A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses

Abstract

Agricultural greenhouse production has to require a stable and acceptable environment, it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment parameters. Dynamic modeling based on machine learning methods, e.g., intelligent time series prediction modeling, is a popular and suitable way to solve the above issue. In this article, a systematic literature review on applying advanced time series models has been systematically conducted via a detailed analysis and evaluation of 61 pieces selected from 221 articles. The historical process of time series model application from the use of data and information strategies was first discussed. Subsequently, the accuracy and generalization of the model from the selection of model parameters and time steps, providing a new perspective for model development in this field, were compared and analyzed. Finally, the systematic review results demonstrate that, compared with traditional models, deep neural networks could increase data structure mining capabilities and overall information simulation capabilities through innovative and effective structures, thereby it could also broaden the selection range of environmental parameters for agricultural facilities and achieve environmental prediction end-to-end optimization via intelligent time series model based on deep neural networks.

Keywords

• Horticultural greenhouse environment
• Time series algorithms
• Prediction
• Deep neural networks