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Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM

Authors :
Qiong Cao
Yihang Wu
Jia Yang
Jing Yin
Source :
Applied Sciences, Vol 13, Iss 3, p 1610 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

A method of establishing a prediction model of the greenhouse temperature based on time-series analysis and the boosting tree model is proposed, aiming at the problem that the temperature of a greenhouse cannot be accurately predicted owing to nonlinear changes in the temperature of the closed ecosystem of a greenhouse featuring modern agricultural technology and various influencing factors. This model comprehensively considers environmental parameters, including humidity inside and outside the greenhouse, air pressure inside and outside the greenhouse, and temperature outside the greenhouse, as well as time-series changes, to make a more accurate prediction of the temperature in the greenhouse. Experiments show that the R2 determination coefficients of different prediction models are improved and the mean square error and mean absolute error are reduced after adding time-series features. Among the models tested, LightGBM performs best, with the mean square error of the prediction results of the model decreasing by 18.61% after adding time-series features. Comparing with the support vector machine, radial basis function neural network, back-propagation neural network, and multiple linear regression model after adding time-series features, the mean square error is 11.70% to 29.12% lower. Furthermore, the fitting degree of LightGBM is the best among the models. The prediction results of LightGBM therefore have important application value in greenhouse temperature control.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.11d51f61e2fd4956a60172a53e137a74
Document Type :
article
Full Text :
https://doi.org/10.3390/app13031610