Deep Learning for greenhouse internal temperature forecast


  • Juan M. Esparza-Gómez Universidad Autónoma de Zacatecas
  • Héctor A. Guerrero-Osuna Universidad Politécnica del Sur de Zacatecas
  • Gerardo Ornelas-Vargas Universidad Politécnica del Sur de Zacatecas
  • Luis F. Luque-Vega Centro de Investigación, Innovación y Desarrollo Tecnológico CIIDETEC-UVM, Universidad del Valle de México


RNN-LSTM, Temperature prediction, Deep Learning


The microclimate inside a greenhouse forecast has been a case of study in recent years; an adequate forecast of variables such as internal temperature helps farmers prevent losses in the harvest. In this investigation, the forecast of the greenhouse internal temperature is implemented through Recurrent Neural Networks (RNN) topology with Long-Short Term Memory (LSTM) algorithm. The analysis is performed with the many to one configuration for a sequence of three input elements and one output element for each of the year's four seasons. The metrics used for the analysis and validation of the data were the RMSE, MAE, R2, and Ceff. These metrics provide the level of efficiency and goodness of the RNN-LSTM showing how the variables considered provide significance to the forecast of one hour into the future of the internal temperature. It is shown that the LSTM algorithm within the RNN is an effective tool for a good internal temperature forecast in time series for each season, significantly helping the forecast of climatic variables inside a greenhouse.




How to Cite

Esparza-Gómez, J. M. ., Guerrero-Osuna, H. A., Ornelas-Vargas, G., & Luque-Vega, L. F. (2023). Deep Learning for greenhouse internal temperature forecast. International Journal of Combinatorial Optimization Problems and Informatics, 14(1), 86–94. Retrieved from