An improved method of tomato photosynthetic rate prediction based on WSN in greenhouse
Keywords:
tomato, photosynthetic rate, wireless sensor network, greenhouse, rough set, BP neural networkAbstract
In order to improve the efficiency of CO2 fertilizer and promote high quality and yield, it is necessary to precisely control CO2 fertilizer by wireless sensor network based on a model of photosynthetic rate prediction in greenhouse. An experiment was carried out on tomato plants in greenhouse for photosynthetic rate prediction modeling combined rough set and BP neural network. In data acquiring phase, plants growth information and greenhouse environmental information that may have influences on photosynthetic rate, including plant height, stem diameter, the number of leaves and chlorophyll content of functional leaves, air temperature, air humidity, light intensity, CO2 concentration and soil moisture, which were measured. And LI-6400XT photosynthetic rate instrument was used for obtaining net photosynthetic rate of functional leaf. After preliminary processing, 135 sets of data were obtained. And twelve of them were used for model test of neural network, while the others were used for modeling. All of the data were normalized before modeling. Two models were built to predict photosynthetic rate based on BP neural network. One had total nine input parameters. The other had six input parameters, chlorophyll content, air temperature, air humidity, light intensity, CO2 concentration, and soil moisture, which were reducted from original nine based on attributes reduction theory of rough set. Both two models have one output parameter, the net photosynthetic rate of single leaf. The genetic algorithm was adopted to reduct attributes. Since continuous data cannot be processed by rough set, the K-mean cluster method was used to discretize the data of nine input parameters before attributes reduction. The prediction results of two models showed that the model with six input parameters had a mean absolute error of 0.6958, an average relative error of 7.28%, a root-mean-square error of 0.7428, and a correlation coefficient of 0.9964, while the other model respectively had 0.4026, 4.53%, 0.3245 and 0.9965, which proved that the model with minimum attributes had higher prediction accuracy. On the other hand, the number of iterations was used to represent the neural network train speed. The result showed that the model with six input parameters had an iteration of 544, while the other had 1038. Hence, the reduction model was applied to controlling CO2 concentration. The net photosynthetic rates at different CO2 concentrations were predicted at a certain condition. The results had the same curve trend with theory analysis, and a high prediction accuracy, which proved that the model was useful for CO2 concentration control. Keywords: tomato, photosynthetic rate, wireless sensor network, greenhouse, rough set, BP neural network DOI: 10.3965/j.ijabe.20160901.1243 Citation: Ji Y H, Jiang Y Q, Li T, Zhang M, Sha Sh, Li M Z. An improved method for prediction of tomato photosynthetic rate based on WSN in greenhouse. Int J Agric & Biol Eng, 2016; 9(1): 146-152.References
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[16] 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.
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[18] Yao H X, Huang Z L, Liu Z G. Comparison of two methods used to data preprocessing for one kind of neural network. Microcomputer Development, 2003; 13(6): 77−79. (in Chinese with English abstract)
[19] Han L, Li R, Zhu H L. Comprehensive evaluation model of soil nutrient based on BP neural network. Transactions of the CSAM, 2011; 42(7): 109−115. (in Chinese with English abstract)
[20] Ehret D L, Hill B D, Raworth D A, Estergaard B. Artificial neural network modeling to predict cuticle cracking in greenhouse peppers and tomatoes. Computers and Electronics in Agriculture, 2008; 61(2): 108−116.
[21] Xiang M J. Optimization and regulation of greenhouse environmental factors based on information fusion technology. Doctoral thesis. Jiangsu University, China, 2009. (in Chinese)
[2] Guo W C, Chen H J, Li R M, Liu J, Zhang H H. Greenhouse monitoring system based on wireless sensor networks. Transactions of the CSAM, 2010; 41(7): 181−185. (in Chinese with English abstract)
[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 with English abstract)
[4] Gonda L, Cugnasca C E. A proposal of greenhouse control using wireless sensor networks. ASABE Publication Number: 701P0606, Florida, USA, 2006.
[5] Nederhoff E M. Effects of CO2 concentration on photosynthesis, transpiration and production of greenhouse fruit vegetable crops. Doctoral thesis. Agricultural University, Wageningen, Netherlands, 1994.
[6] Vance P, Spalding M H. Growth, photosynthesis, and gene expression in chlamydomonas over a range of CO2 concentrations and CO2/O2 ratios: CO2 regulates multiple acclimation states1. Canadian Journal of Botany, 2005; 83(7): 796−809.
[7] Chen S C, Zou Z R, He C X, Zhang Z B, Yang X. Rules of CO2 concentration change under organic soil cultivation and effects of CO2 application on tomato plants in solar greenhouse. Acta Bot. Boreal.-Occident. Sin, 2004; 24(9): 1624−1629.
[8] Hou J L. Study on model to greenhouse tomato growth and development. Doctoral thesis. Beijing: China Agricultural University, 2005. (in Chinese with English abstract)
[9] Li T L, Yan A D, Luo X L, Qiu J Q, Li D, Yao Z K. Temperature modified model for single-leaf net photosynthetic rate of greenhouse tomato. Transactions of the CSAE, 2010; 26(9): 274−279. (in Chinese with English abstract)
[10] Luo X L, Li T L, Li G C, Liu Z Y, Diao J, Gu J G. Relations between leaf net photosynthetic rate of greenhouse tomatoes and meteorological factors. Jiangsu Agricultural Sciences, 2007; 4: 89−92. (in Chinese with English abstract)
[11] El-Sharkawy M A. Overview: Early history of crop growth and photosynthesis modeling. BioSystems, 2011; 103(2): 205−211.
[12] Gary C, Jones J W, Tchamitchian M. Crop modeling in horticulture: state of the art. Scientia Horticulture, 1998; 74: 3−20.
[13] Jones J W, Dayan E, Allen L H, Keulen H. A dynamic tomato growth and yield model (TOMGRO). Transaction of ASAE, 1991; 34(2): 663−672.
[14] Zhang J, Wang S X. Simulation of the canopy
photosynthesis model of greenhouse tomato. Procedia Engineering, 2011; 16: 632−639.
[15] Wang W Z, Zhang M, Jiang Y Q, Sha S, Li M Z. Photosynthetic rate prediction of tomato plants based on wireless sensor network in greenhouse. Transactions of the CSAM, 2013; 40(Supp 2): 192−197. (in Chinese with English abstract)
[16] 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.
[17] Li M, Zhang H G. Research on the method of neural network modeling based on rough sets theory. Acta Automatica Sinica, 2002; 28(1): 1−7. (in Chinese with English abstract)
[18] Yao H X, Huang Z L, Liu Z G. Comparison of two methods used to data preprocessing for one kind of neural network. Microcomputer Development, 2003; 13(6): 77−79. (in Chinese with English abstract)
[19] Han L, Li R, Zhu H L. Comprehensive evaluation model of soil nutrient based on BP neural network. Transactions of the CSAM, 2011; 42(7): 109−115. (in Chinese with English abstract)
[20] Ehret D L, Hill B D, Raworth D A, Estergaard B. Artificial neural network modeling to predict cuticle cracking in greenhouse peppers and tomatoes. Computers and Electronics in Agriculture, 2008; 61(2): 108−116.
[21] Xiang M J. Optimization and regulation of greenhouse environmental factors based on information fusion technology. Doctoral thesis. Jiangsu University, China, 2009. (in Chinese)
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Published
2016-01-31
How to Cite
Yuhan, J., Yiqiong, J., Ting, L., Man, Z., Sha, S., & Minzan, L. (2016). An improved method of tomato photosynthetic rate prediction based on WSN in greenhouse. International Journal of Agricultural and Biological Engineering, 9(1), 146–152. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1243
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Biosystems, Biological and Ecological Engineering
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