Universality of an improved photosynthesis prediction model based on PSO-SVM at all growth stages of tomato
Keywords:
photosynthesis, greenhouse, tomato, CO2 enrichment, photosynthesis prediction model, wireless sensor network, environmental monitoring systemAbstract
CO2 concentration is an environmental factor affecting photosynthesis and consequently the yield and quality of tomatoes. In this study, a photosynthesis prediction model for the entire growth stage of tomatoes was constructed to elevate CO2 level on the basis of crop requirements and to evaluate the effect of CO2 elevation on leaf photosynthesis. The effect of CO2 enrichment on tomato photosynthesis was investigated using two CO2 enrichment treatments at the entire growth stage. A wireless sensor network-based environmental monitoring system was used for the real-time monitoring of environmental factors, and the LI-6400XT portable photosynthesis system was used to measure the net photosynthetic rate of tomato leaf. As input variables for the model, environmental factors were uniformly preprocessed using independent component analysis. Moreover, the photosynthesis prediction model for the entire growth stage was established on the basis of the support vector machine (SVM) model. Improved particle swarm optimization (PSO) was also used to search for the best parameters c and g of SVM. Furthermore, the relationship between CO2 concentration and photosynthetic rate under varying light intensities was predicted using the established model, which can determine CO2 saturation points at the various growth stages. The determination coefficients between the simulated and observed data sets for the three growth stages were 0.96, 0.96, and 0.94 with the improved PSO-SVM and 0.89, 0.87, and 0.86 with the original PSO-SVM. The results indicate that the improved PSO-SVM exhibits a high prediction accuracy. The study provides a basis for the precise regulation of CO2 enrichment in greenhouses. Keywords: photosynthesis, greenhouse, tomato, CO2 enrichment, photosynthesis prediction model, wireless sensor network, environmental monitoring system DOI: 10.3965/j.ijabe.20171002.2580 Citation: Li T, Ji Y H, Zhang M, Sha S, Li M Z. Universality of an improved photosynthesis prediction model based on PSO-SVM at all growth stages of tomato. Int J Agric & Biol Eng, 2017; 10(2): 63–73.References
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[2] Guo W, Cheng H, Li R, Li J, Zhang H. Greenhouse monitoring system based on wireless sensor networks. Transactions of the CSAM, 2010; 41(7): 181−185. (in Chinese)
[3] Li Y H, Ji G F, Han J Y. Application of the wireless sensor network in environment monitoring system of greenhouse. Instrument and Meter for Automation, 2010; 31(10): 61−64. (in Chinese)
[4] Mancuso M, Bustaffa F. A wireless sensors network for monitoring environmental variables in a tomato greenhouse. IEEE International Workshop on Factory Communication Systems, 2006; pp.107−110.
[5] Srbinovska M, Gavrovski C, Dimcev V, Krkoleva A, Borozan V. Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 2015; 88: 297−307.
[6] Hwang J, Shin C, Yoe H. A wireless sensor network-based ubiquitous paprika growth management system. Sensors, 2010; 10(12): 11566−11589.
[7] Prior S A, Brett Runion G, Rogers H H, Allen Torbert H, Wayne Reeves D. Elevated atmospheric CO2 effects on biomass production and soil carbon in conventional and conservation cropping systems. Global Change Biology, 2005; 11(4): 657−665.
[8] Poorter H, Navas M L. Plant growth and competition at elevated CO2: on winners, losers and functional groups. New Phytologist, 2003; 157(2): 175−198.
[9] Kimball B A, Kobayashi K, Bindi M. Responses of agricultural crops to free-air CO2 enrichment. Advances in Agronomy, 2002; 77: 293−368.
[10] Dehshiri A, Modarres-Sanavy S A M, Mahdavi B. Amelioration of salinity on photosynthesis and some characteristics of three rapeseed (Brassica napus) cultivars under increased concentrations of carbon dioxide. Archives of Agronomy and Soil Science, 2015; 61(10): 1423−1438.
[11] Wang W Z, Zhang M, Liu C H, Li M Z, Liu G. Real-time monitoring of environmental information and modeling of the photosynthetic rate of tomato plants under greenhouse conditions. Applied Engineering in Agriculture, 2013; 29(5): 783−792.
[12] Zhang J, Wang S. Simulation of the canopy photosynthesis model of greenhouse tomato. Procedia Engineering, 2011; 16: 632−639. (in Chinese)
[13] Hu J, He D, Ren J, Liu X, Liang Y, Dai J, et al. Optimal regulation model of tomato seedlings’ photosynthesis based on genetic algorithm. Transactions of the CSAE, 2014; 30(17): 220−227. (in Chinese)
[14] Wang D, Wang M, Qiao X. Support vector machines regression and modeling of greenhouse environment. Computers and Electronics in Agriculture, 2009; 66(1): 46−52.
[15] Zai S, Wen J, Guo D, Han Q, Deng Z, Sun H, et al. Determination of leaf area of sweet pepper based on support vector machine model and image processing. Transactions of the CSAE, 2011; 27(3): 237−241. (in Chinese)
[16] Zhang Y, Zheng L, Li M, Deng X, Ji R. Predicting apple sugar content based on spectral characteristics of apple tree leaf in different phenological phases. Computers and Electronics in Agriculture, 2015; 112: 20−27.
[17] Liu B, Hou D, Huang P, Liu B, Tang H, Zhang W et al. An improved PSO-SVM model for online recognition defects in eddy current testing. Nondestructive Testing & Evaluation, 2013, 28(4): 367−385.
[18] Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 1995: 1942-1948.
[19] Xu X, Guan X P, Hua C C. Modeling of blast furnace temperature based on improved particle swarm optimizer and support vector machine. Hebei Province, Yanshan University, 2015. (in Chinese)
[20] Xu S, Zhu X, Li C, Ye Q. Effects of CO2 enrichment on photosynthesis and growth in Gerbera jamesonii. Scientia Horticulturae, 2014; 177: 77−84.
[21] Manual LI-6400XT OPEN6.1. Lincoln, NE, USA: LI-COR Inc.
[22] Liu N J, Shi B L, Zhao L, Qing Z S, Ji B P, Zhou F. Analysis of feature signals of electronic nose in honey nectar detection based on independent components analysis combined with genetic algorithm. Transactions of the CSAE, 2015; 31(Supp.1): 315−324. (in Chinese)
[23] Sun T, Xu W, Hu T, Liu M H. Determination of soluble solids content in Nanfeng Mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM. Spectroscopy and Spectral Analysis, 2013; 33(12): 3235−3239. (in Chinese)
[24] Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Networks, 2000; 13(4): 411−430.
[25] Lin S, Liu Z. Parameter selection in SVM with RBF kernel function. Journal-Zhejiang University of Technology, 2007; 35(2): 163−167. (in Chinese)
[26] Hu W, Yan X, Yuan L, Yang Q, Wu Z. The role of light intensity in the recovery of photosynthesis in the tomato leaves after chilling under low light. Bulletin of Botanical Research, 2011; 31(2): 164−168. (in Chinese)
[27] Thongbai P, Kozai T, Ohyama K. CO2 and air circulation effects on photosynthesis and transpiration of tomato seedlings. Scientia Horticulturae, 2010; 126(3): 338−344.
[28] Sánchez-Guerrero M C, Lorenzo P, Medrano E, Castilla N, Soriano T, Baille A. Effect of variable CO2 enrichment on greenhouse production in mild winter climates. Agricultural and Forest Meteorology, 2005; 132(3): 244−252.
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Published
2017-03-31
How to Cite
Ting, L., Yuhan, J., Man, Z., Sha, S., & Minzan, L. (2017). Universality of an improved photosynthesis prediction model based on PSO-SVM at all growth stages of tomato. International Journal of Agricultural and Biological Engineering, 10(2), 63–73. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2580
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Information Technology, Sensors and Control Systems
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