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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1270544

Assessing The Stability of Indoor Farming Systems Using Data Outlier Detection

Provisionally accepted
Jean P. Pompeo Jean P. Pompeo 1Ziwen Yu Ziwen Yu 1*Chi Zhang Chi Zhang 1Songzi Wu Songzi Wu 1Ying Zhang Ying Zhang 1Celina Gomez Celina Gomez 2Melanie Correll Melanie Correll 1
  • 1 University of Florida, Gainesville, United States
  • 2 Purdue University, West Lafayette, Indiana, United States

The final, formatted version of the article will be published soon.

    This study investigates the quality of air temperature data collected from a small-scale Controlled Environment Agriculture (CEA) system using low-cost IoT sensors during lettuce cultivation at four different temperatures. The methodology includes a generalized linear model regression analysis to examine the correlation between cumulative agricultural operations (Agr.Ops) and z-scores of air temperature residuals, assessing system stability. Outliers were identified and analyzed to determine their impact on system performance. Residual distribution and curve fitting revealed a log-normal distribution as the best fit for the sensor data. Regression analysis showed a strong inverse relationship between Agr.Ops and residual z-scores, suggesting that Agr.Ops contribute to outlier presence and impact system stability. The study highlights that system stability in CEA is influenced by the quality of data, with outliers indicating potential issues such as sensor noise, drift, or other uncertainties. The findings suggest that cumulative Agr.Ops affect system stability differently across trials, with some showing increased resistance to these operations over time. The alternative decomposition method used in this study effectively identified outliers and provided insights into system functionality. Future research should focus on improving surrogate data models, refining sensor selection, and ensuring data redundancy to enhance system reliability. The proposed method offers a promising approach for monitoring and managing uncertainties in indoor farming systems to improve their stability and efficiency.

    Keywords: arduino, Low-cost sensor, outlier, uncertainty, decomposition, regression

    Received: 31 Jul 2023; Accepted: 04 Sep 2024.

    Copyright: © 2024 Pompeo, Yu, Zhang, Wu, Zhang, Gomez and Correll. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ziwen Yu, University of Florida, Gainesville, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.