Modeling and simulation of temperature control system in plant factory using energy balance
Keywords:
plant factory, temperature control system, mechanism simulation, random forest, cart model, generalization errorAbstract
Closed production systems, such as plant factories and vertical farms, have emerged to ensure a sustainable supply of fresh food, to cope with the increasing consumption of natural resource for the growing population. In a plant factory, a microclimate model is one of the direct control components of a whole system. In order to better realize the dynamic regulation for the microclimate model, energy-saving and consumption reduction, it is necessary to optimize the environmental parameters in the plant factory, and thereby to determine the influencing factors of atmosphere control systems. Therefore, this study aims to identify accurate microclimate models, and further to predict temperature change based on the experimental data, using the classification and regression trees (CART) algorithm. A random forest theory was used to represent the temperature control system. A mechanism model of the temperature control system was proposed to improve the performance of the plant factories. In terms of energy efficiency, the main influencing factors on temperature change in the plant factories were obtained, including the temperature and air volume flow of the temperature control device, as well as the internal relative humidity. The generalization error of the prediction model can reach 0.0907. The results demonstrated that the proposed model can present the quantitative relationship and prediction function. This study can provide a reference for the design of high-precision environmental control systems in plant factories. Keywords: plant factory, temperature control system, mechanism simulation, random forest, cart model, generalization error DOI: 10.25165/j.ijabe.20211403.6114 Citation: Zhang M Q, Zhang W, Chen X Y, Wang F, Wang H, Zhang J S, et al. Modeling and simulation of temperature control system in plant factory using energy balance. Int J Agric & Biol Eng, 2021; 14(3): 66–75.References
[1] Xu L H, Su Y P, Liang Y M. Requirement and current situation of control-oriented microclimate environmental model in greenhouse system. Transactions of the CSAE, 2013; 29(19): 1–15. (in Chinese)
[2] Kozai T. Resource use efficiency of closed plant production system with artificial light: concept, estimation and application to plant factory. Proceedings of the Japan Academy. Series B, Physical and Biological Sciences, 2013; 89(10): 447–460.
[3] Takakura T. Research exploring greenhouse environment control over the last 50 years. Int J Agric & Biol Eng, 2019; 12(5): 1–7.
[4] Ren Y Z, Wang M J, Saeeda I A, Chen X R, Gao W L. Progress, problems and prospects for standardization of greenhouse-related technologies. Int J Agric & Biol Eng, 2018; 11(1): 40–48.
[5] Song J W. Grow light for plant factory using quantum dot LED. Journal of International Council on Electrical Engineering, 2016; 6(1): 13–16.
[6] Ma X, Lin C H, Qi L, Jiang L K, Tan Y X, Liang Z W, et al. Effect of different lighting quality and intensities on quality of rice seedling by greenhouse stereoscopic nursing. Transactions of the CSAE, 2015; 31(11): 228–235. (in Chinese)
[7] Wu Y, Li L, Li S S, Wang H K. Optimal control algorithm of fertigation system in greenhouse based on EC model. Int J Agric & Biol Eng, 2019; 12(3): 118–125.
[8] Qi F, Zhou X Q, Ding X M, Wei X M. Discussion on classification method of protected agricultural engineering technology. Transactions of the CSAE, 2012; 28(10): 1–7. (in Chinese)
[9] Stefański P, Siedlarz P, Matysik P, Rybka K. Usefulness of LED lightings in cereal breeding on example of wheat, barley and oat seedlings. Int J of Agric & Biol Eng, 2019; 12(6): 32–37.
[10] Dai J F, Luo W H, Xu G B, Li Y X, Wang X C, Ye J, et al. Simulation of greenhouse air temperature, humidity and canopy transpiration in Yangtze River Delta. Transactions of the CSAE, 2005; 5: 107–112. (in Chinese)
[11] Acquah S J, Yan H F, Zhang C, Wang G Q, Zhao B S, Wu H M, et al. Application and evaluation of Stanghellini model in the determination of crop evapotranspiration in a naturally ventilated greenhouse. Int J Agric & Biol Eng, 2018; 11(6): 95–103.
[12] Al Mamun Hossain S A, Wang L X, Liu H S. Improved greenhouse cucumber production under deficit water and fertilization in Northern China. Int J Agric & Biol Eng, 2018; 11(4): 58–64.
[13] Li J, Qin L L, Yue D Z, Wu G, Xue M S, Chen W, et al. Experiment greenhouse temperature system modeling and simulation. Journal of System Simulation, 2008; 7: 1869–1875. (in Chinese)
[14] Meng L L, Yang Q C, Wen J, Zhang Y, Fang H. Visual simulation model for thermal environment in Chinese solar greenhouse. Transactions of the CSAE, 2009; 25(1): 164–170. (in Chinese)
[15] Kozai T. Towards sustainable plant factories with artificial lighting (PFALs) for achieving SDGs. Int J Agric & Biol Eng, 2019; 12(5): 28–37.
[16] Liu N, Ji F, Xu L J, He D X. Effects of LED light quality on the growth of pepper seedling in plant factory. Int J Agric & Biol Eng, 2019; 12(5): 44–50.
[17] Lin D L. Hybrid modeling and analysis of greenhouse control systems. Doctoral dissertation. Shanghai: Shanghai University, 2010; 171p. (in Chinese)
[18] Wang Xiaochan. Research on microclimate simulation and energy consumption prediction of modern greenhouse in subtropical area. Nanjing: Nanjing Agriculture University, 2003. (in Chinese)
[19] Qin L L, Ma G Q, Chu Z D, Wu G. Molding and control of greenhouse temperature-humidity system based on grey prediction model. Transactions of the CSAE, 2016; 32(S1): 233–241. (in Chinese)
[20] Jiang Y X, Qin L L, Shi C, Wu G. Modern greenhouse humidity system modeling based on mechanism model. Journal of Jiangnan University (Natural Science Edition), 2013; 12(5): 535–540. (in Chinese)
[21] Zhang X, He D X, Niu G H, Yan Z N, Song J X. Effects of environment lighting on the growth, photosynthesis, and quality of hydroponic lettuce in a plant factory. Int J Agric & Biol Eng, 2018; 11(2): 33–40.
[22] Wu C F, Deng J S, Wang K, Ma L G, Tahmassebi A R S. Object-based classification approach for greenhouse mapping using Landsat-8 imagery. Int J Agric & Biol Eng, 2016; 9(1): 79–88.
[23] Wang L S, Hou T, Jiang M. Improved multi-objective evolutionary algorithm for optimization control in greenhouse environment. Transactions of the CSAE, 2014; 30(5): 131–137. (in Chinese)
[24] Graamans L, van den Dobbelsteen A, Meinen E, Stanghellini C. Plant factories; crop transpiration and energy balance. Agricultural Systems, 2017, 153: 138–147.
[25] Dong C, Shao L Z, Fu Y M, Wang M J, Xie B Z, Yu J, et al. Evaluation of wheat growth, morphological characteristics, biomass yield and quality in Lunar Palace-1, plant factory, green house and field systems. Acta Astronautica, 2015; 111: 102–109.
[26] Ye H C, Huang W J, Huang S Y, Cui B. Identification of banana fusarium wilt using supervised classification algorithms with AV-based multi-spectral imagery. Int J Agric & Biol Eng, 2020; 13(3): 136–142.
[27] Wang L J, Kong Y R, Yang X D, Xu Y, Liang L, Wang S G. Classification of land use in farming areas based on feature optimization random forest algorithm. Transactions of the CSAE, 2020; 36(4): 244–250. (in Chinese)
[28] Ping R, Zhou S S, Li D. Cost sensitive random forces classification algorithm for highly unbalanced data. Pattern Recognition and Artificial Intelligence, 2020; 33(3): 249–257. (in Chinese)
[29] Cheng Y S, He D X. A photosynthesis continuous monitoring system for CAM plants. Int J Agric & Biol Eng, 2019; 12(3): 141–146.
[30] Wang L N, Wang B R. Greenhouse microclimate environment adaptive control based on a wireless sensor network. Int J Agric & Biol Eng, 2020; 13(3): 64–69.
[31] Yan Z N, He D X, Niu G H, Zhou Q. Growth, nutritional quality, and energy use efficiency in two lettuce cultivars as influenced by white plus red versus red plus blue LEDs. Int J Agric & Biol Eng, 2020; 13(2): 33–40.
[32] Zheng J F, He D X, Ji F. Effects of light intensity and photoperiod on runner plant propagation of hydroponic strawberry transplants under LED lighting. Int J Agric & Biol Eng, 2019; 12(6): 26–31.
[33] Xu F, Shang C, Li H L, Xue X Z. Comparison of thermal and light performance in two typical Chinese solar greenhouses in Beijing. Int J Agric & Biol Eng, 2019; 12(1): 24–32.
[34] Song J X, Xu L, He D X, Tuskagoshi S. Estimating EC and ionic EC contribution percentage of nutrient solution based on ionic activity. Int J Agric & Biol Eng, 2019; 12(2): 42–48.
[35] Wang W S, Wang C, Pan D Y, Zhang Y K, Luo B, Ji J W. Effects of drought stress on photosynthesis and chlorophyll fluorescence images of soybean (Glycine max) seedlings. Int J Agric & Biol Eng, 2018; 11(2): 196–201.
[36] Han B Q, Li S L. Theory and application of refrigeration and air conditioning. Beijing: China Machine Press, 2002; 569p. (in Chinese)
[37] Ríos Salazar J D, Candelo-Becerra J E, Hoyos Velasco F E. Growing arugula plants using aeroponic culture with an automated irrigation system. Int J Agric & Biol Eng, 2020; 13(3): 52–56.
[38] Wang Y, Qi X N, Shao J F, Liu Y Z, Li Z X. Effects of light intensity at full growing stage on the growth and yield of different maize varieties. Journal of Jilin Agricultural University, 2008; 30(6): 769–773. (in Chinese)
[39] Wang F L, Dong Z G, Wu Z H, Fang K. Optimization of maize planting density and fertilizer application rate based on BP neural network. Transactions of the CSAE, 2017; 33(6): 92–99. (in Chinese)
[40] Cao Z F. Study on optimization of random forests algorithm. Doctoral dissertation. Beijing: Capital University of Economics and Business, 2014; 134p. (in Chinese)
[41] Zhang H W, Wang M W, Gan L X. Automatic text classification model based on random forest. Journal of Shandong University (Natural Science), 2006; 3: 139–143. (in Chinese)
[42] Hu Y R, Li J Q, Liu H G, Fan M P, Wang Y Z. Infrared spectral study on the origin identification of Boletus Tomentipes based on the random forest algorithm and data fusion strategy. Spectroscopy and Spectral Analysis, 2020; 40(5): 1495–1502. (in Chinese)
[43] Wu X Y, He J H, Zhang P, Hu J. Power system short-term load forecasting based on improved random forest with grey relation projection. Automation of Electric Power Systems, 2015; 39(12): 50–55. (in Chinese)
[44] Zheng L L, Xu J Y, Wang X L, Liu B G. Study on relationships between vegetation community distribution and topsoil factors based on random forests in shoaly wetlands of Poyang Lake. Soils, 2020; 52(2): 378–385. (in Chinese)
[45] Song X F, Jiao L C. Classification of hyperspectral remote sensing image based on sparse representation and spectral information. Journal of Electronics & Information Technology, 2012; 34(2): 268–272. (in Chinese)
[46] Lancashire P D, Bleiholder H, Den Boom T V, Langelüddeke P. A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology, 2008; 119(3): 561–601.
[47] Weber E, Bleiholder H. Erläuterungen zu den BBCH-dezimal-codes für die Entwicklungsstadien von Mais, Raps, Faba-bohne, Sonnenblume und Erbse-mit Abbildungen. Gesunde Pflanzen, 1990; 42(9): 308–321. (in German)
[48] Yin J, Liu X Y, Miao Y L, Gao Y, Qiu R C, Zhang M, et al. Measurement and prediction of tomato canopy apparent photosynthetic rate. Int J Agric & Biol Eng, 2019; 12(5): 156–161.
[49] Li H, Yin J, Zhang M, Sigrimis N, Gao Y, Zheng W G. Automatic diagnosis of strawberry water stress status based on machine vision. Int J Agric & Biol Eng, 2019; 12(1): 159–164.
[50] Wen D M, Ren A X, Ji T, Flores-Parra I M, Yang X T, Li M. Segmentation of thermal infrared images of cucumber leaves using K-means clustering for estimating leaf wetness duration. Int J Agric & Biol Eng, 2020; 13(3): 161–167.
[2] Kozai T. Resource use efficiency of closed plant production system with artificial light: concept, estimation and application to plant factory. Proceedings of the Japan Academy. Series B, Physical and Biological Sciences, 2013; 89(10): 447–460.
[3] Takakura T. Research exploring greenhouse environment control over the last 50 years. Int J Agric & Biol Eng, 2019; 12(5): 1–7.
[4] Ren Y Z, Wang M J, Saeeda I A, Chen X R, Gao W L. Progress, problems and prospects for standardization of greenhouse-related technologies. Int J Agric & Biol Eng, 2018; 11(1): 40–48.
[5] Song J W. Grow light for plant factory using quantum dot LED. Journal of International Council on Electrical Engineering, 2016; 6(1): 13–16.
[6] Ma X, Lin C H, Qi L, Jiang L K, Tan Y X, Liang Z W, et al. Effect of different lighting quality and intensities on quality of rice seedling by greenhouse stereoscopic nursing. Transactions of the CSAE, 2015; 31(11): 228–235. (in Chinese)
[7] Wu Y, Li L, Li S S, Wang H K. Optimal control algorithm of fertigation system in greenhouse based on EC model. Int J Agric & Biol Eng, 2019; 12(3): 118–125.
[8] Qi F, Zhou X Q, Ding X M, Wei X M. Discussion on classification method of protected agricultural engineering technology. Transactions of the CSAE, 2012; 28(10): 1–7. (in Chinese)
[9] Stefański P, Siedlarz P, Matysik P, Rybka K. Usefulness of LED lightings in cereal breeding on example of wheat, barley and oat seedlings. Int J of Agric & Biol Eng, 2019; 12(6): 32–37.
[10] Dai J F, Luo W H, Xu G B, Li Y X, Wang X C, Ye J, et al. Simulation of greenhouse air temperature, humidity and canopy transpiration in Yangtze River Delta. Transactions of the CSAE, 2005; 5: 107–112. (in Chinese)
[11] Acquah S J, Yan H F, Zhang C, Wang G Q, Zhao B S, Wu H M, et al. Application and evaluation of Stanghellini model in the determination of crop evapotranspiration in a naturally ventilated greenhouse. Int J Agric & Biol Eng, 2018; 11(6): 95–103.
[12] Al Mamun Hossain S A, Wang L X, Liu H S. Improved greenhouse cucumber production under deficit water and fertilization in Northern China. Int J Agric & Biol Eng, 2018; 11(4): 58–64.
[13] Li J, Qin L L, Yue D Z, Wu G, Xue M S, Chen W, et al. Experiment greenhouse temperature system modeling and simulation. Journal of System Simulation, 2008; 7: 1869–1875. (in Chinese)
[14] Meng L L, Yang Q C, Wen J, Zhang Y, Fang H. Visual simulation model for thermal environment in Chinese solar greenhouse. Transactions of the CSAE, 2009; 25(1): 164–170. (in Chinese)
[15] Kozai T. Towards sustainable plant factories with artificial lighting (PFALs) for achieving SDGs. Int J Agric & Biol Eng, 2019; 12(5): 28–37.
[16] Liu N, Ji F, Xu L J, He D X. Effects of LED light quality on the growth of pepper seedling in plant factory. Int J Agric & Biol Eng, 2019; 12(5): 44–50.
[17] Lin D L. Hybrid modeling and analysis of greenhouse control systems. Doctoral dissertation. Shanghai: Shanghai University, 2010; 171p. (in Chinese)
[18] Wang Xiaochan. Research on microclimate simulation and energy consumption prediction of modern greenhouse in subtropical area. Nanjing: Nanjing Agriculture University, 2003. (in Chinese)
[19] Qin L L, Ma G Q, Chu Z D, Wu G. Molding and control of greenhouse temperature-humidity system based on grey prediction model. Transactions of the CSAE, 2016; 32(S1): 233–241. (in Chinese)
[20] Jiang Y X, Qin L L, Shi C, Wu G. Modern greenhouse humidity system modeling based on mechanism model. Journal of Jiangnan University (Natural Science Edition), 2013; 12(5): 535–540. (in Chinese)
[21] Zhang X, He D X, Niu G H, Yan Z N, Song J X. Effects of environment lighting on the growth, photosynthesis, and quality of hydroponic lettuce in a plant factory. Int J Agric & Biol Eng, 2018; 11(2): 33–40.
[22] Wu C F, Deng J S, Wang K, Ma L G, Tahmassebi A R S. Object-based classification approach for greenhouse mapping using Landsat-8 imagery. Int J Agric & Biol Eng, 2016; 9(1): 79–88.
[23] Wang L S, Hou T, Jiang M. Improved multi-objective evolutionary algorithm for optimization control in greenhouse environment. Transactions of the CSAE, 2014; 30(5): 131–137. (in Chinese)
[24] Graamans L, van den Dobbelsteen A, Meinen E, Stanghellini C. Plant factories; crop transpiration and energy balance. Agricultural Systems, 2017, 153: 138–147.
[25] Dong C, Shao L Z, Fu Y M, Wang M J, Xie B Z, Yu J, et al. Evaluation of wheat growth, morphological characteristics, biomass yield and quality in Lunar Palace-1, plant factory, green house and field systems. Acta Astronautica, 2015; 111: 102–109.
[26] Ye H C, Huang W J, Huang S Y, Cui B. Identification of banana fusarium wilt using supervised classification algorithms with AV-based multi-spectral imagery. Int J Agric & Biol Eng, 2020; 13(3): 136–142.
[27] Wang L J, Kong Y R, Yang X D, Xu Y, Liang L, Wang S G. Classification of land use in farming areas based on feature optimization random forest algorithm. Transactions of the CSAE, 2020; 36(4): 244–250. (in Chinese)
[28] Ping R, Zhou S S, Li D. Cost sensitive random forces classification algorithm for highly unbalanced data. Pattern Recognition and Artificial Intelligence, 2020; 33(3): 249–257. (in Chinese)
[29] Cheng Y S, He D X. A photosynthesis continuous monitoring system for CAM plants. Int J Agric & Biol Eng, 2019; 12(3): 141–146.
[30] Wang L N, Wang B R. Greenhouse microclimate environment adaptive control based on a wireless sensor network. Int J Agric & Biol Eng, 2020; 13(3): 64–69.
[31] Yan Z N, He D X, Niu G H, Zhou Q. Growth, nutritional quality, and energy use efficiency in two lettuce cultivars as influenced by white plus red versus red plus blue LEDs. Int J Agric & Biol Eng, 2020; 13(2): 33–40.
[32] Zheng J F, He D X, Ji F. Effects of light intensity and photoperiod on runner plant propagation of hydroponic strawberry transplants under LED lighting. Int J Agric & Biol Eng, 2019; 12(6): 26–31.
[33] Xu F, Shang C, Li H L, Xue X Z. Comparison of thermal and light performance in two typical Chinese solar greenhouses in Beijing. Int J Agric & Biol Eng, 2019; 12(1): 24–32.
[34] Song J X, Xu L, He D X, Tuskagoshi S. Estimating EC and ionic EC contribution percentage of nutrient solution based on ionic activity. Int J Agric & Biol Eng, 2019; 12(2): 42–48.
[35] Wang W S, Wang C, Pan D Y, Zhang Y K, Luo B, Ji J W. Effects of drought stress on photosynthesis and chlorophyll fluorescence images of soybean (Glycine max) seedlings. Int J Agric & Biol Eng, 2018; 11(2): 196–201.
[36] Han B Q, Li S L. Theory and application of refrigeration and air conditioning. Beijing: China Machine Press, 2002; 569p. (in Chinese)
[37] Ríos Salazar J D, Candelo-Becerra J E, Hoyos Velasco F E. Growing arugula plants using aeroponic culture with an automated irrigation system. Int J Agric & Biol Eng, 2020; 13(3): 52–56.
[38] Wang Y, Qi X N, Shao J F, Liu Y Z, Li Z X. Effects of light intensity at full growing stage on the growth and yield of different maize varieties. Journal of Jilin Agricultural University, 2008; 30(6): 769–773. (in Chinese)
[39] Wang F L, Dong Z G, Wu Z H, Fang K. Optimization of maize planting density and fertilizer application rate based on BP neural network. Transactions of the CSAE, 2017; 33(6): 92–99. (in Chinese)
[40] Cao Z F. Study on optimization of random forests algorithm. Doctoral dissertation. Beijing: Capital University of Economics and Business, 2014; 134p. (in Chinese)
[41] Zhang H W, Wang M W, Gan L X. Automatic text classification model based on random forest. Journal of Shandong University (Natural Science), 2006; 3: 139–143. (in Chinese)
[42] Hu Y R, Li J Q, Liu H G, Fan M P, Wang Y Z. Infrared spectral study on the origin identification of Boletus Tomentipes based on the random forest algorithm and data fusion strategy. Spectroscopy and Spectral Analysis, 2020; 40(5): 1495–1502. (in Chinese)
[43] Wu X Y, He J H, Zhang P, Hu J. Power system short-term load forecasting based on improved random forest with grey relation projection. Automation of Electric Power Systems, 2015; 39(12): 50–55. (in Chinese)
[44] Zheng L L, Xu J Y, Wang X L, Liu B G. Study on relationships between vegetation community distribution and topsoil factors based on random forests in shoaly wetlands of Poyang Lake. Soils, 2020; 52(2): 378–385. (in Chinese)
[45] Song X F, Jiao L C. Classification of hyperspectral remote sensing image based on sparse representation and spectral information. Journal of Electronics & Information Technology, 2012; 34(2): 268–272. (in Chinese)
[46] Lancashire P D, Bleiholder H, Den Boom T V, Langelüddeke P. A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology, 2008; 119(3): 561–601.
[47] Weber E, Bleiholder H. Erläuterungen zu den BBCH-dezimal-codes für die Entwicklungsstadien von Mais, Raps, Faba-bohne, Sonnenblume und Erbse-mit Abbildungen. Gesunde Pflanzen, 1990; 42(9): 308–321. (in German)
[48] Yin J, Liu X Y, Miao Y L, Gao Y, Qiu R C, Zhang M, et al. Measurement and prediction of tomato canopy apparent photosynthetic rate. Int J Agric & Biol Eng, 2019; 12(5): 156–161.
[49] Li H, Yin J, Zhang M, Sigrimis N, Gao Y, Zheng W G. Automatic diagnosis of strawberry water stress status based on machine vision. Int J Agric & Biol Eng, 2019; 12(1): 159–164.
[50] Wen D M, Ren A X, Ji T, Flores-Parra I M, Yang X T, Li M. Segmentation of thermal infrared images of cucumber leaves using K-means clustering for estimating leaf wetness duration. Int J Agric & Biol Eng, 2020; 13(3): 161–167.
Downloads
Published
2021-06-11
How to Cite
Zhang, M., Zhang, W., Chen, X., Wang, F., Wang, H., Zhang, J., & Liu, L. (2021). Modeling and simulation of temperature control system in plant factory using energy balance. International Journal of Agricultural and Biological Engineering, 14(3), 66–75. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6114
Issue
Section
Animal, Plant and Facility Systems
License
IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
