Crop Knowledge Discovery Based on Agricultural Big Data Integration

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Title: Crop Knowledge Discovery Based on Agricultural Big Data Integration
Authors: Ngo, Vuong M.Kechadi, Tahar
Permanent link: http://hdl.handle.net/10197/11804
Date: Jan-2020
Online since: 2020-12-11T10:41:50Z
Abstract: Nowadays, 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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: ACM
Copyright (published version): 2020 ACM
Keywords: Decision supportCrop yieldSoil propertiesInsecticidesHerbicides
DOI: 10.1145/3380688.3380705
Language: en
Status of Item: Peer reviewed
Is part of: ICMLSC 2020: Proceedings of the 4th International Conference on Machine Learning and Soft Computing
Conference Details: The 4th International Conference on Machine Learning and Soft Computing (ICMLSC 2020), Haiphong City Vietnam, January 2020
ISBN: 978-1-4503-7631-0
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection

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