Petr Skobelev
ABSTRACT
The paper discusses the principles of developing a multi-agent digital twin of plants using broccoli as an example of plants. The developed model of the digital twin of plants must meet the following requirements: real-time environmental data acquisition, user feedback collection, continuous adaptation of the plant development plan for each event, individual instance for field or field part. The digital twin of plant is designed as an intelligent cyber-physical system that has a user-defined knowledge bas and a multi-agent system for planning and modeling of plant growth and development, as well as for forecasting crop parameters. For this purpose, a new method for estimate stage duration and yield is proposed, which defines a "tube" – a corridor to each of the factors corresponding plant development. The key factors have been determined during consultations with practicing agronomists but can be adjusted by users experience. This concept was originally introduced for wheat digital twin, but now is scaled and modified to simulate broccoli growth process.
- Barbara Rita Barricelli, Elena Casiraghi, and Daniela Fogli. 2019. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Access 7, 167653-167671.Google Scholar
Cross Ref
- Warren Purcell, and Thomas Neubauer. 2022. Digital Twins in Agriculture: A State-of-the-art review. Smart Agricultural Technology 3, 100094, 1-11. https://doi.org/10.1016/j.atech.2022.100094Google ScholarCross Ref
- Anna Chlingaryan, Salah Sukkarieh, and Brett Whelan. 2018. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Comput. Electron. Agric. 151, 61-69.Google ScholarDigital Library
- Andreas Kamilaris, and Francesc X. Prenafeta-Boldú. 2018. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70-90.Google ScholarCross Ref
- Chengquan Zhou, Jun Hu, Zhifu Xu, Jibo Yue, Hongbao Ye, and Guijun Yang. 2020. A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks. Front Plant Sci. 11, 402. https://doi:10.3389/fpls.2020.00402Google ScholarCross Ref
- Boris Lauser, Margherita Sini, Anita Liang, Johannes Keizer, and Stephen Katz. 2006. From AGROVOC to the Agricultural Ontology Service / Concept Server. An OWL model for creating ontologies in the agricultural domain. Retrieved November 20, 2022 from http://www.fao.orgGoogle Scholar
- Clément Jonquet, AnneToulet, and Elizabeth Arnaud. 2018. AgroPortal: A vocabulary and ontology repository for agronomy. Computers and Electronics in Agriculture 144, 126-143.Google ScholarCross Ref
- Radmir Poluektov. 2007. Information support of the model. AGROTOOL simulation and modeling complex. Russian Academy of Agricultural Sciences, Agrophysical Research Institute, St. Petersburg, .Google Scholar
- Uwe Meier (Ed.). 2001. BBCH Monograf. Growth stages of mono-and dicotyledonous plants (2nd ed.). Germany Federal Biological Research Centre for Agriculture and Forestry. 126-129. Retrieved November 22, 2022 from https://www.politicheagricole.it/flex/AppData/WebLive/Agrometeo/MIEPFY800/BBCHengl2001.pdfGoogle Scholar
- Michael Wooldridge. 2002. An Introduction to Multiagent Systems. John Wiley and Sons Ltd., England.Google ScholarDigital Library
- Petr Skobelev, Igor Mayorov, Elena Simonova, Oleg Goryanin, Aleksey Zhilyaev, Aleksey Tabachinskiy, and Vladimir Yalovenko. 2020. Development of models and methods for creating a digital twin of plant within the cyber-physical system for precision farming management. J. Phys.: Conf. Ser. 1703, 012022.Google ScholarCross Ref
- Hendrik Van Brussel, Jo Wyns, Paul Valckenaers, Luc Bongaerts, and Patrick Peeters. 1998. Reference Architecture for Holonic Manufacturing Systems: PROSA. Computers in Industry 37, 255-274.Google ScholarCross Ref
- David Kelton, and Averill Low. 2004. Simulation modeling (3rd ed.). Peter. BHV Publishing Group, St. Petersburg, Russia.Google Scholar
- Tatyana Gavrilova, Dmitry Kudryavtsev, and Dmitry Muromtsev. 2022. Engineering of knowledge. Models and methods: textbook for universities. Lan, St. Petersburg, Russia.Google Scholar
Index Terms
- Concept and Development of a Multi-Agent Digital Twin of Plant Focused on Broccoli
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