Application of Quality Management System in the Research Process: A Case Study for Plant Phenotyping Research

DOI 10.7160/aol.2024.160406
No 4/2024, December
pp. 79-86

Galba, A., Kánská, E., Mikeš, V., Vaněk, J. and Jarolímek, J. (2024) "Application of Quality Management System in the Research Process: A Case Study for Plant Phenotyping Research", AGRIS on-line Papers in Economics and Informatics, Vol. 16, No. 4, pp. 79-86. ISSN 1804-1930. DOI 10.7160/aol.2024.160406.

Abstract

Phenomics research, driven by advancements in imaging and image processing, enables high-throughput measurements of plant traits, providing insights into growth, tissue development, and biochemical states. However, data accuracy is critical to reliable outcomes, especially in complex methods like 3D reconstruction and hyperspectral imaging. This study demonstrates the role of Quality Management Systems (QMS) in enhancing the research process in plant phenotyping. The study emphasizes the importance of a robust data quality assurance pipeline, focusing on error identification and improving data labeling processes through semi-automation. Root Cause Analysis (RCA) was employed to address discrepancies in annotated datasets and identify critical issues, such as misalignment in experimental protocols and operational errors, including the misplacement of irrigation hoses during data collection. Corrective actions, such as photo documentation and procedural revisions, significantly improved data quality. Additionally, algorithmic support streamlined the annotation process, increasing efficiency and data reliability. This integrated approach underscores the necessity of quality control in research, especially for geographically distributed teams working under variable conditions, and highlights the broader applicability of QMS in optimizing research outputs.

Keywords

Quality management system, data quality, plant phenotyping, research process, root cause analysis, data labeling process.

References

  1. Andersen, B. and Fagerhaug, T. (2000) "Root cause analysis: simplified tools and techniques", pp. 155. [Online]. Available: https://books.google.com/books/about/Root_Cause_Analysis.html?hl=csandid=i_EJAQAAMAAJ. [Accessed: Sept. 8, 2024]. ISBN 0873894669.
  2. Andersen, B. and Fagerhaug, T. (2006) "Root Cause Analysis, Second Edition: Simplified Tools and Techniques" , 240 p., Quality Oress. ISBN 9780873896924.
  3. Bali, P., Kutikuppala, L. V. S., Avti, P. and Medhi, B. (2021) "Data Fraud and Essence of Data Verifiability, Quality Assurance Implementation in Research Labs", pp. 137-159. ISBN 978-981-16-3074-3. DOI 10.1007/978-981-16-3074-3_9.
  4. Brünschwitz, S. and Kleymann-Hilmes, J. (2024) "Benefits and approaches of a quality management system in biomedical research laboratories", Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, Vol. 67, No. 1, pp. 99-106. E.ISSN ISSN 1437-1588. DOI 10.1007/S00103-023-03797-Y.
  5. Daiki, S. and Noshita, K. (2024) "A comparative study of plant phenotyping workflows based on three-dimensional reconstruction from multi-view images", BioRxiv. DOI 10.1101/2024.03.21.586185.
  6. Detring, J., Barreto, A., Mahlein, A.-K. and Paulus, S. (2024) "Quality Assurance of Hyperspectral Imaging Systems for Neural Network supported Plant Phenotyping". DOI 10.21203/RS.3.RS-4648326/V1.
  7. Dhondt, S., Wuyts, N. and Inzé, D. (2013) "Cell to whole-plant phenotyping: the best is yet to come", Trends in Plant Science, Vol. 18, No. 8, pp. 428-439. ISSN 1360-1385. DOI 10.1016/J.TPLANTS.2013.04.008.
  8. Fiorani, F. and Schurr, U. (2013) "Future Scenarios for Plant Phenotyping", Annual Review of Plant Biology, Vol. 64, pp. 267-291. ISSN 1543-5008. DOI 10.1146/annurev-arplant-050312-120137.
  9. Isere, E. E. and Omorogbe, N. E. (2024) "Quality Management in Clinical and Public Health Research: A Panacea for Minimising and Eliminating Protocol Deviations in Research Operations", Nigerian Medical Journal, Vol. 65, No. 3, pp. 367-375. ISSN 0300-1652. DOI 10.60787/NMJ-V65I3-421.
  10. ISO 9001:2015 - Quality management systems - Requirements. (n.d.) [Online]. Available: https://webstore.ansi.org/standards/iso/iso90012015 [Nov. 15, 2023].
  11. Kartal, S., Choudhary, S., Masner, J., Kholova, J., Stoces, M., Gattu, P., Schwartz, S. and Kissel, E. (2021) "Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans", Sensors, Vol. 21, No. 23, pp. 8022. ISSN 1424-8220. DOI 10.3390/S21238022.
  12. Multi-sensor data labeling platform for robotics and AV | Segments.ai. (n.d.). [Online]. Available: https://segments.ai/. [April 28, 2024].
  13. Parker, J. (2024) "The application of operation research in the quality management engineering", Advances in Operation Research and Production Management, Vol. 1, No. 1, pp. 25-32. ISSN 1687-9155. DOI 10.54254/3006-1210/DIRECT/2532.
  14. Paulus, S. (2019) "Measuring crops in 3D: Using geometry for plant phenotyping", Plant Methods, Vol. 15, No. 1, pp. 1-13. ISSN 1746-4811. DOI 10.1186/S13007-019-0490-0/FIGURES/5.
  15. Percarpio, K. B., Watts, B. V. and Weeks, W. B. (2008) "The effectiveness of root cause analysis: what does the literature tell us?", Joint Commission Journal on Quality and Patient Safety, Vol. 34, No. 7, pp. 391-398. ISSN 1553-7250. DOI 10.1016/S1553-7250(08)34049-5.
  16. Phenospex - Smart plant analysis and Phenotyping systems. (n.d.). [Online]. Available: https://phenospex.com/ [Nov. 15, 2023].
  17. Pongpiyapaiboon, S., Tanaka, H., Hashiguchi, M., Hashiguchi, T., Hayashi, A., Tanabata, T., Isobe, S. and Akashi, R. (2023) "Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass", The Plant Phenome Journal, Vol. 6, No. 1, ISSN 2578-2703. DOI 10.1002/PPJ2.20076.
  18. Ronanki, S., Pavlík, J., Masner, J., Jarolímek, J., Stočes, M., Subhash, D., Talwar, H. S., Tonapi, V. A., Srikanth, M., Baddam, R. and Kholová, J. (2022). An APSIM-powered framework for post-rainy sorghum-system design in India, Field Crops Research, Vol. 277, p. 108422. ISSN 0378-4290. DOI 10.1016/J.FCR.2021.108422.
  19. Serrat, O. (2017) "The Five Whys Technique", In: Knowledge Solutions, pp. 307-310. Springer, Singapore. ISBN 978-981-10-0982-2. DOI 10.1007/978-981-10-0983-9_32.
  20. Shook, J. (2008) "Managing to Learn: Using the A3 Management Process to Solve Problems, Gain Agreement, Mentor and Lead", pp. 10-11. ISBN 1934109207.
  21. Šimek, P., Stočes, M., Vaněk, J., and Masner, J. (2015) "Mobile accessibility of information sources in the areas of agriculture, forestry, water management, food industry and rural development", Agrarian Perspectives XXIV: Global Agribusiness and the Rural Economy, pp. 440-446. ISBN 978-80-213-2581-4.
  22. Sozzani, R., Busch, W., Spalding, E. P. and Benfey, P. N. (2014) "Advanced imaging techniques for the study of plant growth and development", Trends in Plant Science, Vol. 19, No. 5, pp. 304-310. ISSN 1878-4372. DOI 10.1016/J.TPLANTS.2013.12.003.
  23. Starzyńska, B. and Hamrol, A. (2013) "Excellence toolbox: Decision support system for quality tools and techniques selection and application", Total Quality Management and Business Excellence, Vol. 24, No. 5-6, pp. 577-595. ISSN 0954-4127. DOI 10.1080/14783363.2012.669557.
  24. Tarí, J. J. and Sabater, V. (2004a) "Quality tools and techniques: Are they necessary for quality management?", International Journal of Production Economics, Vol. 92, No. 3, pp. 267-280. ISSN 0925-5273. DOI 10.1016/J.IJPE.2003.10.018.
  25. Ugochukwu, A. I. and Phillips, P. W. B. (2022) "Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management", Plant Phenome Journal, Vol. 5, No. 1. ISSN 2578-2703. DOI 10.1002/PPJ2.20056.
  26. Vadez, V., Kholová, J., Hummel, G., Zhokhavets, U., Gupta, S. K. and Hash, C. T. (2015) "LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget", Journal of Experimental Botany, Vol. 66, No.18, pp. 5581. ISSN 0022-0957. DOI 10.1093/JXB/ERV251.
  27. Venkatasubramanian, V., Rengaswamy, R., Yin, K. and Kavuri, S. N. (2003) "A review of process fault detection and diagnosis: Part I: Quantitative model-based methods", Computers and Chemical Engineering, Vol. 27, No. 3, pp. 293-311. ISSN 0098-1354. DOI 10.1016/S0098-1354(02)00160-6.
  28. Vianna, E. L. F., De Figueiredo, V. V., Da Silva, C. M. F., Bertolino, L. C. and Spinelli, L. (2022) "Impact of implementing quality control systems in laboratories associated with teaching and research institutions − The case study of the laboratory for macromolecules and colloids in the petroleum industry. International Journal of Metrology and Quality Engineering, Vol. 13, pp. 4. ISSN 2107-6847. DOI 10.1051/IJMQE/2022004.
  29. Wilson, P. F. ., Dell, L. D. and Anderson, G. F. (1993) "Root cause analysis: a tool for total quality management", pp. 216. ASQC Quality Press, 1993. ISBN 9780873891639.
  30. Yuniarto, H. A. (2012) "The Shortcomings of Existing Root Cause Analysis Tools", Proceedings of the World Congress on Engineering 2012 Vol III, WCE 2012, July 4 - 6, 2012, London, U.K. ISSN 2078-0958.

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