# Near-infrared spectroscopy applications for high-throughput phenotyping for cassava and yam: A review

Emmanuel Oladeji Alamu, Ephraim Nuwamanya, Denis Cornet, Karima Meghar, Michael Adesokan, Thierry Tran, John Belalcazar, Lucienne Desfontaines, Fabrice Davrieux

► To cite this version:

Emmanuel Oladeji Alamu, Ephraim Nuwamanya, Denis Cornet, Karima Meghar, Michael Adesokan, et al.. Near-infrared spectroscopy applications for high-throughput phenotyping for cassava and yam: A review. International Journal of Food Science and Technology, 2020, 10.1111/ijfs.14773. hal-02947924

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# SHiNeMaS: a web tool dedicated to seed lots history, phenotyping and cultural practices

Yannick De Oliveira<fnref n="1" />*, Laura Burlot<fnref n="1" />,², Julie C. Dawson³, Isabelle Goldringer<fnref n="1" />, Darkawi Madi<fnref n="1" />, Pierre Rivière⁴, Delphine Steinbach<fnref n="1" />, Gaëlle van Frank<fnref n="1" /> and Mathieu Thomas⁵,⁶

**Abstract**

**Motivation:** In 2005, researchers from the French National Research Institute for Agriculture, Food and Environment (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement, INRAE) started a collaboration with the French farmers' seed network Réseau Semences Paysannes (RSP) on bread wheat participatory breeding (PPB). The aims were: (1) to study on-farm management of crop diversity, (2) to develop population-varieties adapted to organic and low-input agriculture, (3) to co-develop tools and methods adapted to on-farm experiments. In this project, researchers and farmers' organizations needed to map the history and life cycle of the population-varieties using network formalism to represent relationships between seed lots. All this information had to be centralized and stored in a database.

**Results:** We describe here SHiNeMaS (Seeds History and Network Management System) a web tool database. SHiNeMaS aims to provide useful interfaces to track seed lot history and related data (phenotyping, environment, cultural practices). Although SHiNeMaS has been developed in the context of a bread wheat participatory breeding program, the database has been designed to manage any kind and even multiple cultivated plant species. SHiNeMaS is available under Affero GPL licence and uses free technologies such as the Python language, Django framework or PostgreSQL database management system (DBMS).

**Conclusion:** We developed SHiNeMaS, a web tool database, dedicated to the management of the history of seed lots and related data like phenotyping, environmental information and cultural practices. SHiNeMaS has been used in production in our laboratory for 5 years and farmers' organizations facilitate their own information in the system.

**Keywords:** Database, Plant breeding, Software

**Background**

Due to a lack of effort in breeding varieties adapted to organic and low-input agriculture[1] farmers currently have limited access to the proper crop diversity. Cultivated biodiversity has declined in the landscape for decades as a result of the industrialization of agriculture[2].

For these reasons, there is a need to deploy a cultivated diversity adapted to diverse practices and contexts.

In order to create diverse heterogeneous varieties of bread wheat adapted to farmers' practices and needs, researchers from the French National Research Institute for Agriculture, Food and Environment (INRAE) and the wheat group of the French farmers' seed network Réseau Semences Paysannes (RSP) started a collaborative program in 2005. The aims of this program are (i) to study on-farm management of crop diversity[3], (ii) to develop population-varieties adapted to organic and low-input agriculture in the context of a participatory

<footnote>*Correspondence: yannick.de-oliveira@inrae.fr</footnote>

<footnote n="1">¹ Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France</footnote>

<footnote>Full list of author information is available at the end of the article</footnote>
<figure><img src="image_3.png" /></figure>

plant breeding program involving a collective of farmers, facilitators and researchers[4, 5] and, (iii) to co-develop methods and tools such as data management tools, experimental designs and statistical methods adapted to on-farm experiments[6, 7], in order to foster genetic and social innovations.

In this context, participants have (i) developed, evaluated, and measured at global and individual levels new population-varieties[6, 8, 9] and (ii) fostered seed and knowledge circulation among participants in a large network of farms (more than 100 farms in 2018). This program therefore produces a lot of data, and, in particular, relational information between seed lots (traceability) that can be represented as a network (Fig. 1). Thus several dozen users involved in the program, the researchers and facilitators needed a tool adapted to the monitoring of decentralised experimentation, accessible online and with user-friendly interfaces, to facilitate the management of the data produced. Moreover, it is important for us to develop a tool distributed under a free licence, free of charge and using free technologies.

Some tools we identified like PlantDB[10], Phytotracker[11] or SeedUSoon[12] manages seed lots and their stock information but doesn't fit our requirements as they are not adapted to field experimentation.

For instance, PlantDB is mainly related to genotyping data management but doesn't provide a system to collect data from the field. Phytotracker provides features to design plant crosses but no additional data (trait description) can be recorded. Moreover, Phytotracker and PlantDB use proprietary technologies and are operating system dependant. SeedUSoon fill this technological gap as it uses Java and MySQL DBMS, but it offers a limited number of scenarios. For instance, mixture, diffusion or selection can't be handled in this tool. Finally, none of them provide feature to load data from a file, a mobile app or a web application making these tools unsuitable for field experimentation. Another one, GnpIS[13], manages seed lots and germplasm, but doesn't ensure seed lots traceability, it doesn't provide interfaces for data management and consequently, can't be considered as a data management tool. The last, the Breeding Management System[14] provides a lot of useful features but does not ensure complete traceability of seed lots and a part of its component are under proprietary licence. To fill the gaps in these tools and to avoid expensive work to improve one of them to fill our requirements, we developed SHiNeMaS (Seeds History and Network Management System), a web tool to manage the network and history of seed lots and related data.
<figure><img src="image_4.png" /></figure>

offline mode is not a problem as all information needed is stored in the file and accessible in offline mode.

Also, a reproduction file can be used to delete (Fig. 2, step 5) a set of reproduction events. These events can be deleted alone or in cascade, which means that the event itself, all the subsequent events and all the data are deleted.

### Prepare files with wizard tools

Because preparing a large file in a spreadsheet software can be a fastidious task, SHiNeMaS provides a helpful assistant to the creation of files using the seed lots or relations already recorded in the database (Fig. 2, step 3). By executing successive queries from a simple form, a user can build a list of seed lots or relations and export it in one of the file format described below. Thus, the user will only have to fill the remaining cells (Fig. 2, step 4) before the next submit.

### Data management with single form

SHiNeMaS provides form interfaces to create single relation events (Fig. 3 shows a form to create a new reproduction event). As for the files, a different form has been designed for each event type. When creating a new single event, the user can also add all the data measured, like

when submitting a file. Forms are also provided to manage any other type of information recorded in the database (projects, variables, methods, etc.).

### Data querying

SHiNeMaS provides query interfaces to retrieve information about seed lots such as (1) parentage, (2) generation number (on the current location and in total) and the quality of information, (3) data regarding variables measured during a given period of time. The query can be filtered according to various criteria and the results are displayed in three tables showing the data related to seed lots, the data related to the relations and individuals data.

From any query result, the user can access the seed lot card (Fig. 4). It displays information about the seed lot (its name, stock location, creation year, germplasm etc.), how it was created and used and its stock evolution. The stock is computed using initial quantity of the lot, all quantities used in relations as a parent and the inventories of the stock. The user can also access to the relationship card (Fig. 5) between two seed lots. It displays all the data that has been measured for this event and the associated seed lots. From any card the data tables can be downloaded in tabulated text file.
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Reproduction creation form. Form to create a new reproduction. The form is divided in three fieldsets. The first one requires sown seed lot information. The second requires to fill the reproduction information. The last one contains the data related to the reproduction</figcaption></figure>

Lastly, SHiNeMaS provides a global search bar enabling
a quick access to the seed lots, relations or germplasms
cards. This search bar is implemented with an auto-com-
plete feature.

Support and community

SHiNeMaS source code is hosted on the French national
forge for research and higher education, SourceSup<fnref n="3" />.
Through this forge, our aim is to provide tools to the
community such as an issue tracker, forums or wiki site.
This forge provides anyone who wants to contribute to
the development of our project the usual tools of popular
forges.

Currently, the web interfaces of SHiNeMaS are availa-
ble in English or French but, with the help of the commu-
nity, it would be easy to translate the application in any
other language. A virtual machine is also available to run
SHiNeMaS' tutorial<fnref n="4" />.

Future developments

Major future developments of SHiNeMaS will focus on
the management of new data types (climatic data and
plant pictures). In addition to storing data on seed lots
and relations between seed lots, it will be possible to
store data on germplasm. A web service will be devel-
oped to provide features ensuring interoperability with

<footnote n="3">³ https://sourcesup.renater.fr/.</footnote>

<footnote n="4">⁴ https://sourcesup.renater.fr/wiki/shinemas/en:vmtuto.</footnote>
<figure><img src="image_6.png" /></figure>

external tools such as R packages[7]. Efforts will also be made in terms of controlled vocabulary by complying with the Plant Science community of Elixir<fnref n="5" /> and we aim to implement methods of the Breeding API<fnref n="6" />. The

version 2.0 of this API defines new methods related to seed lot and seed lot transactions which would be relevant for our tool. Moreover, we will ensure an audit of submitted files to keep track of the submissions. In a future version of SHiNeMaS, we will focus on the use of controlled vocabulary and ensure the interoperability with standard ontologies. Using the same user role for

<footnote n="5">⁵ https://elixir-europe.org/communities/plant-sciences.</footnote>

<footnote n="6">⁶ https://www.brapi.org/.</footnote>
Relation profil : RXY_PEM_2015_0001 --> RXY_PEM_2016_0001

Relation information

Relation type : Reproduction

X : 1

Y : 2

Block : 1

Quantity used : 100 g

Split : 1

Description : reproduction

Kernel number : None

Realised : None

Start date : 2015

End date : 2016

Reproduction method name :

Description : None

Other relations :

Data

<figure><img src="image_7.png" /></figure>

Individual data

<table><thead><tr><th>Individual</th><th>height (T11)</th><th>ear_weight (T11)</th><th>awns (T11)</th></tr></thead><tbody><tr><td>1</td><td>114 (April 18, 2016, plant height measurement)</td><td>25 (April 18, 2016, ear weight measurement)</td><td>4 (April 18, 2016, awns estimation)</td></tr><tr><td>2</td><td>110 (April 18, 2016, plant height measurement)</td><td>24 (April 18, 2016, ear weight measurement)</td><td>4 (April 18, 2016, awns estimation)</td></tr><tr><td>3</td><td>108 (April 18, 2016, plant height measurement)</td><td>26 (April 18, 2016, ear weight measurement)</td><td>5 (April 18, 2016, awns estimation)</td></tr><tr><td>4</td><td>125 (April 18, 2016, plant height measurement)</td><td>27 (April 18, 2016, ear weight measurement)</td><td>4 (April 18, 2016, awns estimation)</td></tr><tr><td>5</td><td>111 (April 18, 2016, plant height measurement)</td><td>24 (April 18, 2016, ear weight measurement)</td><td>5 (April 18, 2016, awns estimation)</td></tr><tr><td>6</td><td>98 (April 18, 2016, plant height measurement)</td><td>22 (April 18, 2016, ear weight measurement)</td><td>6 (April 18, 2016, awns estimation)</td></tr><tr><td>7</td><td>118 (April 18, 2016, plant height measurement)</td><td>20 (April 18, 2016, ear weight measurement)</td><td>4 (April 18, 2016, awns estimation)</td></tr><tr><td>8</td><td>111 (April 18, 2016, plant height measurement)</td><td>21 (April 18, 2016, ear weight measurement)</td><td>5 (April 18, 2016, awns estimation)</td></tr><tr><td>9</td><td>109 (April 18, 2016, plant height measurement)</td><td>26 (April 18, 2016, ear weight measurement)</td><td>4 (April 18, 2016, awns estimation)</td></tr><tr><td>10</td><td>126 (April 18, 2016, plant height measurement)</td><td>25 (April 18, 2016, ear weight measurement)</td><td>8 (April 18, 2016, awns estimation)</td></tr></tbody></table>

<figure><img src="image_8.png" /></figure>

Global data

<table><caption>**Fig. 5** Relation card. The relation card is divided in two frames: (1) The information about this relation. (2) The data related to this relation at global and individual level</caption><thead><tr><th>Variable</th><th>Value</th><th>Date</th><th>Method</th></tr></thead><tbody><tr><td>sowing_density (T1)</td><td>84</td><td>Nov. 21, 2015</td><td>seeding area dentisy measurement</td></tr><tr><td>sowing_type (T1)</td><td>mechanic</td><td>Nov. 21, 2015</td><td>seeding process description</td></tr><tr><td>tkw (T1)</td><td>111</td><td>June 19, 2016</td><td>grain weight measurement</td></tr></tbody></table>
database administration and data management can be a risk if the data manager is not aware of all these privileges. To fill this gap we will improve the data access control and define more user roles in a further SHiNeMaS release.

## Conclusion

We developed SHiNeMaS to ensure seed lots traceability and to manage phenotypes, environments and cultural practices data for various genetic resources, varieties of many crop species. This web application is a key feature as it enables data administration at different scales (files, or single forms). SHiNeMaS is especially adapted for field experimentation in a network of farms or experimental stations and that requires tracking seed lots history. It is available under AGPL licence and uses free technologies. SHiNeMaS has been used in production in our laboratory for 5 years and some farmers' organizations facilitate their own information in the system.

## Supplementary information

**Supplementary information** accompanies this paper at https://doi.org/10.1186/s13007-020-00640-2.

**Additional file 1.** Database schema. Relational schema of SHiNeMaS' database.

## Acknowledgements

The authors thank Eva Dechaux, Elodie Gras, Johann Joets, Sophie Pin and Mélanie Polart-Donat for testing, developing or advising along this work. The authors thank Marissa Nix for reviewing this manuscript.

## Authors' contributions

YDO, LB and DM produces the code source of the software. PR, MT, GVF, IG, JD and DS contributed to the conception of the tool. They also contributed to the writing of the user documentation, translation, and tutorial. All authors read and approved the final manuscript.

## Funding

This work has been supported by INRAE. It has been funded by (1) the European Community's Seventh Framework Program (Grant Agreement No 245058, Solibam), (2) the European Union's Horizon 2020 research and innovation program (Frant Agreement No 633571, DIVERSIFOOD), (3) the Fondation de France, the French Region Centre (SPEAL project), the French Ministry of Agriculture and Food (ProABioDiv and Covalience CASDAR projects).

## Availability

Project name: SHiNeMaS.
Project home page: https://sourcesup.renater.fr/projects/shinemas.
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Python3, Django 2.0.
License: AFFero GPL.
Any restrictions to use by non-academics: None.

## Ethics approval and consent to participate

Not applicable.

## Consent for publication

Not applicable.

## Competing interests

The authors declare that they have no competing interests.

## Author details

¹ Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France. ² AgroBioPerigord, 24000 Périgueux, France.

³ Department of Horticulture, University of Wisconsin–Madison, Madison, WI 53706, USA. ⁴ Réseau Semences Paysannes, 47190 Aiguillon, France. ⁵ CIRAD, UMR AGAP, 34398 Montpellier, France. ⁶ Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.

Received: 17 January 2020 Accepted: 15 July 2020

Published online: 23 July 2020

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