An Ontology-Driven Decision Support System for Rice Crop Production

Hifza Afzal, M. K. Kasi, B. Kasi, Bushra Naeem, S. K. Sami
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引用次数: 1

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.
基于本体驱动的水稻作物生产决策支持系统
农业领域现在广泛使用物联网(iot)技术为农民提供适当和准确的信息。以更有效的方式定期和定期地帮助农民完全是基于完整的数据、适当的规划和决策。通过物联网将设备相互连接,给传统的农业方式带来了巨大的变化。然而,它也带来了一些挑战,如语义互操作性、数据质量和准确性。在本文中,我们扩展了基础农业本体,以包括由水、农药和种子信息组成的类,这些信息是按季节和阶段组织的。我们扩展了农业本体,特别是农作物生产领域,使用水稻作物作为案例研究。使用语义Web规则语言(SWRL)与Jess规则引擎集成进行推理和推理,使设备之间相互理解。为了维持水稻作物的水灌溉,54条SWRL规则对101个OWL类进行了解释。它还通过识别特定作物类型,提供每个生长阶段的农药和除草剂信息以及种子信息。这有助于农民从收集的数据中获得更好的生产和可持续性结果,为他们提供水稻作物管理方面的决策支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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