Improved Approach to the Development of the Crop Monitoring System Based on the Use of Multi-Source Spatial Data

• Autorzy: Kokhan Svitlana , Dorozhynskyy Oleksandr , Burshtynska Khrystyna , Vostokov Anatoliy , Drozdivskyi Oleg

Identyfikatory

DOI

10.12911/22998993/125442

• Języki publikacji: EN

Abstrakty

EN

The study describes the stages of conceptual modeling to provide a crop monitoring system based on the multisource spatial data to assess the state of agricultural crops. The process of developing geodatabase models, which is the basis of the crop monitoring system, considered the construction of a set of diagrams of the Unified Modeling Language (UML). The UML Sequence diagrams were developed to describe the specific properties of crop monitoring system components and their behavior. The developed data flow diagram showed the data flow in the crop monitoring system and described the processes involved in the system for the transfer of data from the source files to the geodatabase. The approach presented in the study can be suggested as a methodology that is suitable for a wide range of developers of monitoring systems.

Słowa kluczowe

EN

geodatabase; spatial data; remote sensing; crop monitoring system

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2020
• Tom: Vol. 21, nr 7
• Strony: 108--114
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Kokhan Svitlana

• Department of Geoinformatics and Aerospace Research of the Earth, National University of Life and Environmental Sciences of Ukraine, 17 Vasylkivska St., Kyiv, Ukraine

autor

Dorozhynskyy Oleksandr

• Department of Environmental Engineering and Geodesy, University of Life Sciences in Lublin, Leszczyńskiego St. 7, 20-069 Lublin, Poland

autor

Burshtynska Khrystyna

• Department of Photogrammetry and Geoinformatics, Institute of Geodesy, Lviv Polytechnic National University, Lviv, Ukraine

autor

Vostokov Anatoliy

• Department of Geoinformatics and Aerospace Research of the Earth, National University of Life and Environmental Sciences of Ukraine, 17 Vasylkivska St., Kyiv, Ukraine

autor

Drozdivskyi Oleg

• Department of Geoinformatics and Aerospace Research of the Earth, National University of Life and Environmental Sciences of Ukraine, 17 Vasylkivska St., Kyiv, Ukraine

Bibliografia

• 1. Argent, R. 2004. An overview of model integration for environmental applications – components, frameworks and semantics. Environmental Modelling & Software. 19(3), 219–234. https://doi.org/10.1016/S13648152(03)00150–6
• 2. Atzberger, C. 2013. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sensing. 5(2), 949–981. http://https://doi.org/10.3390/rs5020949
• 3. Cucho-Padin, G., Loayza, H., Palacios, D., Balcazar, M., Carbajal, M., Quiroz, R. 2019. Development of low-cost remote sensing tools and methods for supporting smallholder agriculture. Applied Geomatics. https://doi.org/10.1007/s12518–019–00292–5
• 4. Filipova, O., Nikiforova, O. 2019. Definition of the Criteria for Layout of the UML Use Case Diagrams. Applied Computer Systems 24(1), 75–81. ISSN 2255–8691. https://doi.org/10.2478/acss-2019–0010
• 5. Habibie, M., Noguchi, R., Shusuke, M., Ahamed, T. 2019. Land suitability analysis for maize production in Indonesia using satellite remote sensing and GISbased multicriteria decision support system. GeoJournal ,32 DOI: 10.1007/s10708–019–10091–5
• 6. Hyun, C., Lee, J., Lee, I. 2013. Assessment of hydrogen fluoride damage to vegetation using optical remote sensing data. In: International Society for Photogrammetry and Remote Sensing. Spatial Inf. Sci., XL-7/W2” proceedings. Antalya, Turkey. https://doi.org/10.5194/isprsarchives-XL-7-W2–115–2013
• 7. Jetlund, K., Onstein, E., Huang, L. 2019. Adapted Rules for UML Modelling of Geospatial Information for Model-Driven Implementation as OWL Ontologies. International Journal of Geo-Information. 8(9), 1–26. https://doi.org/10.3390/ijgi8090365
• 8. Khaiter, P., Erechtchoukova, M. 2019. Conceptualizing an Environmental Software Modeling Framework for Sustainable Management Using UML. Journal of Environmental Informatics. 34(2). 123–138. https://doi:10.3808/jei.201800400
• 9. de Kinderen, S., Kaczmarek-Heβ, M. 2019. On model-based analysis of organizational structures: an assessment of current modeling approaches and application of multi-level modeling in support of design and analysis of organizational structures”. Software and Systems Modeling, (19), 19. https://doi.org/10.1007/s10270–01900767–4
• 10. Kokhan, S. and Vostokov, A. 2020. Using Vegetative Indices to Quantify Agricultural Crop Characteristics. J. Ecol. Eng., 2020. 21(4):120–127. DOI: https://doi.org/10.12911/22998993/119808
• 11. Leroux, C., Jones, H., Pichon, L., Taylor, J., Tisseyre, B. 2019. Automatic harmonization of heterogeneous agronomic and environmental spatial data. Precision Agriculture, 2019. 20. 1211–1230. https://doi.org/10.1007/s11119–019–09650–0
• 12. Lyalko, V. and Popov, M. 2006. Multispectral Remote Sensing in Nature Management. Naukova Dumka. Kyiv., p. 43 – 62. ISBN 966–00–0403–1 (In Ukrainian)
• 13. Purnamasari, R., Ahamed, T., Noguchi, R. 2018. Land suitability assessment for cassava production in Indonesia using GIS, remote sensing and multicriteria analysis. Asia-Pacific Journal of Regional Science, Springer 3(1), 1–32. http://doi:10.1007/ s41685–018–0079-z
• 14. Quemada, M., Lassaletta, L., Leip, A., Jones, A., Lugato, E. 2020. Integrated management for sustainable cropping systems: Looking beyond the greenhouse balance at the field scale. Global Change Biology, https://doi.org/10.1111/gcb.14989
• 15. Rekha, B., Ajawan, P., Desai, V. 2018. Remote Sensing Technology and Applications in Agriculture. In: International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS): proceedings. 193–197. Karnataka, India. http://doi.10NN.1109/CTEMS.2018.8769124
• 16. Sadowska, M., Huzar, Z. 2019. Representation of UML Class Diagrams in OWL 2 on the Background of Domain Ontologies. e-Informatica Software Engineering Journal (EISEJ). 13(1), 63–103. https://doi.10.5277/e-Inf190103
• 17. Stankevich, S., Shklyar, S., Lisenko, A. 2018. Prohramnyy modulʹ otsinky subpikselʹnoho zmishchennya znimkiv, otrymuvanykh z kvadrokopteru. Ukrayinsʹkyy zhurnal dystantsiynoho zonduvannya Zemli. (17). 10–13. http://nbuv.gov.ua/UJRN/ukjdzz_2018_17_4 (in Ukrainian)
• 18. Weiss, X., Jacobb, F., Duveillerc, G. 2020. Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment (236). https://doi.org/10.1016/j.rse.2019.111402
• 19. Zhai, Z., Martínez, J., Beltran, V., Martínez N. 2020. Decision support systems for agriculture 4.0: Survey and challenges”. Computers and Electronics in Agriculture. 170, 16. https://doi.org/10.1016/j.compag.2020.105256
• 20. Zhai, C., Peterson, B., Mukhopadhyay, S. 2019. Static generation of UML sequence diagrams. International Journal on Software Tools for Technology Transfer. 23. https://doi:10.1007/s10009–019–00545-z