN M E 2013 806

Review of optical-based remote sensing for plant trait mapping Ecological Complexity 15 1-16 807

IBREEDIT LTD BreedIT MobileTM Application 808

httpsplaygooglecomstoreappsdetailsid=comibreeditmobilelive 809

INATURALIST iNaturalist httpwwwinaturalistorg 810

JACKSON R D IDSO S B REGINATO R J amp PINTER P J J 1981 Canopy temperature as a crop water 811

stress indicator Water Resources Research 17 1133-1138 812

JACQUEMOUD S amp BARET F 1990 PROSPECT A model of leaf optical properties spectra Remote 813

Sensing of Environment 34 75-91 814

JEDMOWSKI C amp BRUumlGGEMANN W 2015 Imaging of fast chlorophyll fluorescence induction curve 815

(OJIP) parameters applied in a screening study with wild barley (Hordeum spontaneum) 816

genotypes under heat stress Journal of Photochemistry and Photobiology B Biology 151 153-817

160 818

JONES H G 2004 Application of thermal imaging and infrared sensing in plant physiology and 819

ecophysiology Advances in Botanical Research Academic Press 820

JONES H G amp VAUGHAN R A 2010 Remote Sensing of Vegetation New York Oxford University Press 821

KERR J T amp OSTROVSKY M 2003 From space to species ecological applications for remote sensing 822

Trends in Ecology amp Evolution 18 299-305 823

KIM J LEE S AHN H SEO D PARK S amp CHOI C 2013 Feasibility of employing a smartphone as the 824

payload in a photogrammetric UAV system ISPRS Journal of Photogrammetry and Remote 825

Sensing 79 1-18 826

KRAJEWSKI P CHEN D ĆWIEK H VAN DIJK A D J FIORANI F KERSEY P KLUKAS C LANGE M 827

MARKIEWICZ A NAP J P VAN OEVEREN J POMMIER C SCHOLZ U VAN SCHRIEK M 828

USADEL B amp WEISE S 2015 Towards recommendations for metadata and data handling in 829

plant phenotyping Journal of Experimental Botany 66 5417-5427 830

LICHTENTHALER H K amp MIEHEacute J A 1997 Fluorescence imaging as a diagnostic tool for plant stress 831

LIEBISCH F KIRCHGESSNER N SCHNEIDER D WALTER A amp HUND A 2015 Remote aerial 833

phenotyping of maize traits with a mobile multi-sensor approach Plant Methods 11 1-20 834

LIEVENS B amp THOMMA B P 2005 Recent developments in pathogen detection arrays implications for 835

fungal plant pathogens and use in practice Phytopathology 95 1374-1380 836

LINS E BELASQUE J GASPAROTO M C BAGNATO V S amp MARCASSA L G Fluorescence 837

spectroscopy for detection of citrus canker in orange plantation Frontiers in Optics 2006 838

Optical Society of America JWD55 839

LOBELL D B ASNER G P LAW B E amp TREUHAFT R N 2002 View angle effects on canopy 840

reflectance and spectral mixture analysis of coniferous forests using AVIRIS International 841

Journal of Remote Sensing 23 2247-2262 842

LOumlFFLER C M WEI J FAST T GOGERTY J LANGTON S BERGMAN M MERRILL B amp COOPER M 843

2005 Classification of maize environments using crop simulation and geographic information 844

systems Crop Science 45 1708-1716 845

LUQUET D DINGKUHN M KIM H TAMBOUR L amp CLEMENT-VIDAL A 2006 EcoMeristem a model 846

of morphogenesis and competition among sinks in rice 1 Concept validation and sensitivity 847

analysis Functional Plant Biology 33 309-323 848

LYNCH M amp WALSH B 1998 Genetics and Analysis of Quantitative Traits Sunderland Sinauer 849

MAEGHT J-L REWALD B amp PIERRET A 2013 How to study deep rootsmdashand why it matters Frontiers 850

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Figure 4 Schematic illustrating the integration of several data types for modeling phenotypes The flow diagram shows the process in which genotype environment (including management practices) and their interaction produce the phenotype and how field-based high-throughput phenotyping (FB-HTP) can be integrated with envirotyping genomic models and crop models to predict phenotypes Envirotyping and FB-HTP in conjunction with results from previous experimentation provide the multivariate data needed to develop genotype-specific crop models that capture the nonlinear dynamics of plant development under variable environmental conditions Combining genomic models which tend to capture additive genetic effects with crop models may provide the ability to better incorporate non-additive genetic effects and genotype by environment (GtimesE) interactions The integration of these two approaches has the potential to significantly enhance the prediction accuracy of modeled phenotypes

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MARCASSA L GASPAROTO M BELASQUE JR J LINS E NUNES F D amp BAGNATO V 2006 Fluorescence spectroscopyapplied to orange trees Laser physics 16 884-888

MARTIN M E PLOURDE L C OLLINGER S V SMITH M L amp MCNEIL B E 2008 A generalizable method for remote sensingof canopy nitrogen across a wide range of forest ecosystems Remote Sensing of Environment 112 3511-3519

MASSACCI A NABIEV S M PIETROSANTI L NEMATOV S K CHERNIKOVA T N THOR K amp LEIPNER J 2008 Response ofthe photosynthetic apparatus of cotton (Gossypium hirsutum) to the onset of drought stress under field conditions studied by gas-exchange analysis and chlorophyll fluorescence imaging Plant Physiology and Biochemistry 46 189-195

MAXWELL K amp JOHNSON G N 2000 Chlorophyll fluorescencemdasha practical guide Journal of Experimental Botany 51 659-668 wwwplantphysiolorgon June 5 2020 - Published by Downloaded from Copyright copy 2016 American Society of Plant Biologists All rights reserved

MERONI M ROSSINI M GUANTER L ALONSO L RASCHER U COLOMBO R amp MORENO J 2009 Remote sensing ofsolar-induced chlorophyll fluorescence Review of methods and applications Remote Sensing of Environment 113 2037-2051

MESSMER R FRACHEBOUD Y BAumlNZIGER M STAMP P amp RIBAUT J-M 2011 Drought stress and tropical maize QTLs forleaf greenness plant senescence and root capacitance Field Crops Research 124 93-103

MICKELBART M V HASEGAWA P M amp BAILEY-SERRES J 2015 Genetic mechanisms of abiotic stress tolerance that translateto crop yield stability Nature Review Genetics 16 237-251

MIRMAJLESSI S M DESTEFANIS M GOTTSBERGER R A MAumlND M amp LOIT E 2015 PCR-based specific techniques usedfor detecting the most important pathogens on strawberry a systematic review Systematic reviews 4 1

MONTES J M TECHNOW F DHILLON B S MAUCH F amp MELCHINGER A E 2011 High-throughput non-destructive biomassdetermination during early plant development in maize under field conditions Field Crops Research 121 268-273

MULLA D J 2013 Twenty five years of remote sensing in precision agriculture key advances and remaining knowledge gapsBiosystems Engineering 114 358-371

MUTKA A M amp BART R S 2015 Image-based phenotyping of plant disease symptoms Frontiers Plant Science 5 1-8Pubmed Author and TitleCrossRef Author and TitleGoogle Scholar Author Only Title Only Author and Title

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OELLRICH A WALLS R L CANNON E K CANNON S B COOPER L GARDINER J GKOUTOS G V HARPER L HE M ampHOEHNDORF R 2015 An ontology approach to comparative phenomics in plants Plant methods 11 1

PAULI D ANDRADE-SANCHEZ P CARMO-SILVA A E GAZAVE E FRENCH A N HEUN J HUNSAKER D J LIPKA A ESETTER T L STRAND R J THORP K R WANG S WHITE J W amp GORE M A 2016 Field-based high-throughput plantphenotyping reveals the temporal patterns of quantitative trait loci associated with stress-responsive traits in cotton G3Genes|Genomes|Genetics 6 865-879

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REBOLLEDO M C DINGKUHN M COURTOIS B GIBON Y CLEacuteMENT-VIDAL A CRUZ D F DUITAMA J LORIEUX M ampLUQUET D 2015 Phenotypic and genetic dissection of component traits for early vigour in rice using plant growth modellingsugar content analyses and association mapping Journal of Experimental Botany 66 5555-5566

RIFE T W amp POLAND J A 2014 Field Book An open-source application for field data collection on Android Crop Science 541624-1627

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SANKARAN S KHOT L R ESPINOZA C Z JAROLMASJED S SATHUVALLI V R VANDEMARK G J MIKLAS P N CARTERA H PUMPHREY M O KNOWLES N R amp PAVEK M J 2015 Low-altitude high-resolution aerial imaging systems for row andfield crop phenotyping A review European Journal of Agronomy 70 112-123

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WHITE J W ANDRADE-SANCHEZ P GORE M A BRONSON K F COFFELT T A CONLEY M M FELDMANN K AFRENCH A N HEUN J T HUNSAKER D J JENKS M A KIMBALL B A ROTH R L STRAND R J THORP K R WALL GW amp WANG G 2012 Field-based phenomics for plant genetics research Field Crops Research 133 101-112

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YADETA K A amp THOMMA B P 2014 The xylem as battleground for plant hosts and vascular wilt pathogens Frontiers in PlantScience 4

YANG X TANG J MUSTARD J F LEE J-E ROSSINI M JOINER J MUNGER J W KORNFELD A amp RICHARDSON A D2015 Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in atemperate deciduous forest Geophysical Research Letters 42 2977-2987

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ZENONE T MORELLI G TEOBALDELLI M FISCHANGER F MATTEUCCI M SORDINI M ARMANI A FERREgrave C CHITI T wwwplantphysiolorgon June 5 2020 - Published by Downloaded from

amp SEUFERT G 2008 Preliminary use of ground-penetrating radar and electrical resistivity tomography to study tree roots in pineforests and poplar plantations Functional Plant Biology 35 1047-1058

ZHANG J PU R YUAN L WANG J HUANG W amp YANG G 2014 Monitoring powdery mildew of winter wheat by usingmoderate resolution multi-temporal satellite imagery PLoS ONE 9 e93107

ZHENG B CHAPMAN S C CHRISTOPHER J T FREDERIKS T M amp CHENU K 2015 Frost trends and their estimated impacton yield in the Australian wheatbelt Journal of Experimental Botany 66 6311-3623

ZHU S HUANG C SU Y amp SATO M 2014 3D ground penetrating radar to detect tree roots and estimate root biomass in thefield Remote Sensing 6 5754-5773

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ADVANCEMENTS 43

Plants are intrinsically related to their environment and observed phenotypes are a direct product 44

of this interaction Therefore the ability to study and quantify phenotypes under real-world 45

conditions is essential to the basic understanding as well as improvement of ecophysiological 46

traits 47

Recent technological developments have enabled progress in plant phenotyping but areas such as 48

root phenotyping are still lacking the needed instrumentation in order to capitalize on these 49

developments 50

Extraction of high dimensional phenotype data from images is becoming more commonplace 51

with advancements in image processing software This is leading to the discovery of novel 52

phenotypes not previously identified but that are more related to underlying physiological 53

processes 54

Developments in envirotyping and crop growth modeling can provide a useful framework for 55

understanding plant development 56

technologies 88

stress 216

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

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Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

technologies 88

stress 216

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

technologies 88

stress 216

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

stress 216

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

stress 216

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

stress 216

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

stress 216

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

stress 216

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

phenomena 368

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

ENVIROTYPING 447

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

CONCLUSIONS 547

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

FIGURE LEGENDS 601

stem 629

1448 648

Change 5 143-147 672

Reviews 13 95-120 676

Reports 3 2442 699

139 231-245 703

51 209-222 709

mpt=uo3D4 734

741-752 768

632 772

305 777

Soil 319 15-24 796

160 818

Sensing 79 1-18 826

131-144 856

Research 124 93-103 873

Science 5 1-8 885

7 3232-3249 888

10 923

225 934

e93107 976

6311-3623 979

Parsed Citations

Article File

Figure 1

Figure 2

Figure 3

Figure 4

Parsed Citations

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Documents

The quest for understanding phenotypic variation via integrated ...€¦ · 4 87 where further consideration is needed to capitalize on advancements in phenotyping 88 technologies