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Data-driven evaluation of machine learning models for climate control in operational smart greenhouses

Published: 01 January 2023 Publication History

Abstract

Nowadays, human overpopulation is stressing our ecosystems in different ways, agriculture being a critical example as different predictions point towards food shortages in the near future. Accordingly, smart farming is becoming key to the optimization of natural resources so that different crops can be grown efficiently, consuming as few resources as possible. In particular, greenhouses have proved to be an effective way of producing a high volume of vegetables/fruits in a reduced space and within a short time span. Hence, optimizing greenhouse functioning results in less water use and nutrient consumption, less energy use, faster growth, and better product quality. In this article, we carry out an in-depth analysis of different machine learning (ML) models to improve climate control in smart greenhouses. As part of the analysis of the techniques we also considered 3 ways of pre-processing the data, as well as 12-hour and 24-hour forecasting. We focus on forecasting the indoor air temperature of an operational smart greenhouse, i.e. assessing the data anomalies that are inherently present in these environments due to the instability of IoT infrastructures. Several ML models are adapted to time series forecasting to provide an overview of these techniques and to find out which one performs better in this particular scenario. Our results show that, after statistically validating the results, the Random Forest Regression technique gives the best overall result with a mean absolute error of less than 1 degree Celsius.

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Published In

cover image Journal of Ambient Intelligence and Smart Environments
Journal of Ambient Intelligence and Smart Environments  Volume 15, Issue 1
Current Trends in Energy Management, Sustainability and Security for Intelligent Environments
2023
109 pages

Publisher

IOS Press

Netherlands

Publication History

Published: 01 January 2023

Author Tags

  1. Precision agriculture
  2. artificial intelligence
  3. machine learning
  4. temperature forecasting
  5. smart greenhouses

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