Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the production yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 greenhouses in the region during four periods of plant cultivation in 2009–2010. The total input energy and energy ratio for basil production were 14,308,998 MJ ha−1 and 0.02, respectively. The developed ANN was a multilayer perceptron (MLP) with seven neurons in the input layer, one, two and three hidden layer(s) of various numbers of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chemical fertilizers, farm yard manure, chemicals, electricity and transportation. Results showed, the ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coefficient of determination (R2) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, respectively. Sensitivity analysis revealed that chemical fertilizers are the most significant parameter in the basil production.

[1]  M. Omid,et al.  Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran , 2010 .

[2]  M. Omid,et al.  A comparative study between parametric and artificial neural networks approaches for economical assessment of potato production in Iran , 2011 .

[3]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[4]  Ibrahim Dincer,et al.  Artificial neural network analysis of world green energy use , 2007 .

[5]  Mahmoud Omid,et al.  Energy use patterns and econometric models of major greenhouse vegetable productions in Iran , 2011 .

[6]  Surendra Singh,et al.  Study on energy use efficiency for paddy crop using data envelopment analysis (DEA) technique , 2009 .

[7]  A. Contreras,et al.  Energy ratio analysis of genetically-optimized potato for ethanol production in the Chilean market , 2010 .

[8]  B. Ozkan,et al.  Energy and cost analysis for greenhouse and open-field grape production , 2007 .

[9]  M. Omid,et al.  Modeling Drying Kinetics of Pistachio Nuts with Multilayer Feed-Forward Neural Network , 2009 .

[10]  Ozgur Kisi,et al.  Methods to improve the neural network performance in suspended sediment estimation , 2006 .

[11]  P. Geoffrey Allen,et al.  Economic forecasting in agriculture , 1994 .

[12]  Mahmoud Omid,et al.  Energy use pattern and benchmarking of selected greenhouses in Iran using data envelopment analysis , 2011 .

[13]  Mehmet Musa Özcan,et al.  Comparative essential oil composition of flowers, leavesand stems of basil (Ocimum basilicum L.) used as herb. , 2008, Food chemistry.

[14]  A. Kürklü,et al.  An input-output energy analysis in greenhouse vegetable production: a case study for Antalya region of Turkey , 2004 .

[15]  Mahmoud Omid,et al.  A comparative study on energy use and cost analysis of potato production under different farming technologies in Hamadan province of Iran. , 2010 .

[16]  Ali Azadeh,et al.  Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors , 2008 .

[17]  M. Canakci,et al.  Energy use pattern analyses of greenhouse vegetable production , 2006 .

[18]  R. Lacroix,et al.  Methods of predicting milk yield in dairy cows-Predictive capabilities of Wood's lactation curve and artificial neural networks (ANNs) , 2006 .

[19]  B. Z. Adewole,et al.  Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner , 2013 .

[20]  Surender Singh,et al.  A Study on Technical Efficiency of Wheat Cultivation in Haryana , 2007 .

[21]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[22]  Kemal Esengün,et al.  Energy use and economical analysis of sugar beet production in Tokat province of Turkey , 2007 .

[23]  M. R. Alam,et al.  Energy Flow in Agriculture: Bangladesh , 2005 .

[24]  Henrik Egelyng,et al.  Energy efficiency of organic pear production in greenhouses in China , 2010, Renewable Agriculture and Food Systems.

[25]  P. K. Ghosh,et al.  Bioenergy and economic analysis of soybean-based crop production systems in central India , 2002 .

[26]  K. Esengün,et al.  Input–output energy analysis in dry apricot production of Turkey , 2007 .

[27]  Fausto Freire,et al.  Energy and Environmental Benefits of Rapeseed Oil Replacing Diesel , 2009 .

[28]  Maria J. Diamantopoulou,et al.  Artificial neural networks as an alternative tool in pine bark volume estimation , 2005 .

[29]  Erdemir Gundogmus,et al.  Cost efficiency on organic farming: a comparison between organic and conventional raisin-producing households in Turkey. , 2008 .

[30]  M. M. Rahman,et al.  MODELLING OF JUTE PRODUCTION USING ARTIFICIAL NEURAL NETWORKS , 2010 .

[31]  V. P. Chaudhary,et al.  Energy auditing of diversified rice–wheat cropping systems in Indo-gangetic plains , 2009 .

[32]  M. Boroushaki,et al.  Energy Consumption Forecasting of Iran Using Recurrent Neural Networks , 2011 .

[33]  Hans-Erik Uhlin,et al.  Why energy productivity is increasing: An I-O analysis of Swedish agriculture , 1998 .

[34]  Hanifreza Motamed Shariati,et al.  Comparison of energy consumption and GHG emissions of open field and greenhouse strawberry production , 2014 .

[35]  E. Kwee,et al.  Potassium rate alters the antioxidant capacity and phenolic concentration of basil (Ocimum basilicum L.) leaves. , 2010 .

[36]  M. Omid,et al.  Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran , 2014 .