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Abstract

Wheat is considered as one of the most important products in Iran. Concerning high cultivation area of wheat in Khuzestan, an instrument is required to process stored data in order to give information resulted from such processing to managers of agricultural sectors. Data mining technique is able to give essential information and models to producers of wheat for modelling energy consumption. One of the most practical algorithms is an artificial neural network. The main aim of this research is to predict output energy of wheat farms using a multilayer perceptron neural network. This is an analytic research and its database consists of 1240 records. Data required for the research was obtained from wheat farm during 2014-2018. Data analysis was done via IBM SPSS modeller 14.2 and standard CRISP. Concerning the model used in the research, it was found that variables of chemical fertilizers, machinery & diesel fuel with coefficients of 0.2987, 0.2064 and 0.1527 respectively had the highest effect on output variable (productive energy). Amount of prediction precision in neural network algorithm, meaning ratio of correctly predicted records to total records was 93.08%. Also, linear correlation between actual values and predicted values were 0.92 and 0.88 respectively, for training data and testing data suggesting strong correlation.  The results obtained can be effective for wheat farmers in direction of evaluation and optimization of energy consumption in process of wheat production and reduction of consumption of energy inputs.

Keywords

Energy Predict Data mining ANN Wheat

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How to Cite
Monjezi, N. . (2021). Energy Prediction of Wheat Production Using Data Mining Technique in Iran. Basrah Journal of Agricultural Sciences, 34(1), 14–27. https://doi.org/10.37077/25200860.2021.34.1.02
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References

  1. Almaliki, S., Alimardani, R., & Omid, M. (2016). Artificial neural network based modeling of tractor performance at different field conditions. Agricultural Engineering International: CIGR Journal, 18, 262-274. https://cigrjournal.org/index.php/Ejounral/article/view/3880
  2. Almaliki, S. (2017). Development and evaluation of models for MF-285 tractor performance parameters using computational intelligence Techniques. Ph. D. Thesis. University of Tehran, 215pp.
  3. Ayman, E.K.; Kadry, M., & Walid, G. (2015). Proposed framework for implementing data mining techniques to enhance decisions in agriculture sector. Procedia Computer Science, 65, 633-642. https://doi.org/10.1016/j.procs.2015.09.007
  4. Banaeian, N., & Zangeneh, M. (2011). Modeling energy flow and economic analysis for walnut production in Iran. Research Journal of Applied Sciences, Engineering and Technology, 3, 194-201.https://www.researchgate.net/publication/268341453
  5. Beheshti Tabar, I., Keyhani, A., & Rafiee, S. (2010). Energy balance in Iran’s agronomy (1990–2006). Renewable and Sustainable Energy Reviews, 14, 849-55. https://doi.org/10.1016/j.rser.2009.10.024
  6. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2011). CRISP-DM 1.0: Step-by-step data mining guide. Viewed 22 October 2011.http://www.crisp-dm.org/CRISPwP-0800.pdf
  7. Efendigil, T., Onut, S., & Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems With Applications, 36, 6697-6707. https://doi.org/10.1016/j.eswa.2008.08.058
  8. Ekasingh, B., Ngamsomsuke, K., Letcher, R., & Spate, J. (2005). A data mining approach to simulating farmers’ crop choices for integrated water resources management. Journal of Environmental Management, 77, 315-325. https://doi.org/10.1016/j.jenvman.2005.06.015
  9. Everinghama, Y. L., Smyth, C. W., & Inman-Bamber, N. G. (2009). Ensemble data mining approaches to forecast regional sugarcane crop production. Agricultural and Forest Meteorology, 149: 689-696. https://doi.org/10.1016/j.agrformet.2008.10.018
  10. Fernandes, J. L., Rocha, J. V., & Lamparelli, R. A. C. (2011). Sugarcane yield estimates using time series analysis of spot vegetation images. Scientia Agricola, 68, 139-146. https://doi.org/10.1590/S0103-90162011000200002
  11. Ferraro, D. O., Rivero, D. E., & Ghersa, C. M. (2009). An analysis of the factors that influence sugarcane yield in Northern Argentina using classification and regression trees. Field Crops Research, 112, 149-157. https://doi.org/10.1016/j.fcr.2009.02.014
  12. Geetha, M. C. S. (2015). A Survey on data mining techniques in Agriculture. International Journal of Innovative Research in Computer and Communication Engineering, 3, 887-892. https://link.springer.com/article/10.1007/s12351-009-0054-6
  13. Goktepe, A. B., Altun, S., & Sezar, A. (2005). Soil clustering by fuzzy C-Means algorithm. Advances in Engineering Software, 36, 691-698. https://doi.org/10.1016/j.advengsoft.2005.01.008
  14. Houshyar, E., Sheikh Davoodi, M. J., & Nassiri, S. M. (2010). Energy efficiency for wheat production using data envelopment analysis (DEA) technique. Journal of Agricultural Technology, 6, 663-672. https://www.researchgate.net/publication/235654571
  15. Jeysenthil, K. M. S., Manikandan, T., & Murali, E. (2014). Third generation agricultural support system development using data mining. International Journal of Innovative Research in Science, Engineering, 3, 9923-9930. http://www.ijirset.com/upload/2014/march/6_Third.pdf
  16. Kalpana, R., Shanthi, N., & Arumugam, S. (2014). A survey on data mining techniques in agriculture. International Journal of Advanced Computer Science and Technology, 3, 426-431. http://warse.org/pdfs/2014/ijacst05382014.pdf
  17. Kaltschmitt, M., Reinhardt, G. A., & Stelzer, T. (1997). Life cycle analysis of befouls under different environmental aspects. Biomass. Bioenergy, 12, 121-134. https://doi.org/10.1002/ese3.315
  18. Khan, M. A., Zafar, J., & Bakhash, A. (2008). Energy requirement and economic analysis of sugarcane production in Dera Islamic Khan district of Pakistan. Gomal University Journal of Research, 24, 72-82. https://www.researchgate.net/publication/262048156
  19. Khedr, A. E., Kadry, M., & Walid, G. (2015). Proposed framework for implementing data mining techniques to enhance decisions in agriculture sector applied case on food security information center ministry of agriculture, Egypt. Procedia Computer Science, 65, 633-642. https://doi.org/10.1016/j.procs.2015.09.007
  20. Khoshnevisan, B., Rafiee, S., Omid, M., Mousazadeh, H., & Sefeedpari, P. (2013). Prognostication of environmental indices in potato production using artificial neural networks. Journal of Cleaner Production, 5, 402-409. https://doi.org/10.1016/j.jclepro.2013.03.028
  21. Kitani, O. (1999). Energy and Biomass Engineering. In: CIGR Handbook of Agricultural Engineering Vol. 5, ASAE Publication, St. Joseph, M. I., 330pp. http://cigr.org/documents/CIGRHandbookVol5.pdf
  22. Liao, S., & Wen, C. (2007). Artificial neural networks classification and clustering of methodologies and applications- literature analysis from 1995 to 2005. Expert Systems With Applications, 32, 1-11. https://doi.org/ 10.1016/j.eswa.2005.11.014
  23. Mandal, K. G., Saha, K. P., Ghosh, P. K., Hati, K. M., & Bandyopadhyay, K. K. (2002). Bioenergy and economic analysis of soybean-based crop production systems in central India. Biomass. Bioenergy, 23, 337-345. https://doi.org/10.1016/S0961-9534(02)00058-2
  24. Medar, R. A., & Rajpurohit, V. S. (2014). A survey on data mining techniques for crop yield prediction. International Journal of Advance Research in Computer Science and Management Studies, 2, 59-64. https://www.academia.edu/9421293
  25. Mousavi-Avval, S. H., Rafiee, S., Jafari, A., & Mohammadi, A. (2011). Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach. Applied Energy, 88, 3765-3772. https://doi.org/10.1016/j.apenergy.2011.04.021
  26. Mucherino, A., Papajorgji, P., & Pardalos, P. M. (2009). A survey of data mining techniques applied to agriculture. Operational Research, 9, 121-140. https://doi.org/ 10.1007/s12351-009-0054-6
  27. Nassiri, S. M., & Singh, S. (2009). Non-parametric energy use efficiency, energy ratio and specific energy for irrigated wheat crop production. Iran Agricultural Research, 28, 27-38. http://iar.shirazu.ac.ir/article_151_3e06dddfdf6ccfdfeb5f9b2aaca5c75d.pdf
  28. Oliveira, M. P. G., Bocca, F. F., & Rodrigues, L. H. A. (2017). From spreadsheets to sugar content modeling: A data mining approach. Computers and Electronics in Agriculture, 132, 14-20. https://doi.org/10.1016/j.compag.2016.11.012
  29. Phillips-Wren, G., Sharkey, P. H., & MorssDy, S. (2008). Mining lung cancer patient data to assess healthcare resource utilization. Expert Systems With Applications, 35, 1611-1619. https://doi.org/10.1016/j.eswa.2007.08.076
  30. Ramesh, D., & Vardhan, B. V. (2013). Data mining techniques and applications to agricultural yield data. Journal of Advanced Research in Computer and Communication Engineering, 2, 3477-3480. https://www.ijarcce.com/upload/2013/september/31-h-dantam%20ramesh%20-data%20mining%20techniques%20and%20application%20to.pdf
  31. Raorane, A.A. & Kulkarni, R.V. (2013). Review- role of data mining in agriculture. International Journal of Computer Science and Information Technology, 4, 270 -272. http://cloud.politala.ac.id/politala/1.%20Jurusan/Teknik%20Informatika/19.%20e-journal/Jurnal%20Internasional%20TI/IJCSIT/Vol%204/ISSUE%202/ijcsit20130402018.pdf
  32. Raorane, A. A., & Kulkarni, R. V. (2015). Application of data mining tool to crop management system. Russian Journal of Agricultural and Socio-Economic Sciences, 37, 3-16. https://ideas.repec.org/a/scn/031261/16082009.html
  33. Shahin, S., Jafari, A., Mobli, H., Rafiee, S., & Karimi, M. (2008). Effect of farm size on energy ratio for wheat production: A case study from Ardabil Province of Iran. American-Eurasian Journal of Agricultural and Environmental Sciences, 3, 604-608. https://www.researchgate.net/publication/237334154
  34. Sharma, A. (2006). Spatial data mining for drought monitoring: An approach using temporal NDVI and rainfall relationship. M. Sc. Thesis. Geo-information Science Earth Observation, University of Twente, 75pp. https://www.iirs.gov.in/node/254
  35. Srivastava, A. K., Goering, C. E., & Rohrbach, R. P. (1993). Engineering Principles of Agricultural Machines. ASAE Publication, 604pp. https://www.amazon.com/Engineering-Principles-Agricultural-Machines-2nd/dp/1892769506
  36. Thomas, E. (2017). An artificial neural network for real-time hardwood lumber grading. Computers and Electronics in Agriculture, 132, 71-75. https://doi.org/10.1016/j.compag.2016.11.018