An Efficient Data Warehouse for Crop Yield Prediction

DC FieldValueLanguage
dc.contributor.authorNgo, Vuong M.-
dc.contributor.authorLe-Khac, Nhien-An-
dc.contributor.authorKechadi, M-Tahar-
dc.date.accessioned2019-04-24T10:45:04Z-
dc.date.available2019-04-24T10:45:04Z-
dc.date.issued2018-06-27-
dc.identifier.urihttp://hdl.handle.net/10197/10118-
dc.descriptionThe 14th International Conference on Precision Agriculture, Montreal, Canada, 24-27 June 2018en_US
dc.description.abstractNowadays, precision agriculture combined with modern information and communications technologies, is becoming more common in agricultural activities such as automated irrigation systems, precision planting, variable rate applications of nutrients and pesticides, and agricultural decision support systems. In the latter, crop management data analysis, based on machine learning and data mining, focuses mainly on how to efficiently forecast and improve crop yield. In recent years, raw and semi-processed agricultural data are usually collected using sensors, robots, satellites, weather stations, farm equipment, farmers and agribusinesses while the Internet of Things (IoT) should deliver the promise of wirelessly connecting objects and devices in the agricultural ecosystem. Agricultural data typically captures information about farming entities and operations. Every farming entity encapsulates an individual farming concept, such as field, crop, seed, soil, temperature, humidity, pest, and weed. Agricultural datasets are spatial, temporal, complex, heterogeneous, non-standardized, and very large. In particular, agricultural data is considered as Big Data in terms of volume, variety, velocity and veracity. Designing and developing a data warehouse for precision agriculture is a key foundation for establishing a crop intelligence platform, which will enable resource efficient agronomy decision making and recommendations. Some of the requirements for such an agricultural data warehouse are privacy, security, and real-time access among its stakeholders (e.g., farmers, farm equipment manufacturers, agribusinesses, co-operative societies, customers and possibly Government agencies). However, currently there are very few reports in the literature that focus on the design of efficient data warehouses with the view of enabling Agricultural Big Data analysis and data mining. In this paper, we propose a system architecture and a database schema for designing and implementing a continental level data warehouse. Besides, some major challenges and agriculture dimensions are also reviewed and analysed.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.publisherISPAen_US
dc.relation.ispartofProceedings of the 14th International Conference on Precision Agricultureen_US
dc.subjectData warehouseen_US
dc.subjectConstellation schemaen_US
dc.subjectCrop yield predictionen_US
dc.subjectPrecision agricultureen_US
dc.titleAn Efficient Data Warehouse for Crop Yield Predictionen_US
dc.typeConference Publicationen_US
dc.internal.authorcontactothervuong.ngo@ucd.ieen_US
dc.internal.webversionshttps://www.ispag.org/icpa-
dc.statusNot peer revieweden_US
dc.neeo.contributorNgo|Vuong M.|aut|-
dc.neeo.contributorLe-Khac|Nhien-An|aut|-
dc.neeo.contributorKechadi|M-Tahar|aut|-
dc.description.othersponsorshipOrigin Enterprisesen_US
dc.date.updated2018-08-17T13:13:40Z-
item.fulltextWith Fulltext-
item.grantfulltextopen-
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