Smart & Green: An Internet-of-Things Framework for Smart Irrigation

Clicks: 287
ID: 112888
2019
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Abstract
Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, the data are not guaranteed to be free of discrepant values (i.e., outliers), which can damage the precision of irrigation management. Furthermore, data from different sources must fit into the same temporal window required for irrigation management and the data preprocessing must be dynamic and automatic to benefit users of the irrigation management plan. In this paper, we propose the Smart&Green framework to offer services for smart irrigation, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture. Outlier removal techniques allow for more precise irrigation management. For fields without soil moisture sensors, the prediction model estimates the matric potential using weather, crop, and irrigation information. We apply the predicted matric potential approach to the Van Genutchen model to determine the moisture used in an irrigation management scheme. We can save, on average, between 56.4% and 90% of the irrigation water needed by applying the Zscore, MZscore and Chauvenet outlier removal techniques to the predicted data.
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campos2019sensorssmart Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nidia G. S. Campos;Atslands R. Rocha;Rubens Gondim;Ticiana L. Coelho da Silva;Danielo G. Gomes;G. S. Campos, Nidia;Rocha, Atslands R.;Gondim, Rubens;Coelho da Silva, Ticiana L.;Gomes, Danielo G.;
Journal sensors
Year 2019
DOI
10.3390/s20010190
URL
https://www.mdpi.com/1424-8220/20/1/190/htm
Keywords
iot smart agriculture soil moisture prediction

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