Modeling Energy Consumption of Strawberries on the Basis of Energy Consumption Pattern Using Artificial Neural Network and Anfis and Regression in Dezfoul County

Document Type : Research Article

Authors

1 MSc Student, Department of Biosystem Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

2 Assistant Professor, Department of Biosystem Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

Abstract

This research was carried out to analyze and model energy consumption in the production of software in open fields using intelligent artificial neural network, multi-layered non-fuzzy inference scheme and regression. In order to estimate the amount of energy consumed, data were collected directly from 50 strawberry producers in Dezful. According to the results, the total input and output energy for this product was equal to 36257.25 and 30006.51 megajol per hectare. The highest amount of inputs was allocated to the amount of 18139.84 megajol per hectare and 50 percent to chemical fertilizers. According to the results of ANFIS model, the correlation coefficient and mean square error and mean absolute error for strawberries were 0.98, 0.047 and 0.012 respectively. Also, the values of these parameters for artificial neural network with optimal structure (7-6-1) were 0.97, 0.056 and 0.020 respectively and for regression were 0.90, 0.076 and 0.053 respectively. Also, the effect of energy consumption by different inputs on strawberry production was studied using the Cobb-Douglas parametric method and final physical production. The results showed that the impacts of machine and water inputs were higher than the other inputs. The results of the comparison of the regression model with the ANN and ANN model indicated that the anfis model estimates the output value more accurately than the best artificial neural network model and artificial neural network compared to the regression model.

Keywords

References

باقرپور، ح. 1393. شبیه‌سازی فرآیند تولید سوخت بیودیزل حاصل از روغن‌های پسماند با استفاده از شبکه عصبی. مجله مدل‌سازی در مهندسی، 39: 148-143.
بخشوده، م. 1393. اقتصاد تولید (کاربرد آن در کشاورزی). چاپ چهارم. انتشارات دانشگاه شهید باهنر کرمان. 414 صفحه.
رحیمیان، ب. 1394. تعیین شاخص‌های اقتصادی، انرژی و زیست‌محیطی در کشت چند محصول (چغندرقند، گندم و نخود) در استان آذربایجان غربی به کمک تکنیک‌های هوش محاسباتی. پایان‌نامه کارشناسی‌ارشد. دانشگاه تهران، 129 صفحه.
رحیمی‌زاده، م.، مدنی، ح.، رضادوست، س.، مهربان، ا. و مرجانی، ع. 1386. تجزیه و تحلیل انرژی در بوم نظام‌های کشاورزی و راهکارهای افزایش کارآیی انرژی. ششمین همایش ملی انرژی، تهران. 12 صفحه.
عمید، س. و مصری گندشمین، ت. 1395. مدل‌سازی راندمان انرژی در تولید مرغ گوشتی به کمک رویکرد شبکه‌های عصبی مصنوعی پرسپترون. نشریه تحقیقات تولیدات دامی، 5 (2): 85-73.
موسوی‌اول، س. ه. 1390. مقایسه الگوی مصرف انرژی و تحلیل شاخص‌های مکانیزاسیون در تولید سویا، کلزا و آفتابگردان در شهرستان‌های گرگان، علی‌آباد و کلاله در استان گلستان. پایان‌نامه کارشناسی‌ارشد. دانشگاه تهران، 111 صفحه.
نبوی پله سرایی، ا. 1393. مدل‌سازی و بهینه‌سازی مصرف انرژی و میزان انتشار آلایندگی با استفاده از سیستم‌های خبره در الگوی کشت غالب شهرستان‌های آستانه اشرفیه و لنگرود در استان گیلان. پایان‌نامه کارشناسی‌ارشد. دانشگاه تبریز.110 صفحه.
Al-Ghandoor, A., Jaber, J. O., Al-Hinti, I., Mansour, I. M. 2009. Residential past and future energy consumption: Potential savings and environmental impact. Renewable and Sustainable Energy Reviews, 13 (6-7): 1262-74.
Aydin, G. 2014. Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections. Renewable and Sustainable Energy Reviews, 35: 382-389.
Ceylan, H., Guclu, A., Tutumluer, E. and Thompson, M. R. 2004. Use of artificial neural networks for back calculation of pavement layer moduli. 2004 FWD Users, October 2-5, university Inn, West Lafayette, Indiana.
Cochran, W. G. 1977. Sampling Techniques, third ed. John Wiley & Sons, New York, 1-448.
Dalgaard, T., Halberg, N. and Porter, J. R. 2001. A model for fossil energy use in Danish agriculture used to compare organic and conventional farming. Agriculture, Ecosystem and Environment, 1: 51-65.
Drummond, S. T., Sudduth, K. A., Joshi, A., Birrell, S. J. and Kitchen, N. R. 2003. Statistical and neural methods for site–specific yield prediction. Transactions of the ASAE, 46 (1): 5-14.
Erdal, G., Esengun, K., Erdal, H. and Gunduz, O. 2007. Energy use and economical analysis of sugar beet production in Tokat province of Turkey. Energy, 32: 35-41.
Kaul, M., Hill, R.L., Walthall, C. 2005. Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85: 1-18.
Khoshnevisan, B., Rafiee, S., Omid, M., Yousefi, M. and Movahedi, M. 2013. Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy, 52 (3): 333-338.
Khoshnevisan, B., Rafiee, S., Omid, M. and Mousazadeh, H. 2014. Prediction of potato yield based on energy inputs using multi-layer adaptive neuro-fuzzy inference system. Measurement, 47: 521-530.
Kitani, O. 1999. Energy and Biomass Engineering in: St. Joseph, M. A. (Ed.). CIGR Handbook of Agricultural Engineering, 330 p.
Mesri-Gundoshmian, T., Ghassemzadeh, H. R., Abdollahpour, S. and Navid, H. 2010. Application of artificial neural network in prediction of the combine harvester performance. Food, Agriculture and Environment, 8 (2): 721-724.
Mohammadi, A. and Omid, M. 2010. Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran. Applied Energy, 87: 191-196.
Naderloo, L., Alimardani, R., Omid, M., Sarmadian, F., Javadikia, P., Torabi, M. Y. and Alimardani, F. 2012. Application of ANFIS to predict crop yield based on different energy inputs. Measurement, 45 (6): 1406-1413.
Omid, M., Ghojabeige, F., Delshad, M. and Ahmadi, H. 2011. Energy use pattern and benchmarking of selected greenhouses in Iran using data envelopment analysis. Energy Conversion and Management, 52: 153-62.
Pahlavan, R., Omid, M. and Akram, A. 2012. Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy, 37 (1): 171-17.
Pishgar-Komleh, SH., Ghahderijani, M. and Sefeedpari, P. 2012. Energy consumption and CO2 emissions analysis of potato production based on different farm size levels in Iran. Journal of Cleaner Production, 33: 183-91.
Rahman, M. M. and Bala, B. K. 2010. Modelling of jute production using artificial neural networks. Biosystems Engineering, 105 (3): 350-356.
Rafiee, S., Mousavi Avval, S. H. and Mohammadi, A. 2010. Modeling and sensitivity analysisof energy inputs for apple production in Iran. Energy, 35: 3301-3306.
Singh, S. and Mittal, J. P. 1992. Energy in production agriculture. New Delhi: Mittal Publications. 1-380.
Yang, C., Prasher, S., Landry, J. and Ramaswamy, H. 2003. Development of an herbicide application map using artificial neural networks and fuzzy logic. Agricultural Systems, 76: 561-574.
Volume 11, Issue 1
September 2019
Pages 207-219

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