Standardized Data Infrastructures for Plant Phenomics: A Review of MIAPPE and BrAPI Integration within High-Performance
DOI:
https://doi.org/10.7160/aol.2026.180109How to Cite
Abstract
The increasing complexity and volume of plant phenotypic data have driven the emergence of new computational and standardization frameworks to enable data integration, reproducibility, and reuse. This systematic literature review examines the current state of software tools, data models, and interoperability standards in plant phenomics, focusing on the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Using a structured PRISMA-based methodology, we analyze two major community driven initiatives MIAPPE and BrAPI as representative solutions for standardized data description and exchange. Furthermore, the study evaluates the role of High-Performance Computing (HPC) and deep learning in addressing computational challenges associated with large-scale datasets, including multi-sensor and 3D capture technologies. Special consideration is given to data governance, encompassing secure access, ethical use, and GDPR compliance within expanding phenomics ecosystems. The synthesis identifies persistent gaps in data harmonization and semantic alignment, proposing future research directions toward more integrated, secure, and scalable infrastructures. This review emphasizes that the success of plant phenomics depends on bridging the gap between standard definitions and their practical implementation within high-performance workflows.References
Abdul Hamid, N. A. W. and Singh, B. (2024) "High-Performance Computing Based Operating Systems, Software Dependencies and IoT Integration", In: K. A. Ahmad, K. A., Abdul Hamid, N. A. W., Jawaid, M., Khan, T. and Singh, B. (eds.) "High Performance Computing in Biomimetics", pp. 175-204, Series in BioEngineering. Springer, Singapore. E-ISBN 978-981-97-1017-1, ISSN 2196-8861 DOI: https://doi.org/10.1007/978-981-97-1017-1_8
Ackoff, R. L. (1989) "From data to wisdom", Journal of Applied Systems Analysis, Vol. 16, pp. 3-9. ISSN 0308-9541.
Aksenova, A., Johny, A., Adams, T., Gribbon, P., Jacobs, M. and Hofmann-Apitius, M. (2024) "Current state of data stewardship tools in life science", Frontiers in Big Data, Vol. 7, p. 1428568. E-ISSN 2624-909X. DOI: https://doi.org/10.3389/fdata.2024.1428568
Arend, D., Junker, A., Scholz, U., Schüler, D. and Selbig, J. (2022) "From data to knowledge - big data needs stewardship, a plant phenomics perspective", The Plant Journal, Vol. 110, No. 1, pp. 12-28. E-ISSN 1365-313X, ISSN 0960-7412. DOI: https://doi.org/10.1111/tpj.15804
Arend, D., Junker, A., Scholz, U., Schüler, D., Wylie, J. and Lange, M. (2016) "PGP repository: a plant phenomics and genomics data publication infrastructure", Database (Oxford), Vol. 2016, p. baw033. ISSN 1758-0463. DOI: https://doi.org/10.1093/database/baw033
Bosilj, P., Duckett, T. and Cielniak, G. (2018) "Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture", Computers in Industry, Vol. 98, pp. 226-240. ISSN 0166-3615. DOI: https://doi.org/10.1016/j.compind.2018.02.003
Bosilj, P., Duckett, T., Cielniak, G. and Pearson, S. (2018) "Quantitative phenotyping of plants using three-dimensional imaging and machine vision", Computers and Electronics in Agriculture, Vol. 153, pp. 69-79. ISSN 1872-7107.
Cooper, L., Meier, A., Laporte, M. A., Elser, J. L., Mungall, C., Sinn, B. T., Cavaliere, D., Carbon, S., Dunn, N. A., Smith, B., Qu, B., Preece, J., Zhang, E., Todorovic, S., Gkoutos, G., Doonan, J. H., Stevenson, D. W., Arnaud, E. and Jaiswal, P. (2018) "The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics", Nucleic Acids Research, Vol. 46, No. D1, pp. D1168 - D1180. E-ISSN 1362-4962. DOI: https://doi.org/10.1093/nar/gkx1152
Ćwiek-Kupczyńska, H., Altmann, T., Arend, D., Arnaud, E., Chen, D., Cornut, G., Fiorani, F., Frohmberg, W., Junker, A., Klukas, C., Lange, M., Mazurek, C., Nafissi, A., Neveu, P., van Oeveren, J., Pommier, C., Poorter, H., Rocca-Serra, P., Sansone, S.A., Scholz, U., van Schriek, M., Seren, Ü., Usadel, B., Weise, S., Kersey, P. and Krajewski, P. (2016) "Measures for interoperability of phenotypic data: minimum information requirements and formatting", Plant Methods, Vol. 12, No. 44. E-ISSN 1746-4811. DOI: https://doi.org/10.1186/s13007-016-0144-4
Dumschott, K., Dörpholz, H., Laporte, M. A., Brilhaus, D., Schrader, A., Usadel, B., Neumann, S., Arnaud, E. and Kranz, A. (2023) "Ontologies for increasing the FAIRness of plant research data", Frontiers in Plant Science, Vol. 14, p. 1279694. E-ISSN 1664-462X. DOI: https://doi.org/10.3389/fpls.2023.1279694
European Commission. (2025) "Legal framework of EU data protection". [Online]. Available: https://commission.europa.eu/law/law-topic/data-protection/legal-framework-eu-data-protection_ en [Accessed: Dec. 15, 2025].
European Union. (2018) "General Data Protection Regulation (GDPR)", Official Journal of the European Union, L119, pp. 1-88. E-ISSN 1725-2423.
Fiorani, F. and Schurr, U. (2013) "Future scenarios for plant phenotyping", Annual Review of Plant Biology, Vol. 64, pp. 267-291. ISSN 1545-2123. DOI: https://doi.org/10.1146/annurev-arplant-050312-120137
Frontiers in Plant Science. (2023) "Phenomics as an emerging research discipline". [Online]. Available: https://www.frontiersin.org/articles/10.3389/fpls.2023.1233794/full [Accessed: Sept 21, 2025]
GDPR-info.eu. (2024) "General Data Protection Regulation (GDPR) - Legal Text". [Online]. Available: https://gdpr-info.eu [Accessed: Sept 21, 2025].
Georgiou, Y., Zhou, N., Zhong, L., Hoppe, D., Pospieszny, M., Papadopoulou, N., Nikas, K., Nikolos, O. L., Kranas, P., Karagiorgou, S., Pascolo, E., Mercier, M. and Velho, P. (2020) "Converging HPC, Big Data and Cloud Technologies for Precision Agriculture Data Analytics on Supercomputers", In: Jagode, H., Anzt, H., Juckeland, G., Ltaief, H. (eds) High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science, Vol. 12321, Springer, Cham. E-ISBN 978-3-030-59851-8. DOI: https://doi.org/10.1007/978-3-030-59851-8_25
Ghanem, M. E., Marrou, H. and Sinclair, T. R. (2015) "Physiological phenotyping and its application to the breeding of drought-tolerant crops", Frontiers in Physiology, Vol. 6, 362. E-ISSN 1664-042X. DOI: https://doi.org/10.3389/fphys.2015.00362
Glaubitz, J. C., Casstevens, T. M., Lu, F., Harriman, J., Elshire, R. J., Sun, Q. and Buckler, E. S. (2014) "TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline", PLoS One, Vol. 9, No. 2, p. e90346. ISSN 1932-6203. DOI: https://doi.org/10.1371/journal.pone.0090346
Goldstein, A., Fink, L. and Ravid, G. (2021) "A Framework for Evaluating Agricultural Ontologies", Sustainability, Vol. 13, No. 11, p. 6387. ISSN 2071-1050. DOI: https://doi.org/10.3390/su13116387
Grüning, B. Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch C. H., Valieris, R., Köster, J. and Biocomda Team (2018) "Bioconda: sustainable and comprehensive software distribution for the life sciences", Nature Methods, Vol. 15, No. 7, pp. 475-476. ISSN 1548-7105. DOI: https://doi.org/10.1038/s41592-018-0046-7
Kartal, S., Choudhary, S., Stočes, M., Šimek, P., Vokoun, T. and Novák, V. (2020) "Segmentation of Bean-Plants Using Clustering Algorithms", AGRIS on-line Papers in Economics and Informatics, Vol. 12, No. 3, pp. 36-43. ISSN 1804-1930. DOI: https://doi.org/10.7160/aol.2020.120304
Kartal, S., Masner, J., Kholová, J., Galba, A., Murugesan, T., Baddam, R., Mikes, V. and Kánská, E. (2025) "AI-Driven Background Segmentation for High-Throughput 3D Plant Scans", IEEE Access, Vol. 13, pp. 136027-136037. ISSN 2169-3536. DOI: https://doi.org/10.1109/ACCESS.2025.3594406
Krisnawijaya, N. N. K., Tekinerdogan, B., Catal, C., van der Tol, R. and Herdiyeni, Y. (2025) "Implementing FAIR principles in data management systems: A multi-case study in precision farming", Computers and Electronics in Agriculture, Vol. 230, p. 109855. ISSN 0168-1699. DOI: https://doi.org/10.1016/j.compag.2024.109855
LeBauer, D., Maxwell, B., Demieville, J., Fahlgren, N., French, A., Garnett, R., Hu, Z., Huynh, K., Kooper, R., Li, Z., Maimaitijiang, M., Mao, J., Mockler, T., Morris, G., Newcomb, M., Ottman, M., Ozersky, P., Paheding, S., Pauli, D., Pless, R., Quin, W., Riemer, K., Rohde, G., Rooney, W., Sagan, V., Shakoor, N., Stylianou, A., Thorp, K., Ward, R., White, J., Willis, C. and Zender, C. (2020) "Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics, and imaging sensors", [Dataset], Dryad. DOI: https://doi.org/10.5061/dryad.4b8gtht99
Matteis, L., Skofic, M., Portugal, A., Mclaren, G., Hyman, G. and Arnaud, E. (2012) "Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice", Frontiers in Physiology, Vol. 3, p. 326. E-ISSN 1664-042X. DOI: https://doi.org/10.3389/fphys.2012.00326
Meier, P., Deksnyte, G. and Winter, R. (2021) "Digital Responsibility Goals: A Framework for the Responsible Use of Data and Algorithms", Business & Information Systems Engineering, Vol. 63, No. 6, pp. 665-678. ISSN 1867-0202. DOI: https://doi.org/10.3233/SHTI220377
MIAPPE Contributors. (2024) "Minimum Information About a Plant Phenotyping Experiment (MIAPPE) Specification, Version 1.2". [Online]. Available: https://github.com/MIAPPE [Accessed: Oct 25, 2025].
Minssen, T., Rutz, B. and van Zimmeren, E. (2020) "Clinical trial data transparency and GDPR compliance", Science and Public Policy, Vol. 47, No. 2, pp. 228-238. ISSN 1471-5430. DOI: https://doi.org/10.1093/scipol/scaa014
Nicora, G., Vitali, F., Dagliati, A., Geifman, N. and Bellazzi, R. (2020) "Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools", Frontiers in Oncology. Vol. 10. E-ISSN. 2234-943X. DOI: https://doi.org/10.3389/fonc.2020.01030
OECD. (2022) "Responding to societal challenges with data: Access, sharing, stewardship and control", OECD Publishing. [Online]. Available: https://www.oecd.org/en/publications/ responding-to-societal-challenges-with-data_2182ce9f-en.html [Accessed: Oct 25, 2025].
Papoutsoglou, E. A., Farian D., Arend, D., Arnaud, E., Athanasiadis, I. N., Chaves, I., Coppens, F., Cornut, G., ...Pommier, C. (2020) "Enabling reusability of plant phenomic datasets with MIAPPE 1.1", New Phytologist, Vol. 227, No. 1, pp. 260-273. E-ISSN 1469-8137. DOI: https://doi.org/10.1111/nph.16544
Papoutsoglou, E. A., Athanasiadis, I., Visser, R. and Finkers, R. (2023) "The benefits and struggles of FAIR data: the case of reusing plant phenotyping data", Scientific Data, Vol. 10. E-ISSN 2052-4463. DOI: https://doi.org/10.1038/s41597-023-02364-z
Pommier, C., Michotey, C., Cornut, G., Roumet, P., Duchêne, E., Flores, R., Lebreton, A., Alaux, M., Durand, S., Kimmel, E., Letellier, T., Merceron, G., Laine, M., Guerche, C., Loaec, M., Steinbach, D., Laporte, M. A., Arnaud, E., Quesneville, H. and Adam-Blondon, A. F. (2019) "Applying FAIR Principles to Plant Phenotypic Data Management in GnpIS", Plant Phenomics, Vol. 2019, p. 1671403. ISSN 2643-6515. DOI: https://doi.org/10.34133/2019/1671403
Prifti, K., Krijger, J., Thuis, T. and Stamhuis, E. (2023) "From Bilateral to Ecosystemic Transparency: Aligning GDPR’s Transparency Obligations with the European Digital Ecosystem of Trust", In: Kuhlmann, S., De Gregorio, F., Fertmann, M., Ofterdinger, H. and Sefkow, A. (eds.) Transparency or Opacity : A Legal Analysis of the Organization of Information in the Digital World, 1st ed., pp. 115, Nomos. ISBN 978-3-7560-0027-2. DOI: https://doi.org/10.5771/9783748936060-115
Rowley, J. (2007) "The wisdom hierarchy: representations of the DIKW hierarchy", Journal of Information Science, Vol. 33, No. 2, pp. 163-180. E-ISSN 1741-6485. DOI: https://doi.org/10.1177/0165551506070706
Selby, P., Abbeloos, R., Backlund, J. E., Basterrechea Salido, M., Bauchet, G., Benites-Alfaro, O. E., Birkett, C., Calaminos, V. C., Carceller, P., Cornut, G. ... The BrAPI consortium (2019) "BrAPI - an application programming interface for plant breeding applications", Bioinformatics, Vol. 35, pp. 4147-4155. ISSN 1367-4811. DOI: https://doi.org/10.1093/bioinformatics/btz190
Selby, P., Abbeloos, R., Adam-Blondon, A.F., Agosto-Pérez, F. J., Alaux, M., Alic, I., Al-Shamaa, K., Aparicio, ... BrAPI Consortium. (2025) "BrAPI v2: real-world applications for data integration and collaboration in the breeding and genetics community", Database, Vol. 2025, p. baaf048. ISSN 1758-0463. DOI: https://doi.org/10.1093/database/baaf048
Sterling, T., Anderson, M. and Brodowicz, M. (2024) "High performance computing: Modern systems and practices", 2nd ed., Elsevier. ISBN 9780128230350. DOI: https://doi.org/10.1016/C2013-0-09704-6
Stočes, M., Jarolímek, J., Anderle, M., Kholová, J., Pavlík, J., Masner, J., Spichal, L. and Klimes, P. (2025) "Plant Phenotyping Network: Data Standards". Poster, National EOSC CZ Conference 2025, Ostrava, Czech Republic. [Online]. Available: https://www.eosc.cz/media/4054734/stoces_ a1_height-poster-stoces.pdf [Accessed: Dec. 12, 2025].
Stočes, M., Vaněk, J., Jarolímek, J., Novák, V., Masner, J., Šimek, P., Kánská, E., Havránek, M., Kubata, K. and Voral, V. (2023) "Agriculture Data Platform - Institutional Data Repository - Selected Aspects", AGRIS on-line Papers in Economics and Informatics, Vol. 15, No. 4, pp. 127-133. DOI: https://doi.org/10.7160/aol.2023.150409
Tardieu, F., Cabrera-Bosquet, L., Pridmore, T. and Bennett, M. (2017) "Plant phenomics, from sensors to knowledge", Current Biology, Vol. 27, No. 15, pp. R770 - R783. E-ISSN 0960-9822. DOI: https://doi.org/10.1016/j.cub.2017.05.055
Tricco, A. C., Lillie, E., Zarin, W., O´Brien, K. K., ColQuhoun, H., Levac., D., Moher, D., Peters, M. D. J...Straus, S. E. (2018) "PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation", Annals of Internal Medicine, Vol. 169, No. 7, pp. 467-473. E-ISSN 1539-3704. DOI: https://doi.org/10.7326/M18-0850
Ubbens, J., Stavness, I., Pound, M.P. and Wei Guo. (2025) "Deep learning in plant phenotyping: the first ten years", Plant Phenomics, Vol. 7, No. 4, p. 100062. ISSN 2643-6515. DOI: https://doi.org/10.1016/j.plaphe.2025.100062
UK Data Service. (2025) "FAIR data principles". [Online]. Available: https://ukdataservice.ac.uk/ learning-hub/research-data-management/plan-to-share/fair-data-principles/ [Accessed: Sept. 22, 2025].
Umbach, G. (2024) "Open Science and the impact of Open Access, Open Data, and FAIR publishing principles on data-driven academic research: Towards ever more transparent, accessible, and reproducible academic output?", Statistical Journal of the IAOS, Vol. 40, No. 1, pp. 59-70. ISSN 1875-9254. DOI: https://doi.org/10.3233/SJI-240021
Varshney, R. K., Thudi, M., Pandey, M. K., Tardieu, F., Ojiewo, C., Vadez, V., Whitbread, A. M., Siddiwue, K. H. M., Nguyen, H. T., Carberry, P. S. and Bergvinson, D. (2018) "Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy", Journal of Experimental Botany, Vol. 69, No. 13, pp. 3293-3312. ISSN 0022-0957. DOI: https://doi.org/10.1093/jxb/ery088
Wilkinson, M. D., Dumontier, M., Aalbersberg, I., Appleton, G., Axton, M., Baak, A., Blomebrg, N, Boiten, J.-W...Mons, B. (2016) "The FAIR Guiding Principles for scientific data management and stewardship", Scientific Data, Vol. 3, p. 160018. E-ISSN 2052-4463. DOI: https://doi.org/10.1038/sdata.2016.18
Wong, J., Henderson, T. and Ball, K. (2021) "Data protection for the common good: Developing a framework for a data protection-focused data commons", Data & Policy, Vol. 3, p. e41. E-ISSN 2632-3249. DOI: https://doi.org/10.1017/dap.2021.40
Xu, R. and Li, C. (2022) "A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots", Plant Phenomics, Vol. 2022, pp. 1-20. ISSN 2643-6515. DOI: https://doi.org/10.34133/2022/9760269
Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M. S., Varshney, R. K., Prasanna, B. M. and Qian, Q. (2022) "Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction", Molecular Plant, Vol. 15, No. 11, pp. 1664-1695. ISSN 1674-2052. DOI: https://doi.org/10.1016/j.molp.2022.09.001
Yuan, H., Song, M., Liu, Y., Xie, Q., Cao, W., Zhu, Y. and Ni, J. (2023) "Field Phenotyping Monitoring Systems for High-Throughput: A Survey of Enabling Technologies, Equipment, and Research Challenges", Agronomy, Vol. 13, No. 11, 2832. ISSN 2073-4395. DOI: https://doi.org/10.3390/agronomy13112832
