Smart&Green: Um Framework de Internet das Coisas para Agricultura Inteligente

• Ano de defesa: 2020
• Autor(a) principal: Campos, Nídia Glória da Silva
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Agricultura de precisão; Agricultura inteligente; Internet das coisas; Framework; Predição de umidade do solo; Teleinformática
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/53216

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 time interval 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 agriculture, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management of ’Turno de Rega’, Water Balance, and Matric Potential. 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 of water balance or matric potential. We can save, on average,between 9% and 90% of the irrigation water needed by applying to the predicted data the Zscore, MZscore, and Chauvenet outlier removal techniques and the functions Mean and Maximum as redundant fusion techniques.