Abstract
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.
Hifza Afzal , Mumraiz K. Kasi, Bakhtiar Kasi, Bushra Naeem, Syed Kamran Sami. (2021) An Ontology-Driven Decision Support System for Rice Crop Production, Journal of Applied and Emerging Sciences, Volume-11, Issue-1.
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