Wireless Sensor Networks for Irrigation in Crops Using Multivariate Regression Models. Rosero-Montalvo, P., D., Pijal-Rojas, J., Vasquez-Ayala, C., Maya, E., Pupiales, C., Suarez, L., Benitez-Pereira, H., & Peluffo-Ordonez, D. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM), pages 1-6, 10, 2018. IEEE. ![Wireless Sensor Networks for Irrigation in Crops Using Multivariate Regression Models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 2 downloads The present wireless sensor network system shows a data analysis approach within greenhouses in short cycle crops. This research, on the one hand, is carried out to reduce water consumption and improve the product by predicting the right moment of the irrigation cycle through the evapotranspiration criterion. On the other hand, an efficient electronic system is designed under the electronic standard. To define the best model to define the next irrigation in the crops in base to ground humidity, the algorithms are compared for continuous and discontinuous multivariate regressions. The results are evaluated with different criteria of prediction errors. As a result, the linear regression with Support Vector Machine model is chosen for counting an average deviation error of 7.89% and an error variability of 4.48%. In addition, water consumption is reduced by 20%, achieving better quality products.
@inproceedings{
title = {Wireless Sensor Networks for Irrigation in Crops Using Multivariate Regression Models},
type = {inproceedings},
year = {2018},
keywords = {WSN,crops analysis,regression model},
pages = {1-6},
websites = {https://ieeexplore.ieee.org/document/8580322/},
month = {10},
publisher = {IEEE},
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citation_key = {Rosero-Montalvo2018b},
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abstract = {The present wireless sensor network system shows a data analysis approach within greenhouses in short cycle crops. This research, on the one hand, is carried out to reduce water consumption and improve the product by predicting the right moment of the irrigation cycle through the evapotranspiration criterion. On the other hand, an efficient electronic system is designed under the electronic standard. To define the best model to define the next irrigation in the crops in base to ground humidity, the algorithms are compared for continuous and discontinuous multivariate regressions. The results are evaluated with different criteria of prediction errors. As a result, the linear regression with Support Vector Machine model is chosen for counting an average deviation error of 7.89% and an error variability of 4.48%. In addition, water consumption is reduced by 20%, achieving better quality products.},
bibtype = {inproceedings},
author = {Rosero-Montalvo, Paul D. and Pijal-Rojas, Jose and Vasquez-Ayala, Carlos and Maya, Edgar and Pupiales, Carlos and Suarez, Luis and Benitez-Pereira, Henry and Peluffo-Ordonez, D.H.},
doi = {10.1109/ETCM.2018.8580322},
booktitle = {2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)}
}
Downloads: 2
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