Browse > Article
http://dx.doi.org/10.1633/JISTaP.2019.7.4.5

Intercropping in Rubber Plantation Ontology for a Decision Support System  

Phoksawat, Kornkanok (Faculty of Management Technology, Rajamangala University of Technology Srivijaya)
Mahmuddin, Massudi (School of Computing, Universiti Utara Malaysia)
Ta'a, Azman (School of Computing, Universiti Utara Malaysia)
Publication Information
Journal of Information Science Theory and Practice / v.7, no.4, 2019 , pp. 56-64 More about this Journal
Abstract
Planting intercropping in rubber plantations is another alternative for generating more income for farmers. However, farmers still lack the knowledge of choosing plants. In addition, information for decision making comes from many sources and is knowledge accumulated by the expert. Therefore, this research aims to create a decision support system for growing rubber trees for individual farmers. It aims to get the highest income and the lowest cost by using semantic web technology so that farmers can access knowledge at all times and reduce the risk of growing crops, and also support the decision supporting system (DSS) to be more intelligent. The integrated intercropping ontology and rule are a part of the decision-making process for selecting plants that is suitable for individual rubber plots. A list of suitable plants is important for decision variables in the allocation of planting areas for each type of plant for multiple purposes. This article presents designing and developing the intercropping ontology for DSS which defines a class based on the principle of intercropping in rubber plantations. It is grouped according to the characteristics and condition of the area of the farmer as a concept of the rubber plantation. It consists of the age of rubber tree, spacing between rows of rubber trees, and water sources for use in agriculture and soil group, including slope, drainage, depth of soil, etc. The use of ontology for recommended plants suitable for individual farmers makes a contribution to the knowledge management field. Besides being useful in DSS by offering options with accuracy, it also reduces the complexity of the problem by reducing decision variables and condition variables in the multi-objective optimization model of DSS.
Keywords
ontology development; ontology to decision supporting system; intercropping ontology; knowledge-based decision supporting system; semantic web for decision supporting system;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Buranarach, M. (2017). Introduction to ontology-based data analysis. Paper presented at the Joint International Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2017), Tutorial Session, Hua-Hin, Thailand.
2 Chariyamakarn, W., Boonbrahm, P., Boonbrahm, S., & Ruangrajitpakorn, T. (2015). Framework of ontology based recommendation for farmer centered rice production. In T. Threeramunkong, T. Yuizono, & A. M. J. Skulimowski (Eds.), Proceedings of the Tenth International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2015) (pp. 376-387). Thailand: Sirindhorn International Institute of Technology, Thammasat University.
3 Department of Agriculture Extension. (2014). Alternatives way for small para-rubber farmers as supplemented revenue. Bangkok: Department of Agriculture Extension, Ministry of Agiculture and Cooperatives Thailand.
4 Fielding, N. G. (2012). Triangulation and mixed methods designs: Data integration with new research technologies. Journal of Mixed Methods Research, 6(2), 124-136.   DOI
5 Gomez‐Perez, A. (2001). Evaluation of ontologies. International Journal of Intelligent Systems, 16(3), 391-409.   DOI
6 Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199-220.   DOI
7 Jantzen, B. C., Mayo, D. G., & Patton, L. (2015). Ontology & methodology. Synthese, 192(11), 3413-3423.   DOI
8 Jingjit, R. (2015). Insights "Smart Farmer" just a new concept. Or reinvent agriculture Thailand. Retrieved May 12, 2019 from http://www.tpso.moc.go.th/sites/default/files/1074-img.pdf.
9 Kaewboonma, N., Tuamsuk, K., & Buranarach, M. (2014). Ontology modeling for a drought management information system. Libres, 24(1), 21-33.
10 Lagos-Ortiz, K., Medina-Moreira, J., Paredes-Valverde, M. A., Espinoza-Moran, W., & Valencia-García, R. (2017). An ontology-based decision support system for the diagnosis of plant diseases. Journal of Information Technology Research, 10(4), 42-55.   DOI
11 Lassila, O., & Hendler, J. (2007). Embracing "Web 3.0." IEEE Internet Computing, 11(3), 90-93.   DOI
12 Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382-385.
13 Mawardi, S. Y., Abdel-Aziz, M. H., Omran, A. M., & Mahmoud, T. A. (2013). An ontology-driven decision support system for wheat production. International Journal of Computer Science and Telecommunications, 4(8), 11-17.
14 Srisawat, C., & Payakpate, J. (2016). Comparison of MCDM methods for intercrop selection in rubber plantations. Journal of Information and Communication Technology, 15(1), 165-182.   DOI
15 Worawimolwanich, P., & Kesorn, K. (2015). Decision support system for economic crops using hybrid approaches. Paper presented at the 11th National Conference on Computing and Information Technology, Bangkok, Thailand.
16 Mishra, B., & Singh, S. R. (2016). Optimal land allocation in agricultural production planning using fuzzy goal programming. In M. Pant, K. Deep, J. C. Bansal, A. Nagar, & K. N. Das (Eds.), Proceedings of Fifth International Conference on Soft Computing for Problem Solving: SocProS 2015, Volume 1 (pp. 287-298). Singapore: Springer.
17 Alshaiji, S., El Kadhi, N., Wang, Z., & Al-Anzi, F. S. (2011). Fuzzy-based ontology intelligent DSS to strengthen government bilateral economic relations. Paper presented at the Second Kuwait Conference on e-Services and e-Systems, Kuwait City, Kuwait.
18 Bank of Thailand. (2016). Report on major agricultural price trends in southern Thailand. Retrieved May 12, 2019 from https://www.bot.or.th/Thai/MonetaryPolicy/Southern/CommoitiesPriceReport/QuarterlyReport_on_AgriculturalCommodities_Q12016V4.pdf.
19 Blomqvist, E. (2014). The use of Semantic Web technologies for decision support: A survey. Semantic Web, 5(3), 177-201.   DOI
20 Mousavi, S. R., & Eskandari, H. (2011). A general overview on intercropping and its advantages in sustainable agriculture. Journal of Applied Environmental and Biological Sciences, 1(11), 482-486.
21 Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Retrieved May 12, 2019 from http://www.ksl.stanford.edu/people/dlm/papers/ontology-tutorial-noy-mcguinness.pdf.
22 Office of Agricultural Economics. (2016). Agricultural statistics of Thailand 2016. Bangkok: Office of Agricultural Economic, Ministry of Agriculture and Cooperatives Thailand.
23 Office of Soil Resources Survey and Research. (2010). Soil survey report of Nakhon Si Thammarat province. Bangkok: Land Development Department, Ministry of Agriculture and Cooperatives Thailand.
24 Pick, R. A., & Weatherholt, N. (2013). A review on evaluation and benefits of decision support systems. Review of Business Information Systems, 17(1), 7-20.   DOI
25 Padmavathi, T., & Krishnamurthy, M. (2014). Ontology supported information systems: A review. Journal of Information Science Theory and Practice, 2(4), 61-76.   DOI
26 Antunes, F., Freire, M., & Costa, J. P. (2016). Semantic web and decision support systems. Journal of Decision Systems, 25(1), 79-93.   DOI
27 Phoksawat, K., & Mahmuddin, M. (2019). Knowledge and integrated data management model for personalized intercropping in rubber plantation. International Journal of Electrical and Computer Engineering, 9(6), 5502-5511.
28 Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489-497.   DOI
29 Poveda-Villalon, M., Suarez-Figueroa, M. C., & Gomez-Perez, A. (2015). Did you validate your ontology? OOPS! In E. Simperl, B. Norton, D. Mladenic, E. Della Valle, I. Fundulaki, A. Passant, & R. Troncy (Eds.), The Semantic Web: ESWC 2012 Satellite Events (pp. 402-407). Berlin: Springer-Verlag.
30 Salah, H. A. (2014). Ontology development (OWL&UML) methodology of web-based Decision Support System for water management. In Proceedings of the 2014 6th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 11-22). Red Hook: The Institute of Electrical and Electronics Engineers.
31 Sen, N., & Nandi, M. (2012). A goal programming approach to rubber-tea intercropping management in Tripura. Asian Journal of Management Research, 3(1), 178-183.
32 Brank, J., Grobelnik, M., & Mladenic, D. (2005). A survey of ontology evaluation techniques. In D. Mladenic, & M. Grobelnik (Eds.), Proceedings of the Conference on Data Mining and Data Warehouses (SiKDD 2005) (pp. 166-169). Ljubljana: International multiconference Information Society.
33 Shojanoori, R., & Juric, R. (2015). Ontology design for supporting decision making in self care homes. In Proceedings of the 2015 48th Hawaii International Conference on System Sciences (pp. 3084-3093). Red Hook: The Institute of Electrical and Electronics Engineers.