# Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System

Pascal Neveu, Anne Tireau, Nadine Hilgert, Vincent Negre, Jonathan

Mineau-Cesari, Nicolas Brichet, Romain Chapuis, Isabelle Sanchez, Cyril

Pommier, Brigitte Charnomordic, et al.

► To cite this version:

Pascal Neveu, Anne Tireau, Nadine Hilgert, Vincent Negre, Jonathan Mineau-Cesari, et al. Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System. New Phytologist, 2019, 221 (1), pp.588-601. 10.1111/nph.15385. hal-02627721

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Methods

# Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System

Pascal Neveu<fnref n="1" />, Anne Tireau<fnref n="1" />, Nadine Hilgert<fnref n="1" />, Vincent Nègre², Jonathan Mineau-Cesari<fnref n="1" />,², Nicolas Brichet², Romain Chapuis<fnref n="3" />, Isabelle Sanchez<fnref n="1" />, Cyril Pommier⁴ ID, Brigitte Charnomordic<fnref n="1" />, François Tardieu² ID and Llorenç Cabrera-Bosquet² ID

Author for correspondence:
Llorenç Cabrera-Bosquet
Tel: +33 499 612 956
Email: llorenc.cabrera-bosquet@inra.fr

Received: 7 June 2018

Accepted: 7 July 2018

New Phytologist (2018)

doi: 10.1111/nph.15385

**Key words:** data integration, data science, Information System, interoperability, knowledge, ontology, open source, phenomics.

## Summary

* Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events.

* The open-source Phenotyping Hybrid Information System (PHIS) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved.

* Its ontology-driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata. It interoperates with external resources via web services, thereby allowing data integration into other systems; for example, modelling platforms or external databases.

* It has the potential for rapid diffusion because of its ability to integrate, manage and visualize multi-source and multi-scale data, but also because it is based on 10 yr of trial and error in our groups.

## Introduction

In recent years, plant phenomics has produced massive datasets involving millions of images in experiments performed in the field and in controlled conditions, concerning hundreds of genotypes at different phenological stages (Furbank & Tester, 2011; Fiorani & Schurr, 2013). These datasets also involve the outputs of hundreds of sensors for tens of variables characterizing plants, soil and air (Salehi et al., 2015; Negin & Moshelion, 2016; Rebetzke et al., 2016). They often also involve omic data associated with imaged plants (Hannemann et al., 2009; Großkinsky et al., 2017). Plant phenomics is increasingly multi-source and multi-scale, with joint analyses of information originating from different phenotyping platforms and fields. Taken together, these datasets are unprecedented resources for identifying and testing novel mechanisms and models (Tardieu et al., 2017). They are extremely expensive, and also contain so much information that

the group who has collected a dataset most often has not all the required skills, resources and scientific questions to perform every relevant analysis they may allow. Hence, there is an increasing need to make them available to a range of users, allowing reanalyses and combination with other datasets to generate new knowledge (Adam-Blondon et al., 2016; Arend et al., 2016; Leonelli et al., 2017).

The reuse of data frequently presents the difficulty of insufficient information besides phenotypic data themselves (Hannemann et al., 2009; Gkoutos et al., 2017). Most measured traits differ between experiments for a given genotype because of environmental conditions (Massonnet et al., 2010; Malosetti et al., 2013). Hence, phenotypic datasets in each experiment need to be associated with detailed environmental information for meta-analyses. Furthermore, each plant or sub-plot senses different environmental conditions within a given field, glasshouse or growth chamber (Granier et al., 2006; Cabrera-Bosquet et al., 2016), so

<footnote n="1">¹MISTEA, INRA, Montpellier SupAgro, Université de Montpellier, Montpellier 34060, France; ²LEPSE, INRA, Montpellier SupAgro, Université de Montpellier, Montpellier 34060, France;</footnote>

<footnote n="3">³UE DIASCOPE, INRA, Montpellier SupAgro, Université de Montpellier, Montpellier 34060, France; ⁴INRA, UR1164 URGI - Research Unit in Genomics-Info, INRA de Versailles-</footnote>

<footnote>Grignon, Route de Saint-Cyr, Versailles 78026, France</footnote>
keeping track of the position of each plant or plot is essential. This
is widely accepted for field experiments in view of the large vari-
ability of traits and yield within and between fields, which can be
accounted for by using mixed models (van Eeuwijk et al., 2010;
Bustos-Korts et al., 2016). Paradoxically, this is less accepted in
controlled conditions in which the spatial distribution of environ-
mental conditions and the x-y positions of plants are seldom
stored in databases. Uncertainties about plant management (e.g.
times of sampling, imaging, or irrigation times) and identification
(genotype, seed lot) are also frequent obstacles to meta-analyses.
Finally, it increasingly appears that data analyses themselves are
difficult to reproduce if each step, from sensor to trait, has not
been traced (Pradal et al., 2017; Tardieu et al., 2017).

Reconciling phenotypic information in the field and in con-
trolled conditions is a crucial challenge. Whereas it is accepted that
measuring yield in controlled conditions is most often nonrelevant
(Poorter et al., 2016), the high-throughput measurement of physi-
ological variables in the field is often impossible; for example, the
precise measurement of water or nutrient fluxes through the plant,
or of architectural features of root or shoot systems. Such measure-
ments are possible in controlled conditions, opening the way to
the dissection of the genetic architecture of physiological traits
(Mairhofer et al., 2013; Cabrera-Bosquet et al., 2016; Coupel-
Ledru et al., 2016; Kalogiros et al., 2016; Alvarez Prado et al.,
2018). Combining data in field and controlled conditions is possi-
ble, and provides valuable information for analysing and predict-
ing the genotype × environment interaction of both traits and
yields (Reymond et al., 2003; Lacube et al., 2017; Tardieu et al.,
2018). An essential feature of phenomic information systems is
therefore to facilitate these trans-scale joint analyses of experiments
in field and controlled conditions.

A major challenge in plant phenomics is therefore to design
information systems able to organize and store heterogeneous
datasets including thousands of objects as different as, for exam-
ple, images, spectra, time courses of variables, parameters of
image analysis, x-y positions of plants/plots, biomass, or yield.
Finding and accessing data originating from multiple sources (in-
cluding contextual information associated with individual plants,
plots or sensors) and taking into account spatial and temporal
relationships between objects (i.e. plants, organs, sensors and
phenotyping facilities) is central for both real-time monitoring of
experiments and for post-experiment interpretation of measured
traits (Cabrera-Bosquet et al., 2012). The challenge is still larger
if information systems aim to organize data originating from dif-
ferent groups, different scales and different infrastructures with
FAIR (findable, accessible, interoperable and reusable) require-
ments (Wilkinson et al., 2016) for tracing data, but also proto-
cols, methods and workflows, in such a way that scientists who
did not perform experiments can reuse data. Recently, several
papers have recommended standardization protocols and enrich-
ment of datasets with metadata (Arend et al., 2014, 2016; Junker
et al., 2014; Krajewski et al., 2015; Ćwiek-Kupczyńska et al.,
2016) and scientific workflows (Pradal et al., 2017). However,
this information is rarely incorporated into information systems.

The use of open and extensible database schemas based on
Ontology Web Language (OWL) (Grau et al., 2008) allows

formalized description and contextual information of objects
involved in experiments (Li et al., 2013; Krajewski et al., 2015;
Ćwiek-Kupczyńska et al., 2016; Le Ngoc et al., 2016). Tools
using ontologies and semantics are available in functional
genomics and systems biology (Jones et al., 2007; Gkoutos
et al., 2017). Ontology-centred architectures such as XEML LAB
(Hannemann et al., 2009), PODD (Li et al., 2013) and SILEX
(Information System for Experiment, https://www6.montpellier.
inra.fr/mistea_eng/Projects/Silex) have been proposed for plant
phenomic studies. However, most published or commercial
databases for plant phenomics are still specifically designed to
handle and store data from particular installations or species
(Fabre et al., 2011; Nagel et al., 2012; Klukas et al., 2014; Cop-
pens et al., 2017). Hence, the wide variety of phenotypic, envi-
ronmental and contextual data is spread in a range of databases,
lab books and individual text/spreadsheet files, thereby compli-
cating the traceability and access to experimental results and
associated metadata.

Here, we present a suite of methods, synthesized in the open-
source Phenotyping Hybrid Information System (PHIS) for inte-
grating and sharing multi-source and multi-scale data (in particu-
lar those obtained in both controlled and field conditions), and
semantic annotation of experiments with knowledge and meta-
data. This system is available to the public community and has
been deployed in installations in both field and controlled condi-
tions. Its main interest is that most of its properties have been
built based on trial and error over 10 yr of phenotyping practices
in groups specialized in either information technology or in phe-
notyping. For better legibility, we restrict examples in this paper
to two installations located in Montpellier.

Materials and Methods

PHIS architecture

PHIS is a hybrid information system derived from SILEX, a col-
laborative project aiming to provide software components for
experimental data and knowledge management between different
research groups. The PHIS architecture consists of five compo-
nents structured in layers; namely, a web user interface, a data
and knowledge layer, a web service layer, a smart layer and a sci-
entific computation and workflow layer (Fig. 1).

Web user interface

PHIS is accessed through an interactive web user interface
(http://www.phis.inra.fr) after user login. A user belongs to one
or several groups and has access to data with different access
rights (admin, owner or guest). Administration level gives full
access to all content, owners have access to their own datasets and
to public data, and guests have access to public data only. The
web user interface contains three main menus, which are dynami-
cally adapted depending whether the experiment is performed in
the field or in controlled conditions. The Experimental Organisa-
tion menu contains information about the experimental
resources, including projects, experiments, infrastructure, plants,
<figure><img src="image_4.png" /><figcaption>**Fig. 1** The Phenotyping Hybrid Information System (PHIS) architecture consisting of five major components structured in different layers, which include (1) a web user interface, (2) a data and knowledge storage layer, (3) a web service layer, (4) a smart layer and (5) a scientific computation and workflow layer.</figcaption></figure>

germplasm, devices, events, phenotypic and environmental vari-
ables and object tracking tools (see Supporting Information
Notes S1–S3 for technical details). The Data menu contains
images, graphic visualization of phenotypic and environmental
traits, as well as access to data analysis and workflows. A search
engine allows advanced querying for entities like projects, experi-
ments, domain specific objects (plants, sensors, etc.), images and
genotypes using uniform resource identifiers (URIs) and/or filter-
ing options (Notes S4). The Tools menu contains installation-
specific widgets, namely, a URI generator, a label quick response
code (QR-code) generator, access to the closed-circuit television
of facilities, a vocabulary and access to the web service application
programming interface (API; Notes S5, S6). The Administration
menu contains tools for managing users, groups, experimental
facilities and control of data settings (variables, units and meth-
ods). The web user interface is implemented in PHP and
HTML5, CSS3 and JavaScript (jQuery library, http://jQuery.c
om). The skeleton of the application is developed with the Yii2
framework (http:// Yiiframework.com).

Data and knowledge layer

PHIS contains phenotypic, experimental and environmental
data. Tests were performed on field and controlled-condition
experiments. Field experiments were hosted at DIAPHEN

(https://www.phenome-fppn.fr/phenome_eng/Facilities/Montp
ellier-Field) at INRA Mauguio (southeast of France, 43°36′ N,
03°58′E) over 20 ha, which provides access to high-throughput
phenotyping tools including soil and aerial vectors (carrying
RGB, multi- and hyperspectral cameras and spectroradiometers)
as well as a series of sensors for characterizing environmental
conditions. Controlled-conditions experiments were hosted at
Montpellier Plant Phenotyping Platforms, M3P (https://
www6.montpellier.inra.fr/lepse/M3P), which implements a series
of tools with up to 500 sensors in parallel, three-dimensional
imaging cabins and automatisations (Sadok et al., 2005; Granier
et al., 2006; Cabrera-Bosquet et al., 2016).

Phenotypic data for field and platform experiments include
online (i.e. automatically recorded) images, growth and transpira-
tion kinetics and manually recorded phenotypic measurements.
Experimental data include protocols, description of variables and
plant material. Environmental data include sensor outputs (air
temperature and humidity, light, soil tensiometers) or variables
inferred using algorithms at high temporal and spatial resolution
(e.g. local light and temperature). Currently, these installations
have generated 20 million images and 250 million phenotypic
measurements performed in > 4000 genotypes and 25 species, and
154 million environmental measurements, involving 86 terabytes.

Structured data (e.g. environmental data and standardized
phenotypic variables) are stored using POSTGRESQL and
MONGODB. 'Weakly' structured data (e.g. plant observations and image analyses data) are stored using NoSQL technology (MONGODB). Raw images, thumbnails and analysed images (e.g. segmented images) are stored on a distributed storage system (iRODS) (https://irods.org/) (Rajasekar et al., 2010) with a replication mechanism and built-in scripts that permanently check the consistency with the associated metadata. Metadata and semantic annotations are stored taking into account both the ontologies, implemented in OWL (https://www.w3.org/OWL/), and the knowledge resulting from experimental observations, which is formalized as instances represented using the Resource Description Framework (RDF) format, and stored in a RDF4J Triple Store (http://rdf4j.org). Triple data is a data entity (i.e. subject predicate object) like 'plant736 participatesIn ExperimentA' and a Triple Store is a database system dedicated to the storage and the retrieval of triples through semantic queries (SPARQL). The Triple Store also allows ontology-based inferences, and provides web services accessible using SPARQL queries. The application ontologies are stored in an open-access repository, AgroPortal (http://agroportal.lirmm.fr/; Jonquet et al., 2018); see Notes S7 for technical details. Data access is achieved by the web service layer or by performing CSV extraction.

## Web service layer

A web service layer enables interoperability and data exchange with other applications and systems. This service facilitates the maintenance of the information system and provides a simplified interface to the smart layer and to the data and knowledge layer. The web service is based on RESTFul (representational state transfer) developed using Swagger framework (https://swagger.io/), and all services are available by using URIs. It is developed in Java with Jersey implementation of JAX-RS (Java API for RESTful Web Services) standard. It implements installations' relevant services of the Breeding API (http://www.brapi.org/), which specifies a standard interface for plant phenotype databases to serve data to crop breeding applications. Web service outputs use the data-interchange format JSON (JavaScript Object Notation; see Notes S6 for technical details).

## Smart layer, scientific computation and workflow layer

The Smart layer allows data to be interpretable for other communities by referring to external resources such as standardized semantic resources and reference or species-specific ontologies as described in the Planteome project (Cooper et al., 2018). References are managed using the Simple Knowledge Organization System (Miles & Bechhofer, 2009) that allows support of standardized and advanced queries by using ontologies. These queries and the necessary inferences (subsumption, transitivity, functional, etc.) are obtained by using an RDF4J engine included in the Triple Store. The scientific computation and workflow layer provides advanced visualization and statistical computing, including the automatic report generation, based in R (R Core Team, 2015), and enables computational analysis

and workflows through the scientific platform GALAXY (https://galaxyproject.org/). JavaScript libraries are also used for user friendly and interactive data exploration, including detection of inconsistencies and manual semantic annotation. Export tools are available in different formats for graphic (PDF, JPG, PNG, SVG) and numeric data (HTML, csv, txt, xlx, PDF and JSON). PHIS can integrate scientific workflows such as INFAPHENOGRID (Pradal et al., 2017), which provides provenance functionalities. Technical details are provided as Notes S4.

## Application example

PHIS features are illustrated here by using phenotypic data obtained in two experiments involving 59 common maize (Zea mays L.) hybrids performed in both the field (DIAPHEN) and glasshouse (PHENOARCH) installations. Data are available at http://www.phis.inra.fr/under, an Open Source license (CC-BY-NC-SA). The field experiment contains c. 10 000 scientific objects and 178 sensors, 10 000 images, 70 000 phenotypic observations, 20 000 annotations and 0.5 million environmental measurements. The glasshouse experiment contains 2204 scientific objects, 242 sensors, c. 2 million images, 10 million phenotypic observations, 15 000 annotations and > 4 million environmental measurements. Detailed information of the experiments, installations and measurement of environmental conditions are described in Cabrera-Bosquet et al. (2016) and Brichet et al. (2017).

## Availability and requirements

The source code and user and developer documentation of the latest version of PHIS are available at https://github.com/Ope nSILEX under a GNU AFFero General Public License version 2. PHIS requires Java JRE or JDK v.1.7, PHP 5.6, POSTGRESQL 10.1, RDF4j 2.2.1, MONGODB 3.4.4 and R 3.3.1 and runs on Linux, Mac and Microsoft Windows platforms.

## Results and Discussion

### A common information system for field and controlled-condition experiments

Combining field and controlled conditions in a common information system requires first ensuring that environmental conditions are measured in a compatible way, with common units and protocols, and second designing common ontologies of traits for both types of datasets. The first condition only requires the attention of experimenters, without large theoretical difficulty (Reymond et al., 2003; Lacube et al., 2017). The second condition is straightforward for traits that have a common definition at different scales, such as leaf appearance rate or leaf number per plant (Fig. 1a,b). However, other traits have different meanings and measurement procedures in field and controlled conditions, such as 'plant height', often defined as the highest green pixel corresponding to a plant in controlled
conditions, vs the mean canopy height in the field. In the same
way, 'leaf area' is most often defined based on direct measure-
ment on a three-dimensional plant representation in controlled
conditions vs inversion of a model of light interception based
on the gap fraction in the field (the gap fraction is the propor-
tion of area seen as sky in pictures from below, as in Fig. 3b).
Whereas the progression of phenological stages is similar for a
given genotype in field and controlled conditions (Fig. 2a,b),
the progression of leaf area measured either directly (controlled
conditions, Fig. 2c) or via the gap fraction (field, Fig. 2d)
showed markedly different temporal patterns. This illustrates
the fact that mapping ontologies in field and controlled condi-
tions is not only a question of standardization, but requires a
theoretical study for linking concepts, in particular with the
involvement of other traits such as plant architecture in the
case of leaf area. Hence, an information system for both field
and controlled conditions needs to optimize somewhat contra-
dictory requirements; namely, using ontologies and methods
that can be used for both types of datasets, taking into account
the difficulties mentioned earlier, while keeping the interface
sufficiently simple for the user.

The simplicity issue has been resolved by the design of the
interface that automatically redirects menus and functionalities
depending on whether the experiment is performed in the field
or in controlled conditions. The difficulties associated with the
commonality of tools, ontologies and workflows are therefore
kept in the background. They are addressed with tools examined
further in this paper.

Tracking all objects in phenotyping experiments via object
identification and ontology description

Tracking all objects involved in an experiment may seem unnec-
essary in simple experiments where unique correspondences exist
between, for example, each plant and its position in a glasshouse
or between each genotype and a plot in the field. In our own
experience, automatic tracking is essential when thousands of
plots, plants or sensors are dealt with. If not specifically reported
in the information system, the replacement of a sensor at a given
position (e.g. meteorological sensor or soil tensiometer) is not
obvious in the outputs of an environmental database. In
glasshouse experiments, a plant can be replaced by another plant
at the same position and vector (e.g. pot, cart) during an experi-
ment, potentially generating confusion. Because each sensor has
its own calibration, each pot has intrinsic characteristics (weight,
volume, age) and each position in the glasshouse or in the field
has its local environmental conditions, it is crucial to track these
objects and their relationships. All objects, therefore, need to be
identified in order to keep the necessary information associated
with them (e.g. positions over time, successive calibration for sen-
sors, origin for plants). For example, in Fig. 3(a), a plant, a pot, a
vector (here a cart placed on a conveyor belt) and a given x-y
position need to be considered independently for keeping track
of possible events in a glasshouse experiment. In the same way,
tracking a specific organ in a field experiment involves a leaf
belonging to a plant within a plot (Fig. 3b).

<figure><img src="image_5.png" /><figcaption>**Fig. 2** Example of trait variables that either (a, b) easily map between experiments in glasshouse and field or (c, d) do not map because of methodological difficulties. (a, b) Leaf appearance rate as function of thermal time for three genotypes in (a) glasshouse and (b) field experiments. (c, d) The progression of leaf area, either measured directly in the glasshouse (c) or via gap fraction (see Fig. 3b) in the field for the same three genotypes. For each genotype, points are the mean of nine and three replicates for the glasshouse and field experiments respectively.</figcaption></figure>
In PHIS, object identification is based on a URI, which is a
strings of characters used to identify an object in an unambiguous
way (Fig. 3). This ensures traceability in space and time, whilst a
typical identification by numbers (e.g. 'plant 736') refers to dif-
ferent plants in different experiments and installations. In the
glasshouse experiment described here, the plant 736 in installa-
tion1 has the URI <http://www.Nationalinfra/Local
infra_a/Installation1/2017/c17000736> (Fig. 4).
The plot 206 in the field experiment located in installation2 has
the URI <http://www.Nationalinfra/Localinfra_
b/Installation2/2017/017000206>. In these examples,
URIs share the same prefix because installation1 and -2 belong to
the same national infrastructure <http://www.National
infra/>, followed by the identification of the local infrastruc-
ture Localinfra_a or _b, the installation considered and then the
year, experiment and plant or plot identification (see PHIS
Vocabulary menu for standardized definitions of these terms).
URIs can be accessed from any web service client, thereby allow-
ing the different objects involved in each experiment to be unam-
biguously and specifically identified (i.e. installation, plot, plant,
plant organ, plant sample, sensor, variable).

Links between objects, between events and between traits
via semantic graphs and ontologies used in PHIS

The relations between objects need to be represented adequately
in a high-throughput context. For instance, if thousands of sam-
ple tissues have been collected on different leaves of different
plants, the information ‘sample 884 belongs to the leaf 7 of plant
736’ may be lost if kept in a spreadsheet. The same occurs for the
information that plant 736 has been moved from the position

(x1, y1) to (x2, y2) during the experiment, making it impossible to
connect this plant to local environmental conditions it has experi-
enced over time. Semantic graphs (Berners-Lee et al., 2001) allow
automatic retrieving of this information (Fig. 4). The innumer-
able combinations of objects and events during an experiment are
represented with parsimonious information, based on transitivity.
For example, the notions ‘samples 331 to 333 belong to plant
736’ and ‘samples 331 to 333 have been collected on individual
leaves 883 to 885’ are represented via a single predicate
<isPartOf> . Because of the transitivity in semantic graphs, the
system connects these samples to all objects already connected to
plant 736. For instance, the information that samples come from
an experiment of 2016 in *Installation1* belonging to a national
infrastructure and that they belong to a plant of variety A are
automatically retrieved via the predicates <participatesIn> and
<hasVariety> respectively (Fig. 4). The information that the sam-
ple comes from a plant that moved from position (x1, y1) to
(x2, y2) on day *i* is automatically retrieved via the link to plant
736, itself related to an event via the predicate <concerns> and the
subject <moveTo328> that provides the date, site and old and
new positions in the glasshouse.

The links between objects in Fig. 4 are based on two applica-
tion ontologies proposed here, and compliant with the standards
of OWL. The Ontology for Experimental Phenotypic Objects
(OEPO) describes objects involved in phenotyping experiments
(e.g. infrastructure, devices, germplasm, scientific objects) and
defines specialization hierarchy between them according to the
specificities of the installations and experiments (see Notes S7 for
technical details). The Ontology of Experimental Events (OEEv)
characterizes events that occur during an experiment; for exam-
ple, moving of plants, dates of sowing, application of a given

<figure><img src="image_6.png" /><figcaption>**Fig. 3** An example on the use of unique resource identifiers (URIs) for identifying all the objects present in single images taken in (a) glasshouse and (b) field experiment. (b) Exemplification of the concept of gap fraction in Fig. 2 and text; namely, the proportion of sky that is viewed in this picture. In the glasshouse, an image <m3p:arch/2017/ic17002295855> of a given plant <m3p:arch/2017/c17000118> that is placed in a pot <m3p:arch/2013/pc13001542> and a cart <m3p:arch/2013/ct1300123> is acquired in a cabin <m3p:arch/2018/ac180015> with an RGB camera <m3p:arch/2018/ac180019>. Note that Localinfra_a and installation1 in the text are represented by M3P and arch here, to match with supplementary information. In the field, an image <diaphen:2017/ic14001480237> of a plot <diaphen:2017/o1700029> containing a plant diaphen:2017/17000147> and a leaf <diaphen:2017/l17000590> is acquired using a hemispherical camera <diaphen:2018/ac180002>. The prefixes m3p: and diaphen: preceding URIs stand for <http://www.phenome-fppn.fr/m3p> and <http://www.phenome-fppn.fr/diaphen> respectively.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4** (a) Representation of a semantic graph describing the different objects involved in a phenotyping experiment and their relationships, using ontologies and semantics. (b) The relational instances between objects are formalized via semantic links using the resource description framework (RDF) data model that uses the triple data entity 'subject-predicate-object'. Elliptical boxes are the objects represented as instances, and arrows represent the relational instances formalized in ontologies. Boxes with different colours represent different object types. Note that this is a simplified representation for relations and names of objects (e.g. names of objects presented with labels instead of unique resource identifiers (URIs)). Full URIs of objects and RDF examples are exemplified in (b).</figcaption></figure>

treatment, harvesting, measurements or sampling for -omic mea-
surements, or any category of technical problem (see Notes S7).
For instance, the *Trouble* concept distinguishes *Breakdown* (sen-
sor or conveyor), *Dysfunction* (sensor fault, irrigation trouble)
and *Incident* (a pot falls down, a leaf is blocked in an imaging
cabin, lodging of a plot, human error, etc.). As described in the
associated semantic graph (Notes S7), an event can be associated
with objects (e.g. plant, plot, sensor) and with the user who has
annotated the event, and the occurrence date can be tracked
along with every relevant detail. This information can be
retrieved, plotted on graphs and used, for instance, to detect
anomalous data or to calculate new variables. For example, plants
are often transferred, during a single experiment, between instal-
lations or compartments with different environmental condi-
tions. Fig. 5 represents a case in which plant 262 is sequentially
monitored in two installations over 50 d (Fig. 5a,b). The experi-
ment takes place in Installation 1 for daily measurements of bio-
volume and transpiration, and then temporarily moved to
Installation 2 for 10 d for more precise measurements (19–29
May, blue area in Fig. 5a,b), before being harvested. Environ-
mental conditions sensed by plants differ between the two instal-
lations, so a proper tracking tool is essential. In our experience,
manual tracking is extremely tedious if environmental, pheno-
typic and management data are spread in distinct databases, lab
books or individual text/spreadsheet files, especially when differ-
ent groups are involved. As shown in the associated semantic
graph (Fig. 5d), tracking the presence of the plant 262 in Installa-
tion 2 can be formalized to automatically link environmental data
associated with a given plant in each installation, without the

need to specify the plant location at a given time (Fig. 5c). The
full example together with technical information is provided in
Notes S3.

It is noteworthy that the PHIS-specific OEPO and OEEv
application ontologies formalize the installation entities and allow
dynamic configuration of PHIS. These ontologies refer to objects
and events that can be specific to either field or controlled condi-
tions. The mapping of ontologies between types of installations
can be simple; for example, the x-y position of a plant in a
glasshouse exactly corresponds in the field to GPS coordinates.
Other objects and events can be specific; for example, a plant may
fall in controlled conditions, while lodging can occur in the field.
In this last case, there is no need to establish a correspondence
between events. Hence, OEPO and OEEv application ontologies
involve objects and events that are common to all installations,
whereas other objects and events are specific to one category of
installations and need to be defined by the groups that drive these
installations, but also require coordination and standardization
between installations. The (precise) ontologies OEPO and OEEv
have been mapped whenever possible to existing ontologies; for
example, the Ontology for Biomedical Investigations
(Bandrowski et al., 2016), the Plant Experimental Conditions
Ontology (http://purl.bioontology.org/ontology/PECO), the
Plant Ontology (http://plantontology.org/; Ilic et al., 2007; Walls
et al., 2012; Cooper et al., 2013), the Plant Phenotype Experi-
ment Ontology (http://purl.org/ppeo), and others such as the
AGROVOC (Caracciolo et al., 2013), the Relations Ontology
(Smith et al., 2005) and the Semantic Sensor Network Ontology
(http://purl.oclc.org/NET/ssnx/ssn). The FAO/Bioversity Multi
<figure><img src="image_8.png" /><figcaption>**Fig. 6** (a) Example of description in the Phenotyping Hybrid Information System (PHIS) of the widely used and standardized term 'normalized difference vegetation index' (NDVI) using the Crop Ontology 'trait-method-unit' data model and ontology mapping. (b) Description of the local term 'Silk_volume' derived from maize ear images captured in the glasshouse in PHIS using ontology mapping and local methods. The prefixes *oepo*, *skos* and *variable* preceding the unique resource identifiers stand for <http://www.phenome-fppn.fr/vocabulary/2018/oepo#>, <http://www.w3.org/2004/02/skos/core#> and <http://www.phenome-fppn.fr/m3p/variable#> respectively.</figcaption></figure>

Fig. 7(b,d), users 1 and 2 declared the incidents *event356* and *event849* that involved several plots or plants. A description of the event (text, image or video) can be attached; for example, ‘Plots lodged after the storm’ (Fig. 7b). This allows the user either to request the characteristics of a given plant or to request the list of all plots or of all leaves originating from plots that have suffered lodging in the experiment. The full example together with technical information is described in Notes S2.

Expert annotation during data analysis

Some anomalies can only be detected at the data processing step. For instance, a plant supposed to belong to a given genotype might grow much more slowly than its replicates. This can be automatically pointed out by classical clustering methods or

annotated manually after expert user validation (Bernal-Vasquez *et al.*, 2016). The latter is shown in Fig. 8, where a suspicious plant (probably related to seed contamination) is detected out of the four replicates of the same genotype in a common experiment after a visual analysis of either images (Fig. 8a) or growth curves (Fig 8b). Clicking on the curves in Fig. 8(b) allows the displaying of images of the four replicates. In this case, the user can see that the third replica, in addition to having smaller leaf area, presents a different architecture, thereby making it still more suspicious. Data annotation associated with specific algorithms is therefore a help to data cleaning.

As described by the semantic graph presented in Fig. 8(c), the expert user declares an annotation *Annotation338* that concerns the detected anomalous plant *plant816*. The OEPO and OEEV ontologies allow tracking this event by going backwards in order
to find the origin of the incident, which may be related to seed
contamination (Fig. 8c). In this example, the seeds used for this
experiment came from a particular seed lot (seedLotSample857),

generating the suspicion that other errors may have occurred in
the same seed lot. The information associated with this seed lot is
gathered in the information system via the web interface. More

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 7** Examples of data annotation in the Phenotyping Hybrid Information System for describing (a) a lodging event occurring in the field (installation1) and (c) an accident during image acquisition in the glasshouse (installation2). (b, d) Informal semantic graphs representing the information associated with each event (users, installations, plots, plants, description) using the Ontology of Experimental Events ontology and the web annotation data model. Names in this figure are neutral for better legibility. Instances, classes and literals are depicted as coloured ellipses, white rectangles and white lozenges respectively. Relationships and properties are depicted as black lines. Note that this is a simplified representation. Corresponding names for consulting the online information system are rchapuis for 'Agent1' and 'lcabrera' for 'Agent2', 'diaphen' for 'installation1' and 'phenoarch' for 'installation2'.</figcaption></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 8** Example of expert annotation using the Ontology of Experimental Events ontology. (a) RGB side images of four replicates of the same genotype in a common experiment. (b) Plant leaf area curves resulting from image analysis of these four plant replicates. (c) Associated informal semantic graph that shows how an expert user Agent1 declares an expert annotation Annotation338 that concerns the detected anomalous plant plant816. Instances, classes and literals are depicted as coloured ellipses, white rectangles and white lozenges respectively. Relationships and properties are depicted as black lines. Names in this figure are neutral for better legibility. Corresponding names for consulting the online information system are lcabrera for Agent1, and <http://www.phenome-fppn.fr/m3p/arch/2017/c17000816> for plant816.</figcaption></figure>

<figure><img src="image_15.png" /></figure>
generally, images, videos and media files associated with any
event can be uploaded to the information system for further
interpretation of phenotyping data. The full example, together
with technical information and further examples, is provided in
Notes S2.

Advanced data querying, data visualization and scientific computing through interaction between the different component layers of PHIS

The user may want to retrieve 'plants with leaf area higher than 0.6 m² and/or plant height higher than 1500 mm', 'images of plots having suffered lodging' or 'show all sensors that display temperatures higher than 40°C' (Notes S4). This is accomplished by using the inference engine based on the semantics and rules represented in the OEPO and OEEv ontologies, thereby linking the knowledge stored in the Triple Store to the information distributed among the different storage systems. Interaction between different layers of PHIS (Fig. 1) also provides advanced visualization features for displaying images, dynamic graphs of static or

time courses of phenotypic and environmental variables that are
automatically adapted to the particular experimental settings, and
variables such as glasshouse (Fig. 9a) or field (Fig. 9d). For
instance, the user may request a dynamic visualization of image
analyses and watering results based on different filtering options
(image angles, genotypes, plants, treatments; Fig. 9a). Such inter-
active figures allow the exploration of dynamic variables over
time (e.g. plant area or water use). Graphs can be zoomed into a
particular time window: clicking on a data point automatically
displays the images associated with this point together with the
associated annotations (Fig. 9b,c). Raw images, segmented
images and metadata can be displayed in both field (Fig. 9d) and
glasshouse experiments (Fig. 9e). The interaction with the knowl-
edge layer allows the projection of variables using GPS coordi-
nates associated with plants or plots in field experiments (Fig 9d)
and linking data with environmental sensor outputs.

PHIS includes extensible scientific computing modules based
on R packages for calculating elaborated variables and generating
experimental summaries and reports. Automatic reporting is
based on data query through the interface and R integration in

<figure><img src="image_16.png" /><figcaption>**Fig. 9** Examples of advanced data visualization. (a) Data querying for dynamic visualization of traits resulting from image analyses based on different filters (object type, varieties, plants, image angles). (b) Interactive graphics for exploring dynamic variables over time (silk volume and image thumbnails) and annotating points with comments and events. (c) Pop-up of an ear image corresponding to a particular data point in the graph. Raw images, segmented images and metadata can be displayed in both (d) field and (e) glasshouse experiments. (f) The interaction with the knowledge layer allows projection of variables using GPS coordinates associated with plants or plots and sensors in field experiments.</figcaption></figure>
text processing (R Markdown). Basic reports include daily,
weekly or final overviews of experiments, with standard statistics
and graphic visualization of averaged queried traits. Extended
reports may include specific calculation of traits and environmen-
tal conditions associated with a given plant or genotype. In par-
ticular, new variables can be calculated for a given experiment
based on the joint use of phenotypic and environmental data
together with associated metadata to plants and traits (e.g. events,
methods). For instance, the progression of thermal time for each
individual plant or plot can be calculated taking into account the
local air or leaf temperatures, the dates of sowing and the method
to perform such calculation (see Notes S4).

Integration of external data and interoperability with external installations and resources

The dialogue with external applications and information systems is managed through the web service API that allows integration of data from external databases and resources, export to computing and modelling platforms and integration of phenomic data into other systems. Web services provide flexible and powerful capabilities for the integration of a diverse and multi-source amount of data, including structured environmental and phenotypic data acquired by the different sensors of the installation (stored in a POSTGRESQL database), the images stored in the iRODS system, nonstructured data such as elaborated variables stored in the MONGODB, and rich metadata and knowledge stored in the Triple Store (Notes S6). The versatile use of web services allows one to virtually integrate data from any external client, therefore allowing PHIS to be adapted into other infrastructures. For instance, environmental data from a network of field meteorological stations (http://w3.avignon.inra.fr/carto/) and soil sensors (http://www.agriscope.fr/) are integrated in PHIS via the web service API. Similarly, features extracted from image analyses via Python are routinely integrated in PHIS through the web services, and an R client allows different analysis pipelines (Notes S6).

PHIS can also export data to external databases, in particular
those dedicated to genetic analyses or modelling. For example,
export to the GNPIS information system (https://urgi.versailles.
inra.fr/gnpis; Steinbach et al., 2013), a member of the ELIXIR
European infrastructure, allows genome-wide association studies
based on the phenotypic datasets organized in PHIS via the col-
laborative Breeding API. Export to the modelling platform
OPENALEA (Pradal et al., 2008, 2015) has allowed calculation of
the light interception and radiation-use efficiency of hundreds of
maize plants using data obtained in the M3P installation (Cabr-
era-Bosquet et al., 2016). Finally, data search and advanced
queries can be performed to remote databases thanks to the web
service API and the inference engines that use the semantics and
rules represented in the ontologies. These import and export APIs
facilitate the interoperability and data sharing capabilities in the
context of Open Data Science (Halewood et al., 2018); for exam-
ple, in the context of European projects EPPN2020 (https://epp
n2020.plant-phenotyping.eu/) Trans-PLANT (http://transpla
ntdb.eu/), ELIXIR-EXCELERATE (https://www.elixir-europe.

org/excelerate/plants) or EMPHASIS (http://emphasis.plant-phe
notyping.eu/).

Taken together, the functionalities present in PHIS may allow assembling a number of datasets from different installations in controlled and field conditions (including phenotypic, environmental and contextual information), resulting in an unprecedented amount of information that can be reused, combined and reanalysed to generate new knowledge. This can be of particular interest for covering the necessities of most phenotyping installations not having the appropriate tools for storing, organizing and managing phenomic data, as well as for data management strategies for networks of installations. Nevertheless, PHIS can also be adapted to local software and databases and used as a mapping layer enabling interoperability between information systems.

Acknowledgements

This work was supported by the ‘Infrastructure Biologie Santé’ PHENOME-EMPHASIS project (ANR-11-INBS-0012) funded by the National Research Agency and the ‘Programme d’Investissements d’Avenir’ (PIA). Arnaud Charleroy, Guilhem Heinrich, Morgane Vidal, Alexandre Mairin, Isabelle Nembrot and Pierre-Étienne Alary are acknowledged for their contributions to software and documentation development. We are grateful to Claude Welcker for designing experiments and to all members at the DIAPHEN and M3P platforms for providing technical support, conducting the experiments and collecting data. France-Grilles and Patrick Moreau are acknowledged for providing computing resources on the French National Grid Infrastructure.

Author contributions

P.N., A.T., N.H., F.T. and L.C-B. planned and designed the research, A.T. designed the software architecture and J.M-C., V.N., I.S. and N.B. contributed to software development, C.P. tested web services, L.C-B. and R.C. performed the experiments, and L.C-B., R.C. and N.B. analysed data. P.N., A.T., N.H., B.C., F.T. and L.C-B. wrote the manuscript.

ORCID

Cyril Pommier http://orcid.org/0000-0002-9040-8733

François Tardieu http://orcid.org/0000-0002-7287-0094

Llorenç Cabrera-Bosquet http://orcid.org/0000-0002-2147-2846

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**Supporting Information**

Additional Supporting Information may be found online in the
Supporting Information section at the end of the article:

**Notes S1** Experimental Organisation menu of PHIS web user interface.

**Notes S2** Annotations and events.

**Notes S3** Object tracking menu of PHIS web user interface.

**Notes S4** Data menu of PHIS web user interface.

**Notes S5** Tools menu of PHIS web user interface.

**Notes S6** Web Service API.

**Notes S7** OEPO and OEEV ontologies and annotations.

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