=Paper=
{{Paper
|id=Vol-2969/paper12-IFOW
|storemode=property
|title=Crop Planning and Production Process Ontology (C3PO), a New Model to Assist Diversified Crop Production
|pdfUrl=https://ceur-ws.org/Vol-2969/paper12-IFOW.pdf
|volume=Vol-2969
|authors=Baptiste Darnala,Florence Amardeilh,Catherine Roussey,Clément Jonquet
|dblpUrl=https://dblp.org/rec/conf/jowo/DarnalaARJ21
}}
==Crop Planning and Production Process Ontology (C3PO), a New Model to Assist Diversified Crop Production==
https://ceur-ws.org/Vol-2969/paper12-IFOW.pdf
Crop Planning and Production Process Ontology
(C3PO), a New Model to Assist Diversified Crop
Production
Baptiste Darnala1,2 , Florence Amardeilh2 , Catherine Roussey3 and Clément Jonquet1,4
1
LIRMM, University of Montpellier, CNRS, Montpellier, France
2
Elzeard, Cité du Numérique, 2 rue Marc Sangnier, 33310, Bègles, France
3
Université Clermont Auvergne, INRAE, UR TSCF, F-63000 Clermont–Ferrand, France.
4
MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France
Abstract
Crop planning and production process ontology (C3P0) is a representation of agricultural knowledge for
diversified crop production. It is composed of eight modules to take into account all the elements involved
in crop production as well as the complexity for representing farming practices and constraints. Especially,
the crop management module is sliced in three different layers to capture the generic information about
crop itineraries, the planned production process and the tasks eventually really achieved been by various
agents in the plots. To implement this ontology, we designed a new pipeline, presented here, integrating
various existing tools to ease the ontology documentation, population and maintenance tasks.
Keywords
ontology, agriculture, semantic resource, knowledge representation, crop itinerary
1. Introduction
Agricultural production practices are changing and crop production are facing multiple vul-
nerabilities: economic, regulatory, environmental and social (isolation, skills, overload, etc.).
Moreover, the pressure created by distribution chains and the consumers’ demand for better
and healthier vegetables, produced in local area, force farmers to improve crop management.
Several scientific studies [1, 2] have shown diversity –-both in time and space– of cultivated
plants:
• improves risks management with respect to weather and economic changing context;
• delivers a natural defence system against diseases and pest attacks;
• increases stability and resilience of agro-ecosystems.
IFOW 2021: 2nd Integrated Food Ontology Workshop, held at JOWO 2021: Episode VII The Bolzano Summer of
Knowledge, September 11-18, 2021, Bolzano, Italy
Envelope-Open baptiste.darnala@elzeard.co (B. Darnala); florence.amardeilh@elzeard.co (F. Amardeilh);
catherine.roussey@inrae.fr (C. Roussey); jonquet@lirmm.fr (C. Jonquet)
GLOBE https://www.elzeard.co/ (B. Darnala)
Orcid 0000−0002−6306−4437 (F. Amardeilh); 0000−0002−3076−5499 (C. Roussey); 0000−0002−2404−1582 (C. Jonquet)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
However, with new agroecological techniques, farmers’ workload, management complexity
and mental load also increase [3, 4]. Between 2019 and 2020, the SME Elzeard conducted over
150 interviews with farmers, agricultural advisors, teachers and researchers. The company
identified several technological barriers, among them the need for knowledge sharing and for
new operational tools to assist farmers in their daily crop management: optimizing their crop
rotations, their yields and finding alternatives to chemicals.
To address the previous identified need, Elzeard develops a web and mobile application to
assist farmers in their planning and production activities. In order to encode the knowledge
in the system and to provide right decision-support and adapted recommendations, Elzeard
have designed an ontological model for representing plot management and crop itineraries,
called Crop Planning and Production Process Ontology (C3PO). This work is done in collaboration
with the ANR D2KAB project consortium1 and the MesclunDurab consortium2 . The objective
of this ontology is to structure a knowledge graph about agroecological crop practices, that
will become a common good for the benefits of the various actors of the vegetable production
sector. The knowledge graph will be accessible via a Web portal, called La Serre des Savoirs
being currently developed. La Serre des Savoirs will leverage the information contained in
the knowledge base into a more usable way for the different actors of the food industry. The
C3PO ontology will also be published and shared on the AgroPortal ontology repository [5]
and adhere to the FAIR principles (Findable, Accessible, Interoperable, Reusable) [6].
In this paper, we present preliminary results about the design requirements and implementa-
tion workflow of the C3PO ontology. Section 2 covers related research regarding crop production
models; Section 3 explains the specificity of the diversified vegetable crop production repre-
sentation; Section 4 outlines the first version of the C3PO ontology; Section 5 describes the
ontology creation pipeline used to develop, document and populate the ontology with reference
data.
2. Related Work
Semantic Web technologies were chosen to represent the interlinked knowledge needed to
manage crop planning and production process in its digital application because of their two
key advantages. Not only do these technologies allow knowledge to be represented through
formal and logical semantics enabling native reasoning (the ontology), but they also allow this
knowledge to be interconnected with open linked data thanks to shared or aligned ontologies,
thus promoting data interoperability (graph database).
For designing the C3PO ontology, we firstly examined existing ontologies [7] that could be
reused to represent plant characteristics and crop itineraries, that is to say general information
on a sequence of farming practices to ensure optimal growth of all crops in a plot. Some
agronomic resources about plants or crops are already available in a semantic format like the
agroecological knowledge base GECO [8] or in various thesaurus and agronomic reference
ontologies like Plant Ontology [9], Crop Ontology [10], AGROVOC [11], TAXREF-LD [12]
or FrenchCropUsage [13]. Nevertheless, these semantic resources did not describe farming
1
http://www.d2kab.org/
2
https://maraichage.wixsite.com/mesclun
practices and their interactions from an agricultural production perspective.
Searching AgroPortal’s content for ”crop management plan” or ”crop itinerary”, returned
several results. The first one is the Durum Wheat ontology but it only contains ”itinerary” as a
< s k o s : C o n c e p t > , i.e. as a terminological definition. We also found a ”TechnicalItinerary” as a
< o w l : C l a s s > into MS2O but this ontology is now deprecated. Another ontology identified was
AgroRDF [14]. It represents and describes the farm practices as a set of technical processes.
Unfortunately some of their classes are only defined in German and there is no sequence of
processes or plan.
Lately we also discovered Agronomy Ontology (AGRO) [15]. The farming practices are
defined as a set of technical processes, named planned processes, in the context of agronomic
experiments. This ontology is based on Basic Formal Ontology (BFO) [16]. The main scope of
application of BFO seems to focus on the concrete representations of the real world and not
really on the abstract representations of the human mind that may help in decision making.
That is why AGRO is oriented towards the recording of precise observations concerning specific
agronomic experiments rather than towards the planning of agricultural production from
reference data. We are already planning future work to interconnect the definitions of common
F a r m i n g p r a c t i c e s with those of the Agronomic Ontology.
We also looked for for process representation from other domains, such as the W3C Recom-
mendation Prov Ontology [17]. Prov-O is a process-flow model in the sense that it traces the
provenance and evolution of A c t i v i t y concepts, interacting with Agents and other Entities. It
is a very interesting model because it brings a representation of agents, activities but also entity
collection, ordered or not, that can be very useful to represent a vegetable production. Indeed,
with Prov-O, we could represent the farm, its actors represented by the producers for example,
and the different activities realised on plants or in the plots. But after testing its implementation
on our use cases, we chose not to use Prov-O to represent crop planning and production process
as it does not allow a theoritical representation of activities with relative dates placed into the
calendar. We rather decided to use Prov-O to keep trace of the updates on the various entities
in the knowledge graph data.
We studied another process ontology dedicated to transformation processes in food science,
called Process and Observation Ontology (PO2) [18]. This ontology is also derived from BFO
principles and has the same drawbacks that AGRO. It was a good start for implementing
operational processes composed of steps, products, observations, measures, and agents. But it
was not adapted to distinguish the reference data about the crop itineraries from the observed
data in a specific plot.
Recently, we discovered Valueflows3 , an ontology that describes flows of economic value
according to a three layers representation (Knowledge, Plan, Observation). As described in
Section 4, we used the same three-layers division to design the ontology part about crop
management process. To the best of our knowledge, this is the first conceptualisation of a
sequence of cultivation processes associated with a production plan linked to some generic
knowledge about those plans.
3
https://lab.allmende.io/valueflows
3. Representing Diversified Vegetable Production
The workflow of vegetable production and farm management requires a high level of knowledge
with regards to production techniques, especially to adapt to the on-going agroecological
transition. Moreover, with increasing regulatory standards and consumer pressure, it becomes
very complicated for farmers to produce diversified, tasty and healthy vegetables without too
many economic losses. It is therefore necessary to relieve the mental burden of farmers by
identifying all the knowledge related to agricultural techniques that allow them to organise, in
time and space, the schedules of each of the crops as well as their association and rotation over
a given season.
To face this challenge, the digital services offered by Elzeard to vegetable farmers rely on the
exploitation of the technical crop itineraries. A crop itinerary can be defined as a generic process
consisting of a succession of technical tasks and recommendations for agricultural production.
For a given crop, the crop itinerary has to be specialised according to the cultural context
and the farmer’s cultivation methods. Elzeard’s decision support system assists farmers in
the adaptation from a generic crop itinerary to a custom-made one, therefore optimizing crop
production.
The scope of the Crop Planning and Production Process Ontology (C3PO) presented in
this paper therefore targets information about the various possible crop itineraries and their
dependence with the whole agro-ecosystem. It has to take into account the general information
about the crops and cultivars but also the constraints about the farming system (organic or
conventional), the location of the farm along with the soil and climate conditions, the client
orders, the crop rotations and associations, the farming equipments, the potential diseases or
aggressors and crop protection solutions, etc.
4. Ontology Modelling
As the scope of this ontology is quite large and complex, we have chosen to split it into eight
modules where each part defines a particular need of crop production. The modules interact
with each other to represent the entire planning and production process on a farm, from seed
selection to crop delivery. Moreover, in order to be interoperable according to FAIR principles,
the modules have been linked to external ontologies for direct reuse or alignment wherever
possible as described in Figure 1.
• The Plant module represents cultivated plants (e.g., ”Carrot” vegetables), their botanical
family (e.g., ”Apiaceae”), their usage category (e.g., ”Root vegetable”) and the different
cultivars (e.g., ”Nantaise”) with their characteristics like the earliness, size, colour, shape.
This module reuses the French Crop Usage thesaurus [13] for vernacular names, crop
usages and aligns with TAXREF-LD [12] for scientific names and taxonomic organization
of families and species and plant development stage with BBCH (Biologische Bundesanstalt,
Bundessortenamt und CHemische Industrie)4 .
4
http://ontology.irstea.fr/bbch/core#
Figure 1: Overview of the Crop Planning and Production Process Ontology.
• The Aggressor module defines potential diseases and pests of each cultivated plants with
their description and protection solutions. For this we will rely on the GECO (GEstion des
COnnaissance)5 ontology model and knowledge base. We are working with the GECO’s
team in D2KAB to document the model and align it with other resources such as Agrovoc,
Wikidata or TaxRefld to complete with additional data about those living organisms.
• The Treatment module contains the description of every inputs that enhances plant
growth or plant defence. For this we will rely on an OWL version of French ANSES’s
E-Phy inputs catalogue developed in D2KAB.
• The Plot module represents the spatial organisation of the farm, the division into plots
and even beds for diversified vegetable production, the different infrastructures present
on the farm like the irrigation equipment, etc. For this we have not yet identified an
external semantic resource that goes into sufficient details regarding the plot description
dedicated to diversified crop productions.
• The Admin module imports and links with existing ontology models such as FOAF,
Time Ontology, the Event Ontology or Prov-O, to represents agents, organisation or
administrative entities.
• The Supply Manager module organises the stocks and the product delivery. For this
module, we are discussing with the DataFoodConsortium6 to reuse part of their supply
chain ontology model, which they are currently also aligning on ValueFlows model.
• The Crop Management module represents all the crop production process. It is the core of
the C3PO ontology, importing all other modules needed to organise the crop production.
• The Vocabulary module, formalized in SKOS, organises reference values used for the
definitions of class instances, like units of measures (aligned with QUDT definitions),
5
https://geco.ecophytopic.fr/
6
https://www.datafoodconsortium.org/en/
the climate choices, the farming practices, the earliness levels and so on. Each time it is
possible, reference values are aligned to AGROVOC concepts.
The Crop Management module is the most complex. To better take into account the crop
planning and production process, we need to represent crop itineraries from different perspective:
the generic theoretical information as advised in agricultural literature –how tomatoes should be
grown under various constraints; the planned process –how a farmer plan to produce tomatoes
according to its own constraints; the observation data –how a farmer eventually produced
tomatoes taking into account possible hazards.
Those three dimensions better capture changes between plans and results in order to opti-
mise production from one year to another. Additionally, we want to encourage the sharing
of observation data between farmers to update theoretical knowledge if needed to propose
adaptations to climate change for example.
To describe the process according to those separate layers we get inspiration from the Value-
Flows model, itself inspired by the REA ontology [19] about economic phenomena occurring
in Enterprise Information System. REA introduced a distinction between Knowledge Infras-
tructure and Operational Infrastructure whereas ValueFlows model goes further distinguishing
the Knowledge level (core concepts, guidelines, recipes, procedures), the Plan level (offers,
requests, schedules and plans) and the Observation level (what really happened). This three-
layer representation completely fits our requirements for modelling the crops’ operational
planning and production process. Figure 2 describes the production flow with an example. The
Knowledge Level describes the generic concepts about plants, technical operations that are part
of a itinerary procedure, implementing specific farming practices organised as a hierarchy, the
various contextual conditions that may affect the expected yield, the relative periods of the
different steps of the process (germination, plantation, maintenance, harvest, conservation), etc.
Then the Plan Level represents the production process that is deduced from the itinerary proce-
dure, the cultivars used as a seed material, the planned operational tasks, from soil preparation
to harvest, according to market opportunities, delivery dates and volumes, etc. Moreover, in a
diversified crop production, such as for the vegetables, it is important to take into account the
fact that a production process can be sliced into various series of the same crop in order to have
longer lasting harvests. A farmer repeats more or less the same tasks of a production process,
at different dates to benefit from a continued flow of production. Lastly, the Observation Level
declines the planned operational tasks into activities that are assigned to a specific location
(plots and/or beds), persons or teams and at a certain date. It gives an historical view of the
achieved task with complementary data about the equipment used, the inputs dose or volumes,
the time spent to do the activity, etc. It also represents observation data (weather conditions,
hazards, presence of an agressor, and so on).
5. Pipeline of Ontology Production
To start implementing our ontology, we used the Linked Open Terms (LOT) [20] methodology
as it proposes an agile and iterative approach to ontology development. First, we worked with
several domain experts to identify a set of competency questions along with the functional
Figure 2: Example of a production process with concepts represented in the three layers of the C3PO
module
specification of Elzeard’s application. For example, ”what are the ordered technical tasks to
cultivate organic tomatoes in Mediterranean climate?”.
We gathered additional requirement specifications from various domain-oriented text books,
guidelines procedures, crop datasets or dedicated knowledge bases such as GECO. We also chose
to reuse existing ontologies and thesauri in order to avoid reinventing the wheel and facilitate
the interoperability of our model, either upper-level metadata vocabularies such as FOAF,
EVENT Ontology or OWL-TIME or domain-specific ontologies such as FCU or TAXREF-LD.
At first, to model the onotlogy we used a graphical model by using the online graphic tool
”Draw.io”7 to ease the discussions with the domain experts. Then, we had to manually transcript
the concepts’ definitions of this graphical model in an ontology editor such as Protégé [21]. Not
only this method is prone to human error when transcribing the ontology model but it may
lead to serious problems when keeping the graphical model up to date, still useful to provide an
overview of the ontology model. It appeared that some modifications made into Protégé were
not always updated on the graphical model. To counter this problem, an ontology creation
pipeline was built by combining different existing tools in the same workflow.
The first tool of that pipeline is still Draw.io but we added Chowlk [22], a graphical extension
7
https://www.draw.io
Figure 3: C3PO’s pipeline for ontology production.
for an ontology representation. Thanks to a standardized graphical notation to represent
ontological classes, properties and restrictions, Chowlk automatically transcribes Draw.io’s
XML output into an OWL representation with the Turtle syntax. Then, the generated TTL file
can be imported into Protégé for verification and validation by the ontologist. Then the ontology
model can be imported in a RDF triple store. GraphDB8 has been chosen for its performance
and reasoning capabilities as the RDF storage of the future Knowledge Base. Moreover, to
simplify the documentation process, GraphDB includes OntoRefine, an integrated upgraded
version of the open-source tool OpenRefine9 . It enables data transformation for converting
tabular data (e.g. CSV) into RDF data. There are two uses for this tool. The first one is to import
the concepts and properties definitions (all labels, comments and other annotation properties)
that are maintained outside the Draw.io to make the graphical model easier to read. The second
one is to populate the data graph with knowledge provided by domain experts in tabular files
as it is often more convenient for them. Once the ontological model is complete, the last tool
used is Widoco [23] to automatically generate all the ontology documentation.
The C3PO ontology documentation is currently being finalised and will be published at its
namespace URL10 . Meanwhile, the C3PO’s owl files are available at Serre des Savoirs project’s
GitLab repository11 to collect future contributions and revision requests.
6. Conclusion
To comply with operational needs for Elzeard’s digital application, we modelled a new ontology
for representing Crop Planning and Production Process dedicated to diversified crop production.
8
https://www.ontotext.com/graphdb
9
https://openrefine.org/
10
http://www.elzeard.co/ontologies/c3pos
11
https://gitlab.com/serre-des-savoirs/c3po
Even if we tried to reuse existing thesaurus and agronomy ontologies, there was no existing
ontology that captured the specific need of representing crop itineraries from the perspective of
the farmers’ production processes. The C3PO ontology is used to construct a knowledge base
for the storage of the Elzeard web application data and into la Serre des Savoirs, a web portal that
will give access to extensive knowledge about plants and their crop itineraries on a practical
way for the agriculture actors. Various domain expert partners were included in our approach to
question and identify the main concepts and properties: scientists (D2KAB, MESCLUNDURAB)
and agricultural professionals (crop farmers, networks of agricultural advisors, teachers.). A
pipeline was initiated and tested to ease the different steps of ontology creation, documentation
and population, integrating various independent tools. In future work, we will align our
concepts and properties to BFO models, such as Agronomy Ontology or PO2. We will also
publish the ontology model according to the FAIR principles on AgroPortal and our GitLab
for tracking issues and new contributions. For the first time, an ontology with information
oriented on French farming practices is developed, both in French and in English. However, the
ontology is intended to become a global open knowledge base about all farming practices and
crop itineraries related to diversified crop production such as market gardening, arboriculture
or agroforestry.
Acknowledgments
This work was partially achieved with support of the Data to Knowledge in Agronomy and
Biodiversity (D2KAB – www.d2kab.org) project that received funding from the French National
Research Agency (ANR-18-CE23-0017). We also acknowledge support from the National Office
for Biodiversity with MesclunDurab grant and the Nouvelle-Aquitaine Region with ”Social
Innovation AMI” and ”Digital Prototypes” grants. We also thank Dr. Kevin Morel for his
feedback and all contributors from MesclunDurab and D2KAB projects.
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