The Concept Design of Rice Quality Detection System Using Model-Based System Engineering Approach

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Purwa Tri Cahyana
Titi Candra Sunarti
Erliza Noor
Hartrisari Hardjomidjojo
Noer Laily

Abstract

Quality of rice is determined by several factors such as water content, broken grains, and whiteness. The approach often used for the measurement is manual, time-consuming, and prone to error. Therefore, this research proposes a faster and more accurate rice quality detection system using Model-Based System Engineering (MBSE) approach. System was based on the needs analysis presented through an activity diagram showing the components and activities flow. Logical architecture diagrams were also used to structurally describe system logic to be further abstracted to the physical architecture stage. Moreover, machine learning techniques were used to simulate rice quality data analysis using the decision tree classification with the Iterative Dichotomizer 3 (ID3) algorithm. The simulation was applied to 200 supervised random datasets divided into 80% training and 20% test data with a focus on three attributes, including water content, broken grains, and whiteness. System design was developed using Visual Paradigm Community Edition software and the data were analyzed through the application of R software. The ID3 algorithm simulation produced rice quality detection system with a 92.5% accuracy rate, where 53% of rice was classified as good and 47% as bad. The proposed conceptual design for rice quality detection can be a starting point for the development of an industrial-scale system design.

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Author Biographies

Purwa Tri Cahyana, IPB University

Agroindustrial Engineering Study Program, Graduate School

Titi Candra Sunarti, IPB University

Department of Agroindustrial Technology, Faculty of Agricultural Engineering and Technology

Erliza Noor , IPB University

Department of Agroindustrial Technology, Faculty of Agricultural Engineering and Technology

Hartrisari Hardjomidjojo, IPB University

Department of Agroindustrial Technology, Faculty of Agricultural Engineering and Technology

Noer Laily, National Research and Innovation Agency

Research Center for Food Technology and Processing

How to Cite
1.
Cahyana PT, Sunarti TC, Noor E, Hardjomidjojo H, Laily N. The Concept Design of Rice Quality Detection System Using Model-Based System Engineering Approach. J. appl. agricultural sci. technol. [Internet]. 2024Nov.24 [cited 2024Dec.8];8(4):437-49. Available from: https://jaast.org/index.php/jaast/article/view/256

References

  1. Saikrishna A, Dutta S, Subramanian V, Moses JA, Anandharamakrishnan C. Ageing of rice: A review. Journal of Cereal Science 2018;81:161–70. https://doi.org/10.1016/j.jcs.2018.04.009.
  2. Zia H, Fatima HS, Khurram M, Hassan IU, Ghazal M. Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection. Foods 2022;11:1–17. https://doi.org/10.3390/foods11182723.
  3. Liu J, Liu Y, Wang A, Dai Z, Wang R, Sun H, et al. Characteristics of moisture migration and volatile compounds of rice stored under various storage conditions. Journal of Cereal Science 2021;102:1–12. https://doi.org/10.1016/j.jcs.2021.103323.
  4. Lin L, He Y, Xiao Z, Zhao K, Dong T, Nie P. Rapid-detection sensor for rice grain moisture based on NIR spectroscopy. Applied Sciences (Switzerland) 2019;9. https://doi.org/10.3390/app9081654.
  5. Durai S, Vadivel MT, Sujithra T. Grading of Rice Quality by Chalky area analysis Using Simple Digital Image Processing Techniques. International Journal of Pure and Applied Mathematics 2017;114:657–65. http://www.ijpam.eu
  6. Pokhrel A, Dhakal A, Sharma S, Poudel A. Evaluation of Physicochemical and Cooking Characteristics of Rice (Oryza sativa L.) Landraces of Lamjung and Tanahun Districts, Nepal. International Journal of Food Science 2020;2020. https://doi.org/10.1155/2020/1589150.
  7. An Y, Zhou X, Zhang Y. Changes in Physicochemical, Cooking and Sensory Characteristics of Rice Shifted from Low-temperature Storage. Grain & Oil Science and Technology 2018;1:8–14. https://doi.org/10.3724/sp.j.1447.gost.2018.18018.
  8. Shen Y, Gong W, Li Y, Deng J, Shu X, Wu D, et al. The physiochemical and nutritional properties of high endosperm lipids rice mutants under artificially accelerated ageing. Lwt 2022;154. https://doi.org/10.1016/j.lwt.2021.112730.
  9. Tao K, Yu W, Prakash S, Gilbert RG. Investigating cooked rice textural properties by instrumental measurements. Food Science and Human Wellness 2020;9:130–5. https://doi.org/10.1016/j.fshw.2020.02.001.
  10. Xie LH, Tang SQ, Wang XQ, Sheng ZH, Hu SK, Wei XJ, et al. Simultaneously determining amino acid contents using near-infrared reflectance spectroscopy improved by pre-processing method in rice. Lwt 2023;188:115317. https://doi.org/10.1016/j.lwt.2023.115317.
  11. Li Z, Song J, Ma Y, Yu Y, He X, Guo Y, et al. Identification of aged-rice adulteration based on near-infrared spectroscopy combined with partial least squares regression and characteristic wavelength variables. Food Chemistry: X 2023;17:100539. https://doi.org/10.1016/j.fochx.2022.100539.
  12. Wibben DR, Furfaro R. Model-Based Systems Engineering approach for the development of the science processing and operations center of the NASA OSIRIS-REx asteroid sample return mission. Acta Astronautica 2015;115:147–59. https://doi.org/10.1016/j.actaastro.2015.05.016.
  13. Gregory J, Berthoud L, Tryfonas T, Rossignol A, Faure L. The long and winding road: MBSE adoption for functional avionics of spacecraft. Journal of Systems and Software 2020;160:110453. https://doi.org/10.1016/j.jss.2019.110453.
  14. Chen J, Wang G, Lu J, Zheng X, Kiritsis D. Model-based system engineering supporting production scheduling based on satisfiability modulo theory. Journal of Industrial Information Integration 2022;27:100329. https://doi.org/10.1016/j.jii.2022.100329.
  15. Feldmann S, Herzig SJI, Kernschmidt K, Wolfenstetter T, Kammerl D, Qamar A, et al. Towards effective management of inconsistencies in model-based engineering of automated production systems. IFAC-PapersOnLine 2015;28:916–23. https://doi.org/10.1016/j.ifacol.2015.06.200.
  16. Holt J, Perry S. SysML for systems engineering: A model-based approach (3rd edition). 3rd ed. London: The Institution of Engineering and Technology; 2018. https://doi.org/10.1049/PBPC020E.
  17. Fernandez JL, Hernandez C. Practical Model-Based Systems Engineering. Norwood, Massachusetts: Norwood, Massachusetts:Artech House; 2019.
  18. Quinlan JR. Induction of decision trees. Machine Learning 1986;1:81–106. https://doi.org/10.1007/bf00116251.
  19. Slocum M. Decision Making Using ID3 Algorithm. Rivier Academic Journal 2012;8:1–12. https://www2.rivier.edu/journal/ROAJ-Fall-2012/J674-Slocum-ID3-Algorithm.pdf
  20. BSN. Indonesian National Standard on Rice 2020. https://www.bsn.go.id
  21. BSN. Indonesian National Standard on Rice 2015. https://www.bsn.go.id
  22. Matzavela V, Alepis E. Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments. Computers and Education: Artificial Intelligence 2021;2:100035. https://doi.org/10.1016/j.caeai.2021.100035.
  23. Boucher M. 3 Ways Model-Based Systems Engineering (MBSE) Will Help You. PTC Digital Transforms Physical 2016. https://www.ptc.com/en/blogs/plm/3-ways-model-based-systems-engineering-mbse-will-help-you (accessed November 28, 2023).
  24. Tschirner C, Dumitrescu R, Bansmann M, Gausemeier J. Tailoring Model-Based Systems Engineering concepts for industrial application. 9th Annual IEEE International Systems Conference, SysCon 2015 - Proceedings 2015:69–76. https://doi.org/10.1109/SYSCON.2015.7116731.
  25. Lemazurier L, Chapurlat V, Grossetête A. An MBSE Approach to Pass from Requirements to Functional Architecture. IFAC-PapersOnLine 2017;50:7260–5. https://doi.org/10.1016/j.ifacol.2017.08.1376.
  26. Barrett J, Wang N, Dalbavie J-M, Carricajo C. Agile model-based systems engineering of passenger train operational design. Transportation Research Procedia 2023;72:295–302. https://doi.org/10.1016/j.trpro.2023.11.407.
  27. Mustofah M, Utami P. Perangkat Penentu Kualitas Beras Ditinjau dari Kadar Air dan Berat Butir Menir Berbasis Arduino Uno. Elinvo (Electronics, Informatics, and Vocational Education) 2019;4:39–48. https://doi.org/10.21831/elinvo.v4i1.21516.
  28. Alaya MA, Tóth Z, Géczy A. Applied Color Sensor Based Solution for Sorting in Food Industry Processing. Periodica Polytechnica Electrical Engineering and Computer Science 2019;63:16–22. https://doi.org/https://doi.org/10.3311/PPee.13058.
  29. Fay A, Vogel-Heuser B, Frank T, Eckert K, Hadlich T, Diedrich C. Enhancing a model-based engineering approach for distributed manufacturing automation systems with characteristics and design patterns. Journal of Systems and Software 2015;101:221–35. https://doi.org/10.1016/j.jss.2014.12.028.
  30. Riedel R, Jacobs G, Konrad C, Singh R, Sprehe J. Managing knowledge and parameter dependencies with MBSE in textile product development processes. Procedia CIRP 2020;91:170–5. https://doi.org/10.1016/j.procir.2020.01.138.
  31. Sharma A, Srivastava A. Understanding Decision Tree Algorithm by using R Programming Language. ACEIT Conference 2016, India: 2016, p. 177–82. https://ijcsit.com/ijcsit-aceit2016-conference.php
  32. Vujović Ž. Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications 2021;12:599–606. https://doi.org/10.14569/IJACSA.2021.0120670.
  33. Hamzah AS, Mohamed A. Classification of white rice grain quality using ANN : a review. International Journal of Artificial Intelligence (IJ-AI) 2020;9:600–8. https://doi.org/10.11591/ijai.v9.i4.pp600-608.
  34. Meizenty S, Sahid DSS, Sari JN. Rice Quality Detection Based on Digital Image Using Classification Method. International Applied Business and Engineering Conference, August 25, 2021, Riau, Indonesia: Politeknik Caltex Riau, Pekanbaru-Riau; 2021, p. 35–8. https://abecindonesia.org/iabec/index.php/iabec/article/view/22/17
  35. Son NH, Thai-Nghe N. Deep Learning for Rice Quality Classification. 2019 International Conference on Advanced Computing and Applications (ACOMP), 27-29 November, Nha Trang, Vietnam, Nha Trang, Vietnam: IEEE Computer Society; 2019, p. 92–6. https://doi.org/10.1109/ACOMP.2019.00021.
  36. Farahnakian F, Sheikh J, Farahnakian F, Heikkonen J. A comparative study of state-of-the-art deep learning architectures for rice grain classification. Journal of Agriculture and Food Research 2024;15:100890. https://doi.org/10.1016/j.jafr.2023.100890.