Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/23417
Autoria: Cardoso, J.
Glória, A.
Sebastião, P.
Data: 2020
Título próprio: Improve irrigation timing decision for agriculture using real time data and machine learning
Título do evento: 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
Palavras-chave: Machine learning
Neural network
Decision tree
Support vector machine
Random forest
Smart irrigation
Resumo: 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.
Arbitragem científica: yes
Acesso: Acesso Aberto
Aparece nas coleções:IT-CRI - Comunicações a conferências internacionais

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