Simple model for predicting hourly air temperatures inside Chinese solar greenhouses
Keywords:
solar greenhouse, hourly temperature, prediction model, Bezier curve equationAbstract
For an efficient energy greenhouse, temperature is the most important climate parameter, which not only affects crop growth and health but also determines the management of energy consumption. So reliable monitoring of temperature is of great significance, and often hourly values are required. However, due to the low level of automation for Chinese solar greenhouse, the loss or poor quality of climate data often occurs. In order to accurately supplement the missing data, as well as for the generation of future temperature, a 24-hour indoor temperature prediction model was established. It uses a piecewise Bezier curve equation that takes the characteristic temperature as the control point which was determined by the outside weather recording. The 130 d of observed hourly temperature data were used to build and validate the model, and the results showed that the temperature model proposed was accurate and sufficient for the simulation of the trend curve of hourly temperature change inside a solar greenhouse. (EF=0.98, R2=0.89). After validation, this temperature model proposed can be useful for the quantitative analysis of crop growth and optimal management. Keywords: solar greenhouse, hourly temperature, prediction model, Bezier curve equation DOI: 10.25165/j.ijabe.20231605.6922 Citation: Dong Q X, Liu J C, Qu M. Simple model for predicting hourly air temperatures inside Chinese solar greenhouses. Int J Agric & Biol Eng, 2023; 16(5): 56–60.References
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greenhouse. Northern Horticulture, 2014; 4: 37–41. (in Chinese).
[2] Yang Z C. Hourly ambient air humidity fluctuation evaluation and
forecasting based on the least-squares Fourier-model. Measurement, 2019; 133: 112–123.
[3] Chen W H, You F Q. Efficient greenhouse temperature control with datadriven robust model predictive. 2020 American Control Conference
(ACC), 2020; pp.1986–1991. doi:10.23919/ACC45564.2020.9147701.
[4] Matinez-Riuz A, Lopez-Cruz I L, Ruiz-Garcia A, Pineda-Pineda J, Prado-Hernandez J V. HortSyst: A dynamic model to predict growth, nitrogen uptake, and transpiration of greenhouse tomatoes. Chilean Journal of Agricultural Research, 2019; 79(1): 89–102.
[5] Rodriguez D, deVoil P, Hudson D, Brown J N, Hayman P, Marrou H, et al. Predicting optimum crop designs using crop models and seasonal climate forecasts. Scientific Reports, 2018; 8: 2231.
[6] Gao P, Tian Z W, Lu Y Q, Lu M, Zhang H H, Wu H R, Hu J. A decisionmaking model for light environment control of tomato seedlings aiming at the knee point of light-response curves. Computers and Electronics in Agriculture, 2022; 198(6): 107103.
[7] Pu B S, Zheng H Y, Huang Y Y, Wu J C. Development status and
suggestions of China’s greenhouse agricultural facilities and equipment
technology. Jiangsu Agricultural Sciences, 2019; 47(14): 13–18. (in
Chinese)
[8] Mao H P, Jing C, Chen Y. Research progress and prospection control
methods of greenhouse environment. Transactions of the CSAM, 2018;
49(2): 1–13. (in Chinese)
[9] Hao X, Jia J D, Chu X F, Tao S, Gao W L, Wang M J. Greenhouse crop
model: Methods, trends and future perspectives. International Agricultural Engineering Journal, 2020; 28(4): 386–398
[10] Shen C Y. Research on temperature control model of cucumber cultivation in solar greenhouse based on data-driven. Master dissertation. Shenyang: Shenyang Agricultural University, 2020; 59p. (in Chinese)
[11] Qin L L, Ma G Q, Chu Z D, Wu G. Modeling and control of greenhouse
temperature-humidity system based on grey prediction model. Transactions of the CSAE, 2016; 32(S1): 233–241. (in Chinese)
[12] Xiao X P. Design of spatial distribution detection and simulation system of greenhouse temperature field and optimization of sensor location. Master dissertation. Baoding: Hebei Agricultural University, 2020; 66p.
[13] Francik S, Kurpaska S. The use of artificial neural networks for forecasting of air temperature inside a heated foil tunnel. Sensors, 2020; 20(3): 652.
[14] Yu H H, Chen Y Y, Hassan S G, Li D L. Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO. Computers and Electronics in Agriculture, 2016; 122: 94–102.
[15] Reyes-Rosas A, Molina-Aiz F D, López A, Valera D L. A simple model to predict air temperature inside a Mediterranean greenhouse. Acta Horticulturea, 2017; 1182: 95–104.
[16] Jun W, Yu H. Y. 24-hour greenhouse temperature prediction model based on the weather forecast. Proceedings of the 2015 Information Technology and Mechatronics Engineering Conference, 2015; pp: 191–195.
[17] Narendra K, Ravi P. Review of greenhouse management using embedded system. International Journal of Science and Research (IJSR), 2015; 4(10): 2112–2115.
[18] Saiz-Rubio V, Rovira-Más F. From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 2020; 10(2): 207.
[19] O'Grady M J, Langton D, O'Hare G M P. Edge computing: A tractable
model for smart agriculture? Artificial Intelligence in Agriculture, 2019; 3:
42–51.
[20] Zhang L J, Wang L, Tan L Y, Su J L. Python practice of data analysis and mining. Beijing: China Machine Press, 2016; 335p.
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Published
2023-12-29
How to Cite
Dong, Q., Liu, J., & Qu, M. (2023). Simple model for predicting hourly air temperatures inside Chinese solar greenhouses. International Journal of Agricultural and Biological Engineering, 16(5), 56–60. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6922
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Animal, Plant and Facility Systems
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