https://socictopen.socict.org/files/original/de2185feeb517ba10e84ed5cc29a4aef.pdf c4f824b02646af1d52f7e01600f390e5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Smart &amp; Green: An Internet-of-Things Framework for Smart Irrigation Creator An entity primarily responsible for making the resource Nidia G. S. Campos, Atslands R. Rocha, Rubens Gondim, Ticiana L. Coelho da Silva, Danielo G. Gomes Description An account of the resource 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&amp;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. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource IoT, Soil moisture prediction, smart agriculture Identifier An unambiguous reference to the resource within a given context 10.3390/s20010190 Source A related resource from which the described resource is derived Sensors Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chemical technology Relation A related resource <a href="https://www.mdpi.com/1424-8220/20/1/190" target="_blank" rel="noreferrer noopener">https://www.mdpi.com/1424-8220/20/1/190</a>