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IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors (Basel, Switzerland), 2021-02, Vol.21 (5), p.1631 [Peer Reviewed Journal]

2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21051631 ;PMID: 33652603

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  • Title:
    IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories
  • Author: Martini, Bruno Guilherme ; Helfer, Gilson Augusto ; Barbosa, Jorge Luis Victória ; Espinosa Modolo, Regina Célia ; da Silva, Marcio Rosa ; de Figueiredo, Rodrigo Marques ; Mendes, André Sales ; Silva, Luís Augusto ; Leithardt, Valderi Reis Quietinho
  • Subjects: Agriculture ; computing in agriculture ; Context ; context awareness in agriculture ; context histories in agriculture ; Cultivation ; Farms ; Greenhouses ; indoor agriculture ; Internet of Things ; Mobile computing ; Neural networks ; prediction in agriculture ; Protocol (computers) ; Regression analysis ; Sensors ; Technology utilization ; Ubiquitous computing
  • Is Part Of: Sensors (Basel, Switzerland), 2021-02, Vol.21 (5), p.1631
  • Description: 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 (R , 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 (R ), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R ), 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.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s21051631
    PMID: 33652603
  • Source: Open Access: PubMed Central
    DOAJ Directory of Open Access Journals
    GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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