1. Ben Ali, R. E., M. Aridhi, and A. Mami. 2016. Fuzzy Logic Controller of temperature and humidity inside an agricultural greenhouse. IEEE Journals & Magazines.
2. Boaventura, L., C. Couto, and A. E. B. Ruano. 2000. A greenhouse climate multivariable predictive controller, Acta Horticulturae, ISHS 534: 269-276.
3. Bot G. P. A. 1983. Greenhouse Climate: From Physical Processes to a Dynamic Model. Ph.D. dissertation, Wageningen Agricultural University, Wageningen, The Netherlands, 240 pp.
4. Coelho, J. P., J. Boaventura, and P. B. Moura Oliveira. 2002. Solar radiation prediction methods applied to improve greenhouse climate control, in: World Congress of Computers in Agriculture and Natural Resources, 13-15 March. pp. 154-161.
5. Dariouchy, A., E. Aassif, K. Lekouch, L. Bouirden, and G. Maze. 2009. Prediction of the intern parameters tomato greenhouse in a semi-arid area using a time-series model of artificial neural networks. Measurement 42: 456-463.
6. Ehret, D., L. Hill, B. D. T. Helmer, and D. R. Edward. 2011. Neural network modeling of greenhouse tomato yield, growth and water use from automated crop monitoring data. Computers and Electronics in Agriculture 79: 82-89.
7. Falamarzi, Y., N. Palizdan, Y. F. Huang, and T. S. Lee. 2014. Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs). Agricultural Water Management 140: 26-36.
8. Feng, L. X., Q. L. Lin, M. G. Qi, and W. Gang. 2016. Modeling Greenhouse Temperature by Means of PLSR and BPNN. 35th Chinese Control Conference. July 27-29, Chengdu, China.
9. Ferreira, P. M., E. A. Faria, and A. E. Ruano. 2002. Neural network models in greenhouse air temperature prediction. Neurocomputing 43 (1-4): 51-75.
10. Gupta, R., G. N. Tiwari, G. N. Kumar, and Y. Gupta. 2012. Calculation of total solar fraction for different orientation of greenhouse using 3D-shadow analysis in Auto-CAD. Energy and Buildings 47: 27-34.
11. He, F., and C. Ma. 2010. Modeling greenhouse air humidity by means of artificial neural network and principal component analysis. Computers and Electronics in Agriculture 71S (2010): S19-S23.
12. Hill, J. 2006. Dynamic modeling and energy use in a nursery greenhouse. MSc thesis.
13. Lachouri, C. E., K. H. Mansouri, M. M. Lafifi, and A. Belmeguenai. 2016. Adaptive Neuro-Fuzzy Inference Systems for Modeling Greenhouse Climate. International Journal of Advanced Computer Science and Applications 7: 12-18.
14. Linker, R., and I. Seginer. 2004. Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models. Mathematics and Computers in Simulation 65: 19-29.
15. Manuel, A., R. Francisco, R. Armando, and B. Manuel. 2005. Discrete-time nonlinear FIR models with integrated variables for greenhouse indoor temperature simulation, in: Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC’05, pp. 4158-4162.
16. Rodriguez, J. D., A. Perez, and J. A. Lozano. 2010. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceeding of the international joint conference on artificial intelligence, 32: 569-575.
17. Rohani, A., M. Taki, and M. Abdollahpour. 2018. A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). Renewable Energy 115: 411-422
18. Rohani, A., M. H. Abbaspour-Fard, and Sh. Abdolahpour. 2011. Prediction of tractor repair and maintenance costs using artificial neural network. Expert Sys. Applications 38: 8999-9007.
19. Sethi, V. P. 2009. On the selection of shape and orientation of a greenhouse: thermal modeling and experimental validation. Solar Energy 83: 21-38.
20. Sethi, V. P., and R. K. Dubey. 2008. Optimal space utilization of a greenhouse using multi-rack tray system: Thermal modeling and experimental validation. Energy Conversion and Management 49: 2890-2899.
21. Shukla, A., G. N. Tiwari, and M. S. Sodha. 2006. Thermal modeling for greenhouse heating by using thermal curtain and an earth–air heat exchanger. Building and Environment 41 (7): 843-850.
22. Singh, R. D., and G. N. Tiwari. 2010. Energy conservation in the greenhouse system: A steady state analysis. Energy 35: 2367-2373.
23. Taki, M., A. Rohani, M. Rahmati-Joneidabad. 2018b. Solar thermal simulation and applications in greenhouse. Information Processing in Agriculture 5: 83-113.
24. Taki, M., S. Abdanan Mehdizadeh, A. Rohani, M. Rahnama, and M. Rahmati-Joneidabad. 2018a. Applied machine learning in greenhouse simulation; new application and analysis. Information Processing in Agriculture, https://doi.org/10.1016/j.inpa.2018.01.003.
25. Taki, M., Y. Ajabshirchi, and A. Mahmoudi. 2012. Prediction of output energy for wheat production using artificial neural networks in Esfahan province of Iran. Journal of Agricultural Technology 8 (4): 1229-1242.
26. Taki, M., Y. Ajabshirchi, S. F. Ranjbar, A. Rohani, and M. Matloobi. 2017. Evaluation of heat transfer mathematical models and multiple linear regression to predict the inside variables in semi-solar greenhouse. Journal of Agricultural Machinery 7 (1): 2014-220. (In Farsi).
27. Taki, M., Y. Ajabshirchi, S. F. Ranjbar, A. Rohani, and M. Matloobi. 2016. Heat transfer and MLP neural network models to predict inside environment variables and energy lost in a semi-solar greenhouse. Energy and Buildings 110: 314-329.
28. Vadiee, A., and V. Martin. 2013. Energy analysis and thermo economic assessment of the closed greenhouse – The largest commercial solar building. Applied Energy http://dx.doi.org/10.1016/j.apenergy.2013.06.051.
29. Van Ooteghem, R. J. C. 2007. Optimal Control Design for a Solar Greenhouse, Systems and Control. Wageningen: Wageningen University.
30. Van Straten, G., G. Van Willigenburg, E. Van Henten, and R. Van Oothghem. 2011. Optimal control of greenhouse cultivation. CRC press, Taylor and Francis, New York.