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

Neveu, Pascal; Tireau, Anne; Hilgert, Nadine; Nègre, Vincent; Mineau-Cesari, Jonathan; Brichet, Nicolas; Chapuis, Roland; Sanchez, Isabelle; Pommier, Cyril; Charnomordic, Brigitte; Tardieu, Francois F.; Cabrera‐Bosquet, Llorenç

doi:10.1111/nph.15385

Cited by 66 publications

(56 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Large scale data management systems of national research collaborations such as the "Phenotyping Hybrid Information System" [23] or the "Brassica Information Portal" [24] were proposed in the academic field. These systems handle phenotypic and genomic data by collecting them in a central database and making it accessible to users within the community.…”

Section: Introductionmentioning

confidence: 99%

Plant, space and time - linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials

et al. 2020

View full text Add to dashboard Cite

Background: To ensure further genetic gain, genomic approaches in plant breeding rely on precise phenotypic data, describing plant structure, function and performance. A more precise characterization of the environment will allow a better dealing with genotype-by-environment-by-management interactions. Therefore, space and time dependencies of the crop production processes have to be considered. The use of novel sensor technologies has drastically increased the amount and diversity of phenotypic data from agronomic field trials. Existing data management systems either do not consider space and time, are not customizable to individual needs such as field trial handling, or have restricted availability. Hence, we propose an integrative data management and information system (DMIS) for handling of traditional and novel sensor-based phenotypic, environmental and management data. The DMIS must be customizable, applicable and scalable from individual users to organizations. Results: Key element of the system is a dynamic PostgreSQL database with GIS-extension, capable of importing, storing and managing all types of data including images. The database references every structural database object and measurement in a threefold approach with semantic, spatial and temporal reference. Timestamps and geo-coordinates allow automated linking of all data. Traits can be precisely defined individually or uploaded as predefined lists. Filtering and selection routines allow compilation of all data for visualization via tables, charts or maps and for export and external statistical analysis. New possibilities of environmental information-based planning of field trials, weatherguided phenotyping and data analysis for outlier or hot-spot detection are demonstrated. Conclusions: The DMIS supports users in handling experimental field trials with crop plants and modern phenotyping methods. It focuses on linking all space and time dependent processes of plant production. Weather, soil and management, as well as growth and yield formation of the plants can be depicted, thus allowing a more precise interpretation of the results in relation to environment and management. Breeders, extension specialists, official testing agencies and agricultural scientists are assisted in all steps of a typical workflow with planning, designing, conducting, controlling and analyzing field trials to generate new information for decision support in the crop improvement process.

show abstract

Section: Introductionmentioning

confidence: 99%

Plant, space and time - linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials

et al. 2020

View full text Add to dashboard Cite

show abstract

“…We tested 3D reconstruction analysis workflow by processing data of eight experiments (Table 1), in which 1680 plants were imaged daily for 30 to 40 days with either 3 or 13 images of each plant every day, obtained with two calibrated camera positioned on the side and the top of the plant. Each experiment involved up to 955,372 RGB images (Table 1) that are stored in the local instance of the Phenotyping Hybrid Information System (PHIS, Neveu et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

Artzet

Chen

Chopard

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

In the era of high-throughput visual plant phenotyping, it is crucial to design fully automated and flexible workflows able to derive quantitative traits from plant images. Over the last years, several software supports the extraction of architectural features of shoot systems. Yet currently no end-to-end systems are able to extract both 3D shoot topology and geometry of plants automatically from images on large datasets and a large range of species. In particular, these software essentially deal with dicotyledons, whose architecture is comparatively easier to analyze than monocotyledons. To tackle these challenges, we designed the Phenomenal software featured with: (i) a completely automatic workflow system including data import, reconstruction of 3D plant architecture for a range of species and quantitative measurements on the reconstructed plants; (ii) an open source library for the development and comparison of new algorithms to perform 3D shoot reconstruction and (iii) an integration framework to couple workflow outputs with existing models towards model-assisted phenotyping. Phenomenal analyzes a large variety of data sets and species from images of high-throughput phenotyping platform experiments to published data obtained in different conditions and provided in a different format. Phenomenal has been validated both on manual measurements and synthetic data simulated by 3D models. It has been also tested on other published datasets to reproduce a published semi-automatic reconstruction workflow in an automatic way. Phenomenal is available as an open-source software on a public repository.

show abstract

“…To bring this power to the field of algal research, there will need to be investments in building a comprehensive phenotypic database. The field of plant phenomics has already created the standards for data sharing, knowledge retrieval, and ontology annotation (Oellrich et al, 2015;Munir and Sheraz Anjum, 2018;Neveu et al, 2019), which can be adapted to algae. The data analysis tools presently used for model microbes (e.g.…”

Section: Phenomicsmentioning

confidence: 99%

Emerging Technologies in Algal Biotechnology: Toward the Establishment of a Sustainable, Algae-Based Bioeconomy

et al. 2020

View full text Add to dashboard Cite

Mankind has recognized the value of land plants as renewable sources of food, medicine, and materials for millennia. Throughout human history, agricultural methods were continuously modified and improved to meet the changing needs of civilization. Today, our rapidly growing population requires further innovation to address the practical limitations and serious environmental concerns associated with current industrial and agricultural practices. Microalgae are a diverse group of unicellular photosynthetic organisms that are emerging as next-generation resources with the potential to address urgent industrial and agricultural demands. The extensive biological diversity of algae can be leveraged to produce a wealth of valuable bioproducts, either naturally or via genetic manipulation. Microalgae additionally possess a set of intrinsic advantages, such as low production costs, no requirement for arable land, and the capacity to grow rapidly in both large-scale outdoor systems and scalable, fully contained photobioreactors. Here, we review technical advancements, novel fields of application, and products in the field of algal biotechnology to illustrate how algae could present high-tech, low-cost, and environmentally friendly solutions to many current and future needs of our society. We discuss how emerging technologies such as synthetic biology, highthroughput phenomics, and the application of internet of things (IoT) automation to algal manufacturing technology can advance the understanding of algal biology and, ultimately, drive the establishment of an algal-based bioeconomy.

show abstract

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

Cited by 66 publications

References 60 publications

Plant, space and time - linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials

Plant, space and time - linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials

Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

Emerging Technologies in Algal Biotechnology: Toward the Establishment of a Sustainable, Algae-Based Bioeconomy

Contact Info

Product

Resources

About