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

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

This paper follows an integrated approach of Internet of Things based sensing and machine learning for crop growth prediction in agriculture. A Dynamic Bayesian Network (DBN) relates crop growth associated measurement data to environmental control data via hidden states. The measurement data, having...

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Bibliographic Details
Main Authors: Alexander Kocian, Giulia Carmassi, Fatjon Cela, Luca Incrocci, Paolo Milazzo, Stefano Chessa
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Sensors
Subjects:
prediction
sigmoid
time-series
dynamic Bayesian network
missing data
evapotranspiration
Online Access:https://www.mdpi.com/1424-8220/20/11/3246

Internet

https://www.mdpi.com/1424-8220/20/11/3246

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