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Santos, R., Beko, M. & Leithardt, V. (2023). Package proposal for data pre-processing for machine learning applied to precision irrigation. In Proceedings - 2023 6th Conference on Cloud and Internet of Things, CIoT 2023. (pp. 141-148). Lisboa:  IEEE.

D. S. Pereira et al.,  "Package proposal for data pre-processing for machine learning applied to precision irrigation", in Proc. - 2023 6th Conf. on Cloud and Internet of Things, CIoT 2023, Lisboa,  IEEE, 2023, pp. 141-148

@inproceedings{pereira2023_1734953388198,
	author = "Santos, R. and Beko, M. and Leithardt, V.",
	title = "Package proposal for data pre-processing for machine learning applied to precision irrigation",
	booktitle = "Proceedings - 2023 6th Conference on Cloud and Internet of Things, CIoT 2023",
	year = "2023",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/ciot57267.2023.10084899",
	pages = "141-148",
	publisher = " IEEE",
	address = "Lisboa",
	organization = ""
}

TY  - CPAPER
TI  - Package proposal for data pre-processing for machine learning applied to precision irrigation
T2  - Proceedings - 2023 6th Conference on Cloud and Internet of Things, CIoT 2023
AU  - Santos, R.
AU  - Beko, M.
AU  - Leithardt, V.
PY  - 2023
SP  - 141-148
DO  - 10.1109/ciot57267.2023.10084899
CY  - Lisboa
AB  - The evolution of the Internet of Things (IoT) devices
for precision agriculture is directly linked to the needs and
interests of humanity. These advances include migration to
cloud computing, data engineering, and the democratization
of tools. These changes allow for better management, data
quality, security, and scalability, reducing operational costs. The
objective of this research was to present a proposal for a data
pre-processing package for meteorological stations classified as
conventional. Among the main findings of this research is the
need for data pre-processing for Machine Learning applications
focused on precision irrigation, controlled by IoT devices; the use
of data from conventional weather stations for Machine Learning
applications; the availability of applications developed in Open
Source repositories, and the proposal of a data pre-processing
package to help professionals from different areas. The systematic
review examined the various machine-learning applications for
precision irrigation. Different models and mechanisms used to
apply Machine Learning in precision irrigation projects were
identified. In addition, we look at the challenges faced when
using Machine Learning for precision irrigation, including the
lack of data, the need for efficient data pre-processing, and
the need to tune the model to get the best possible result.
At the end of the article, we propose a data pre-processing
package for conventional meteorological stations. This package
includes normalization, noise removal, and outliers to improve
the reliability of the input data.

ER  -