Management of CO2 in a tomato greenhouse using WSN and BPNN techniques
Abstract
Keywords: WSN, ZigBee, greenhouse information, photosynthetic rate, CO2 fertilization
DOI: 10.3965/j.ijabe.20150804.1572
Citation: Li T, Zhang M, Ji Y H, Sha S, Jiang Y Q, Li M Z. Management of CO2 in a tomato greenhouse using WSN and BPNN techniques. Int J Agric & Biol Eng, 2015; 8(4): 43-51.
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Park D H, Kang B J, Cho K R, Shin C S, Cho S E, Park J W, et al. A study on greenhouse automatic control system based on wireless sensor network. Wireless Pers Commun, 2011; 56: 117–130.
Ruiz-Garcia L, Lunadei L, Barreiro P, Robla J I. A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends. Sensors, 2009; 9: 4728–4750.
Serodio C, Cunha J B, Morais R, Couto C A, Monteiro J L. A networked platform for agricultural management systems. Computers and Electronics in Agriculture, 2001; 31: 75–90.
Mizunuma M, Katoh T, Hata S. Applying IT to farm fields—A wireless LAN. NTT Techinical Review, 2003; 1(2): 56–60.
Guo W C, Cheng H J, Li R M, Liu J, Zhang H H. Greenhouse Monitoring System Based on W ireless Sensor Networks. Transactions of the CSAM, 2010; 41(7): 181–185. (in Chinese with Chinese abstract)
Zou G H. Development of the monitoring system for greenhouse environment based on WSN. Doctoral thesis. Nanjing University of Aeronautics and Astronautics, China, 2012.
Zhou J J, Wang X, Zou W, Zhang R R, Ma W. Remote greenhouse monitor and control system based on Zigbee. China’s Rural Water and Hydropower, 2013; (1): 67–69. (in Chinese with Chinese abstract)
Lee D H, Lee K S, Choi C H, Kim H J, Chung S O, Cho Y J. Prediction of CO2 emission from soil for optimal greenhouse control. In: 2011 ASABE Annual International Meeting, Paper Number: 1111628.
Gary C, Jones J W, Tchamitchian M. Crop modelling in horticulture: State of the art. Scientia Horticulturae, 1998; 74: 3–20.
Jones J W, Dayan E L, Allen H, Keulen V, Challa H. A dynamic tomato growth and yield model (TOMGRO). Transactions of the ASAE, 1991; 34(2): 663–672.
Moreno R S, Aguilar A R, Cruz I L, Reyes M G. Artificial neural network as a prediction tool in agricultural variables. In: 2006 ASABE Annual International Meeting, Paper Number: 063071.
Ehret D L, Hill B D, Raworth D A. Artificial neural network modeling to predict cuticle cracking in greenhouse peppers and tomatoes. Computers and Electronics in Agriculture, 2008; 61(2): 108–116.
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 Botanica Boreali-Occidentalia Sinica, 2004; 24(9): 1624–1629.
Xiang M J, Zhang M. Control of CO2 based on two kinds of neural network in greenhouse. Agricultural Technology & Equipment, 2010; 184(2): 11–12.
Wang Y, Xie J M, Yu J H, Lü J, Wang X Y. Variations of CO2 concentration in solar greenhouse on sand land with tomato substrates cultivation. Journal of Gansu Agricultural University, 2012; 47(6): 57–62. (in Chinese with Chinese abstract)
Xu D Q, Chen G Y, Yu G L, Chen Y. Exploring the observation methods of photosynthetic responses to light and carbon dioxide. Journal of Plant Physiology and Molecular Biology, 2006; 32(6): 691–696.
Manual LI-6400XT OPEN6.1. Lincoln, NE, USA: LI-COR Inc.
Chen G Y, Yu G L, Chen Y. Exploring the observation methods of photosynthetic responses to light and carbon dioxide. Journal of Plant Physiology and Molecular Biology, 2006; 32(6): 691–696.
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 Chinese abstract)
Xiang M J. Optimization of regulation greenhouse environmental factors based on information fusion Technology. Doctoral thesis. Jiangsu University, China, 2009. (in Chinese with Chinese abstract)
Han M, Ding J. Improvement of BP algorithm based on cross-validation method and its implementation. Computer Engineering and Design, 2008; 29(14): 3738–3739. (in Chinese with Chinese abstract)
Ehret D L, Hill B D, Helmer T, Edwards D R. Neural network modeling of greenhouse tomato yield, growth and water use from automated crop monitoring data. Computers and Electronics in Agriculture, 2011; 79(1): 82–89.
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