ISSN 2308-4057 (Печать),
ISSN 2310-9599 (Онлайн)

Digital transformation metamodel in smart farming: Crop classification prediction based on recurrent neural network

Agriculture 4.0 is an opportunity for farmers to meet the current challenges in food production. It has become necessary to adopt a set of agricultural practices based on advanced technologies. Agriculture 4.0 enables farms to create added value by combining innovative technologies, such as precision agriculture, information and communication technology, robotics, and Big Data. As an enterprise, a connected farm is highly sensitive to strategic changes in organizational structures, objectives, modified variety, new business objects, processes, etc. To control the farm’s information system strategically, we proposed a metamodel based on the ISO/IS 19440 standard, where we added some new constructs relating to advanced digital technologies for smart and connected agriculture. We applied the proposed metamodel to the crop classification prediction process. This involved using machine learning methods such as recurrent neural networks to predict the type of crop being grown in a given agricultural area. Our research bridges farming with modern technology through our metamodel for a connected farm, promoting sustainability and efficiency. Furthermore, our crop classification study demonstrates the power of advanced machine learning, guided by our metamodel, in accurately predicting crop conditions, emphasizing its potential for crop management and food security. In essence, our work advances the transformative role of digital agriculture in modern farming.
Ключевые слова
Farm modeling, digital agriculture, agriculture 4.0, advanced technologies, connected farm, ISO 19440-2007
  1. Yu L, Qin H, Xiang P. Incentive mechanism of different agricultural models to agricultural technology information management system. Sustainable Computing: Informatics and Systems. 2020;28:100423.
  2. Mouratiadou I, Latka C, van der Hilst F, Müller C, Berges R, Bodirsky BL, et al. Quantifying sustainable intensification of agriculture: The contribution of metrics and modelling. Ecological Indicators. 2021;129:107870.
  3. Radhi A. Design and Implementation of a Smart Farm System. Association of Arab Universities Journal of Engineering Sciences. 2017;24(3):227–241.
  4. Coble KH, Mishra AK, Ferrell S, Griffin T. Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy. 2018;40(1):79–96.
  5. Fielke S, Taylor B, Jakku E. Digitalisation of agricultural knowledge and advice networks: A state-of-the-art review. Agricultural Systems. 2020;180:102763.
  6. Triantafyllou A, Tsouros DC, Sarigiannidis P, Bibi S. An architecture model for smart farming. 15th International Conference on Distributed Computing in Sensor Systems; 2019; Santorini. Santorini, 2019. p. 385–392.
  7. Verdouw C, Sundmaeker H, Tekinerdogan B, Conzon D, Montanaro T. Architecture framework of IoT-based food and farm systems: A multiple case study. Computers and Electronics in Agriculture. 2019;165:104939.
  8. Jaekel F-W, Zelm M, Chen D. Service modelling language applied for hyper connected ecosystem. Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics. Vol. 1; 2021; Setúbal. Setúbal: SciTePress; 2021. p. 209–215.
  9. Vernadat F. Enterprise modelling: Research review and outlook. Computers in Industry. 2020;122:103265.
  10. Rabhi L, Jabir B, Falih N, Afraites L, Bouikhalene B. A Connected farm metamodeling using advanced information technologies for an agriculture 4.0. AGRIS on-line Papers in Economics and Informatics. 2023;15(2):93–104.
  11. Rabhi L, Falih N, Afraites A, Bouikhalene B. Big Data Approach and its applications in Various Fields: Review. Procedia Computer Science. 2019;155:599–605.
  12. Avadí A, Galland V, Versini A, Bockstaller C. Suitability of operational N direct field emissions models to represent contrasting agricultural situations in agricultural LCA: Review and prospectus. Science of The Total Environment. 2022;802:149960.
  13. Li C, Niu B. Design of smart agriculture based on big data and Internet of things. International Journal of Distributed Sensor Networks. 2020;16(5).
  14. Rabhi L, Falih N, Afraites L, Bouikhalene B. Digital agriculture based on big data analytics : A focus on predictive irrigation for smart farming in Morocco. Indonesian Journal of Electrical Engineering and Computer Science. 2021;24(1):581–589.
  15. Jabir B, Falih N, Sarih A, Tannouche A. A strategic analytics using convolutional neural networks for weed identification in sugar beet fields. AGRIS on-line Papers in Economics and Informatics. 2021;13(1):49–57.
  16. Ngo VM, Kechadi M-T. Crop knowledge discovery based on agricultural big data integration. ICMLSC'20: Proceedings of the 4th International Conference on Machine Learning and Soft Computing; 2020; Haiphong City. New York: Association for Computing Machinery; 2020. p. 46–50.
  17. Rabhi L, Falih N, Afraites L, Bouikhalene B. A functional framework based on big data analytics for smart farming. Indonesian Journal of Electrical Engineering and Computer Science. 2021;24(3):1772–1779.
  18. Ming L, GuoHua Z, Wei W. Study of the game model of e-commerce information sharing in an agricultural product supply chain based on fuzzy big data and LSGDM. Technological Forecasting and Social Change. 2021;172:121017.
  19. Symeonaki E, Arvanitis K, Piromalis D. A context-aware middleware cloud approach for integrating precision farming facilities into the IoT toward agriculture 4.0. Applied Sciences. 2020;10(3):813.
  20. Wang B, Tao F, Fang X, Liu C, Liu Y, Freiheit T. Smart manufacturing and intelligent manufacturing: A comparative review. Engineering. 2021;7(6):738–757.
  21. Jabir B, Rabhi L, Falih N. RNN- and CNN-based weed detection for crop improvement: An overview. Foods and Raw Materials. 2021;9(2):387–396.;
Как цитировать?
Rabhi L, Jabir B., Falih N, Afraites L, Bouikhalene B. Digital transformation metamodel in smart farming: crop classification prediction based on recurrent neural network. Foods and Raw Materials. 2025;13(1):107–118. 
О журнале