IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories
| Autor: | André Sales Mendes, Rodrigo Marques de Figueiredo, Marcio Rosa da Silva, Luis Augusto Silva, Bruno Guilherme Martini, Regina Célia Espinosa Modolo, Jorge Luis Victória Barbosa, Gilson Augusto Helfer, Valderi Reis Quietinho Leithardt |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Ubiquitous computing
Computer science Mobile computing context awareness in agriculture Greenhouse Context (language use) 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Article Analytical Chemistry Statistics 0202 electrical engineering electronic engineering information engineering Context awareness computing in agriculture lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation prediction in agriculture business.industry 010401 analytical chemistry indoor agriculture 020206 networking & telecommunications Usability Atomic and Molecular Physics and Optics 0104 chemical sciences Technology acceptance model Internet of Things business context histories in agriculture |
| Zdroj: | Sensors, Vol 21, Iss 1631, p 1631 (2021) Sensors (Basel, Switzerland) Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP Sensors Volume 21 Issue 5 |
| ISSN: | 1424-8220 |
| Popis: | The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use. |
| Databáze: | OpenAIRE |
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