An Ontology-Driven Decision Support System for Rice Crop Production

Hifza Afzal, Mumraiz K. Kasi, Bakhtiar Kasi, Bushra Naeem, Syed Kamran Sami


Agriculture domain now extensively uses the Internet of Things (IoTs) technology to provide farmers with proper and accurate information. Assisting farmers regularly and periodically in a more efficient manner is totally based on complete data, proper planning, and decision making. Connecting devices with each other through IoT has brought huge changes to traditional way of farming. However, it has also invited some challenges such as the semantic interoperability, quality and accuracy of data.

In this paper, we extend a base farming ontology to include classes comprising of water, pesticides, and seeds information that is organized both seasonally and phase-wise. We have extended a farming ontology specifically a crop production domain using rice crop as a case study. Semantic Web Rule Language (SWRL) integrated with Jess rule engine is used for reasoning and inferencing to make devices understandable to each other. A collection of 54 SWRL rules reason about 101 OWL classes in order to maintain water irrigation in rice crops. It also provides pesticide and weedicide information for each growth stage along with seed information by identifying specific crop type. This helps the farmers to obtain better results in terms of production and sustainability from the collected data by offering them decision making support in the management of rice crops.


Internet of Things, smart farming, semantic web, ontology, SWRL rules, Jess rule engine

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A. Lawan, A. Rakib, N. Alechina, and A. Karunaratne, “Advancing underutilized crops knowledge using swrl-enabled ontologies-a survey and early experiment.” in JIST (Workshops & Posters), 2014, pp. 69–84.

A. I. Maarala, X. Su, and J. Riekki, “Semantic reasoning for context aware internet of things applications,” arXiv preprint arXiv:1604.08340, 2016.

M. Grobe, “Rdf, jena, sparql and the’semantic web’,” in Proceedings of the 37th annual ACM SIGUCCS fall conference: communication and collaboration. ACM, 2009, pp. 131–138.

A. Kamilaris, F. Gao, F. X. Prenafeta Boldú, and M. I. Ali, “Agri- iot: A semantic framework for internet of things enabled smart farming applications,” in Internet of Things (WF-IoT), 2016 IEEE 3rd World Forum on. IEEE, 2016, pp. 442–447.

S. Pokharel, M. A. Sherif, and J. Lehmann, “Ontology based data access and integration for improving the effectiveness of farming in nepal,” in Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)Volume 02. IEEE Computer Society, 2014, pp. 319–326.

A. Jemal, H. Ktait, R. B. Halima, and M. Jmaiel, “Oodaas: Ontology driven analysis for self adaptive ambient systems,” in Proceedings of the International Conference on Internet of things and Cloud Computing. ACM, 2016, p. 66.

S. Sivamani, N. Bae, and Y. Cho, “A smart service model based on ubiquitous sensor networks using vertical farm ontology,” International Journal of Distributed Sensor Networks, vol. 9, no. 12, p. 161495, 2013.

S. Sivamani, H.-g. Kim, M. Lee, J. Park, C. Shin, and Y. Cho, “An ontology model for smart service in vertical farms–an owl-s approach,” International Journal of uand eService, Science and Technology, vol. 9, no. 1, pp. 161–170, 2016.

M. O’connor, H. Knublauch, S. Tu, and M. Musen, “Writing rules for the semantic web using swrl and jess,” Protégé With Rules WS, Madrid, 2005.

A. Gyrard, M. Serrano, J. B. Jares, S. K. Datta, and M. I. Ali, “Sensor-based linked open rules (slor): An automated rule discovery approach for iot applications and its use in smart cities,” in Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 2017, pp. 1153–1159.

S. Arora, I. Mukherji, A. Kumar, and R. Tanwar, “Pesticide residue analysis of soil, water, and grain of ipm basmati rice,” Environmental monitoring and assessment, vol. 186, no. 12, pp. 8765–8772, 2014.

G. Gines, J. Bea, and T. Palaoag, “Characterization of soil moisture level for rice and maize crops using gsm shield and arduino microcontroller,” in IOP Conference Series: Materials Science and Engineering, vol. 325, no. 1. IOP Publishing, 2018, p. 012019.

D. Wang and X. Cai, “Irrigation scheduling—role of weather forecasting and farmers’ behavior,” Journal of water resources planning and management, vol. 135, no. 5, pp. 364–372, 2009.

J. Mateo-Sagasta, S. M. Zadeh, H. Turral, and J. Burke, Water pollution from agriculture: a global review. Executive summary. Rome, Italy: FAO Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Research Program on Water, Land and Ecosystems (WLE)., 2017.

M. P. Polson and S. Eswaran, “Ontology creation and semantic web for paddy,” International Journal of Computer applications, vol. 41, no. 9, 2012.

A. Thunkijjanukij, A. Kawtrakul, S. Panichsakpatana, U. Veesommai et al., “Ontology development: a case study for thai rice,” Kasetsart J.(Nat. Sci.), vol. 43, no. 3, pp. 594–604, 2009.


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