Ngo, Vuong M.Vuong M.NgoKechadi, TaharTaharKechadi2020-12-112020-12-112020 ACM2020-01978-1-4503-7631-0http://hdl.handle.net/10197/11804The 4th International Conference on Machine Learning and Soft Computing (ICMLSC 2020), Haiphong City Vietnam, January 2020Nowadays, the agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. The analysis of this big data enables farmers, companies and agronomists to extract high business and scientific knowledge, improving their operational processes and product quality. However, before analysing this data, different data sources need to be normalised, homogenised and integrated into a unified data representation. In this paper, we propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models. We also apply some methods to extract knowledge with the view to improve crop yield; these include finding suitable quantities of soil properties, herbicides and insecticides for both increasing crop yield and protecting the environment.en© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ICMLSC 2020: Proceedings of the 4th International Conference on Machine Learning and Soft Computing (2020) http://doi.acm.org/10.1145/nnnnnn.nnnnnnDecision supportCrop yieldSoil propertiesInsecticidesHerbicidesJournal Article10.1145/3380688.33807052020-03-3116/SPP/3296https://creativecommons.org/licenses/by-nc-nd/3.0/ie/