An Ontology-Driven Decision Support System for Rice Crop Production
| Autor: | Hifza Afzal, Mumraiz Khan Kasi, Bakhtiar Kasi, Bushra Naeem, Syed Kamran Sami |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Applied and Emerging Sciences. 11:85 |
| ISSN: | 2415-2633 1814-070X |
| DOI: | 10.36785/jaes.111410 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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