Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM

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.

Long Short-Term Memory Recurrent Neural Network and Extreme Gradient Boosting Algorithms Applied in a Greenhouse’s Internal Temperature Prediction

“…A study in [39] presented a model to predict the temperature inside a greenhouse using time series analysis and the LightGBM. The model considered environmental factors such as humidity, air pressure inside and outside the greenhouse, external temperature, and time series data to make the temperature predictions.…”

Prediction of Internal Temperature in Greenhouses Using the Supervised Learning Techniques: Linear and Support Vector Regressions

“…Instead, datasets are gathered from other projects or repositories to make AI predictions. Some approaches involve adding feature functions to time series through techniques like LR, SVR, RMM, or LSTM for predictive analysis [39,40]. Additionally, certain studies employ methods like the Bayesian optimization-based multi-head attention encoder to forecast changes in climate time series accurately [41].…”

Prediction of Greenhouse Indoor Air Temperature Using Artificial Intelligence (AI) Combined with Sensitivity Analysis

“…On the other hand, growers' awareness of the upcoming conditions during the day can lead to quicker reactions and better management of energy resources in the greenhouse [9]. Therefore, many studies have been conducted since the early 20th century to model the greenhouse energy loads [10,11], as well as indoor parameters such as temperature [12], humidity [13], light intensity [14], CO 2 [15], etc. The basis for all these research studies is the initial modeling of the greenhouse conditions based on external variables such as temperature, humidity, wind speed, radiation level, etc.…”