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Bayesian Sigmoid-Type Time Series Forecasting with Missing Data for Greenhouse Crops.

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  • 1Department of Computer Science, University of Pisa, 56127 Pisa, Italy.

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|June 11, 2020
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Summary
This summary is machine-generated.

This study uses Internet of Things (IoT) sensors and machine learning to predict crop growth. A Dynamic Bayesian Network (DBN) accurately forecasts micro-tomato development up to three weeks in advance, even with limited data.

Keywords:
IoTMicroTomdynamic Bayesian networkevapotranspirationleaf area indexmissing datapredictionsigmoidtime-series

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Area of Science:

  • Agricultural Technology
  • Machine Learning in Agriculture
  • Internet of Things (IoT)

Background:

  • Accurate crop growth prediction is crucial for optimizing agricultural practices and ensuring food security.
  • Integrating real-time environmental data with advanced analytical models can improve prediction accuracy.
  • Limited availability of crop measurement data often hinders predictive modeling.

Purpose of the Study:

  • To develop and evaluate an integrated approach using IoT sensing and machine learning for crop growth prediction.
  • To apply a Dynamic Bayesian Network (DBN) model for forecasting crop development based on environmental and measurement data.
  • To assess the DBN model's predictive performance under conditions of scarce measurement data.

Main Methods:

  • An integrated approach combining Internet of Things (IoT) sensors and machine learning.
  • Utilizing a Dynamic Bayesian Network (DBN) to model relationships between environmental data (temperature, solar irradiance) and crop measurements (Leaf Area Index, Evapotranspiration).
  • Employing the Expectation-Maximization algorithm for parameter inference and hidden state estimation in the DBN model.

Main Results:

  • The proposed DBN model successfully predicted crop growth up to three weeks ahead, despite scarce measurement data.
  • The model demonstrated high-quality predictions for Leaf Area Index (LAI) and Evapotranspiration (ET).
  • Environmental parameters like Growing Degree Days (GDD) and solar irradiance were effectively integrated into the prediction model.

Conclusions:

  • The integrated IoT and DBN approach offers a robust solution for accurate crop growth prediction in precision agriculture.
  • This method can overcome challenges posed by limited data availability, enhancing agricultural forecasting capabilities.
  • The findings support the application of advanced machine learning techniques for optimizing crop management and yield prediction.