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Crop Knowledge Discovery Based on Agricultural Big Data Integration

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Download 2003.05043v1.pdf725.76 KB
Author(s)
Ngo, Vuong M. 
Kechadi, Tahar 
Uri
http://hdl.handle.net/10197/11804
Date Issued
January 2020
Date Available
11T10:41:50Z December 2020
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.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
ACM
Copyright (Published Version)
2020 ACM
Keywords
  • Decision support

  • Crop yield

  • Soil properties

  • Insecticides

  • Herbicides

DOI
10.1145/3380688.3380705
Language
English
Status of Item
Peer reviewed
Part of
ICMLSC 2020: Proceedings of the 4th International Conference on Machine Learning and Soft Computing
Description
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/
Owning collection
Computer Science Research Collection
Scopus© citations
4
Acquisition Date
Feb 5, 2023
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