Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/23417
Author(s): | Cardoso, J. Glória, A. Sebastião, P. |
Date: | 2020 |
Title: | Improve irrigation timing decision for agriculture using real time data and machine learning |
Event title: | 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020 |
ISBN: | 978-1-7281-9675-6 |
DOI (Digital Object Identifier): | 10.1109/ICDABI51230.2020.9325680 |
Keywords: | Machine learning Neural network Decision tree Support vector machine XGBoost Random forest Sustainability Smart irrigation |
Abstract: | With the constant evolution of technology and the constant appearance of new solutions that, when combined, manage to achieve sustainability, the exploration of these systems is increasingly a path to take. This paper presents a study of machine learning algorithms with the objective of predicting the most suitable time of day for water administration to an agricultural field. With the use of a high amount of data previously collected through a Wireless Sensors Network (WSN) spread in an agricultural field it becomes possible to explore technologies that allow to predict the best time for water management in order to eliminate the scheduled irrigation that often leads to the waste of water being the main objective of the system to save this same natural resource. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | IT-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
conferenceobject_78553.pdf | Versão Aceite | 254,74 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.