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Application of the technology systems is growing in various fields and the agriculture is not an exception. Agriculture is also reaping the benefits of technological innovation which helps in quantitative and qualitative food production. Vertical farm, one of the agricultural practices, is considered to be the future of agriculture with the rate of population migrating into urban areas. Ubiquitous computing in agriculture is emerging remarkably in this fast processing pervasive environment, owing to wireless sensor network (WSN). Building a context aware system for the vertical farm is complex without the semantic interoperability between the Internet of things (IOT). In this paper, we propose a vertical farm ontology (VFO), an OWL based ontology model which helps in more understanding of the relationship between the domain factors. With the proposed model, the information from the Internet of things is recomposed as context information and made understandable for the other systems. For the sake of agriculture, we hope that our proposed model will pave great path for the development of smart and intelligent agricultural services.

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This research paper presents a rule-based regression predictive model for bike sharing demand prediction. In recent days, Pubic rental bike sharing is becoming popular because of is increased comfortableness and environmental sustainability. Data used include Seoul Bike and Capital Bikeshare program data. Both data have weather data associated with it for each hour. For both the dataset, five statistical models were trained with optimized hyperparameters using a repeated cross validation approach and testing set is used for evaluation: (a) CUBIST (b) Regularized Random Forest (c) Classification and Regression Trees (d) K Nearest Neighbour (e) Conditional Inference Tree. Multiple evaluation indices such as R 2 , Root Mean Squared Error, Mean Absolute Error and Coefficient of Variation were used to measure the prediction performance of the regression models. The results show that the rule-based model CUBIST was able to explain about 95 and 89% of the Variance (R 2 ) in the testing set of Seoul Bike data and Capital Bikeshare program data respectively. An analysis with variable importance was carried to analyse the most significant variables for all the models developed with the two datasets considered. The variable importance results have shown that Temperature and Hour of the day are the most influential variables in the hourly rental bike demand prediction.

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