# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 7 Conclusions  
Based on an analysis of environmental factors that affect the indoor temperature, we added difference features, statistical features, and other time-series characteristics of the temperature, humidity, and air pressure using cross-features as the input of the nonlinear relationship features and applying the LightGBM model, linear regression, SVM, BP neural network, RBF neural network, and MLPRegressor for predictive analysis. In comparative tests using only the original features and adding time-series features, we found that, after adding time-series features, the prediction results of LightGBM were better than those of the other models, which revealed that LightGBM has better adaptability to time-series features. Compared with the LightGBM model, linear regression does not have strong recognition ability for time-series features. Therefore, applying time-series features to linear regression does not help to identify high-dimensional complex feature information. Compared with the SVM, BP neural network, and RBF neural network, LightGBM took lesstime in the training process and did not easily fall into a local optimum. Compared with the dynamic model and MLPRegressor mentioned in the literature [10; 11], LightGBM is not limited to small ecosystems but is applicable to other situations. Furthermore, LightGBM had the smallest mean value and standard deviation of the absolute error, indicating that LightGBM made the most stable prediction among the various models.  
It is therefore concluded that the greenhouse temperature prediction model based on time-series features and LightGBM not only trains the model in a shorter time but also more accurately predicts the greenhouse temperature.  
The growth of crops is strongly dependent on aspects of the natural environment, such as light, temperature, humidity, and air pressure, which affect the crop yield and quality. In the growth cycle of crops, all kinds of chemical reactions need to occur within the crop material at an appropriate temperature, and therefore temperature is often the decisive factor of crop growth and development. The LightGBM prediction results are important reference values for greenhouse temperature control and play an important role in improving the crop yield and quality and promoting the development of smart agriculture.  
Owing to the limitation of data samples, the reported experiment has not been verified on other datasets outside the dataset for 2019 used in the present study. Future work will obtain more complete original data affecting the greenhouse temperature for further testing of the model and expanding the use scenario.  
Conceptualization, Q.C.; methodology, Q.C.; software, Y.W.; formal analysis, J.Y. ([PERSON]); writing--original draft preparation, Y.W.; writing--review and editing, Q.C. and J.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Chongqing Federation of Social Sciences, grant number 2021 NDYB101.  
Not applicable.  
The experimental data came from an online competition (2020 iFLYTEK A.I., url: [[http://challenge.xfyun.cn/topic/info?type=temperature](http://challenge.xfyun.cn/topic/info?type=temperature)]([http://challenge.xfyun.cn/topic/info?type=temperature](http://challenge.xfyun.cn/topic/info?type=temperature)) (accessed on 1 July 2020)) held by iFLYTEK.  
The authors would like to thank the anonymous reviewers for their valuable comments on our paper.  
The authors declare no conflict of interest.