OntologyBased Knowledge Modeling for Rice Crop Production In recent times, smart farming based on Internet of Thing (IoT) technologies has enabled the farmers to enhance productivity of their farms and reduce the waste. However, the heterogeneity of the connecting devices in IoTs has invited several challenges such as the lack of understanding between devices when sharing data acquired from heterogeneous data sources. To overcome the interoperability issues, semanticbased technologies are used to makes devices understand and share heterogeneous data among various devices in an IoT system. In this paper, an existing farming ontology has been extended by adding several crucial classes taking rice crop as a case study. The appended classes include water, pesticide, nutrients, and seedrelated classes. Based on all the classes of the ontology, SWRL rules have been defined to infer knowledge with the help of Jess rule engine. In this work, a total of 63 rules reason on 101 classes and its associated properties, thereby, inferring several new results including the management of water and nutrients in yield, continuously at each growth stage of the rice crop production. It also maintains the pesticide use throughout the crop lifecycle along with identifying the seed of specific rice crop type. This results in assisting the farmers in daily and phasewise decisionmaking related to their rice crops.

Interactive Cultivation System for the Future IoTBased Agriculture As initiatives to increase Japanu0027s declining food selfsufficiency rate and revitalize the field of agriculture, the concept of smart agriculture and urban agriculture are currently being implemented. Automation via Artificial Intelligence is expected to overcome the labor shortage in the agricultural industry. However, the number of skilled farmers who can contribute to gathering crop growth data required for machine learning is restricted, and these data are also limited to localand environmentalbased conditions. In this paper, we propose a system for obtaining composite growth data in various environments and crops targeted for home gardens and paddy fields. An interactive cultivation sensing system consisting of IoTbased technologies is designed and realized to ensure the continuous growth of crops in optimum conditions daily. With this, progress will be made in determining the efficient cultivation conditions for machine learning, and in finding solutions to future problems of agriculture.