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Prediction of Citrus Fruits Production using Artificial Neural Networks and Linear Regression Analysis

Year 2020, Volume: 13 Issue: 3, 972 - 983, 31.12.2020
https://doi.org/10.18185/erzifbed.679531

Abstract

Accurate and timely prediction of fruits production plays a significant role in the agriculture industry. Therefore, it is very important to predict of citrus fruits production. In this study, prediction the production amount of different citrus fruits for a city of Turkey (Adana) is aimed. Orange, mandarin and bigarade are included as citrus products and the production amounts of ten years are used as dataset. Artificial neural network (ANN) and linear regression analysis are performed for predicting the production amounts. A feed forward neural network is proposed with regarding some inputs such as districts of Adana, product types, product specific plant area, average yield per tree, number of fruitless trees, number of fruit trees, total number of trees, population, inflation rate, total fruit area, temperature, average rainfall. The obtained results in which the R2 values are greater than 0.98 for all datasets show us that the proposed method can predict the production amount accurately regarding the input parameters.

References

  • Alp, M. and Cığızoğlu, K. (2004). “Farklı yapay sinir aği metotları ile yağış-akiş ilişkisinin modellenmesi”. İTÜ Dergisi/ D Mühendislik 3(1), 80-88.
  • Baş, N. (2006). “Yapay sinir ağları yaklaşımı ve bir uygulama.” Mimar sinan güzel sanatlar üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul.
  • Çolak, C., Çolak, C.M., and Atıcı, M.A. (2005). “Ateroskloroz’un tahmini için bir yapay sinir ağı”, Ankara Üniversitesi Tıp Fakültesi Mecmuası , 58, 159-162.
  • Elizondo, D.A., McClendon, R.W., Hoongenboom, G. (1994). “Neural network models for predicting flowering and physiological maturity of soybean”. Transactions of the ASABE 37, 981–988
  • Fiona, M. R., Thomas, S., Maria, I. J., and Hannah, B. (2019). “Identification Of Ripe And Unripe Citrus Fruits Using Artificial Neural Network”. In Journal of Physics: Conference Series, 1362(1), 012033.
  • Grossberg, S. (1988). “Nonlinear neural networks: principles. Mechanisms, and architectures”, Neural Networks 1(1), 17-61.
  • Ho, S.L., Xie, M., and Goh, T.N. (2002). “A comparative study of neural network and box-jenkins arima modeling in time series prediction”, Comput. Ind. Eng. 42, 371-375.
  • Kisi, Ö. (2004). “Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt,” Hydrological Sciences Journal, 49(6),1025-1040.
  • Kohzadi, N., Boyd, M.S., Kermanshahi, B., and Kaastra, I. (1996). “A comparison of artificial neural network and time series model for forecasting commodity price”, Neurocomputing 10, 169-181.
  • Luxhoj, J.T, Riis, J.O, and Stensballe, B. (1996). “Neural network models for forecasting univariate time series”, Neural Networks World 6, 747-772.
  • Movagharnejad, K. and Nikzad, M. (2007). “Modeling of tomato drying using artificial neural network”, Journal Computers and Electronics in Agriculture, 59(1), 2.
  • Parmar, R.S. (1997). “All estimation of aflatoxin contamination in preharvest peanuts using neural networks”, Transactions of the ASAE, 40(3), 809-813.
  • Priddy, K. L., and Keller, P. E. (2005). “Artificial neural networks: an introduction” SPIE press, 68.
  • Prasetyo, D., and Bimantaka, R. D. M. (2018). “Identification of Red Dragon Fruit Using Backpropagation Method Based on Android”. International Journal of Applied Business and Information Systems, 2(2), 40-45.
  • Prybutok, V.R, Yi, J, and Mitchell, D. (2000). “Comparison of neural network models with arima and regression models for prediction of Houston’s daily maximum ozone concentrations”, Eur. J. Oper. Res. 122, 31-40.
  • Şahin, C., Oğulata, S.N., Kırım, S., and Koçak, M. (2004). “Troid bezi bozukluklarının yapay sinir ağları ile teşhisi,” YA/EM’2004.,Yöneylem Araştırması/ Endüstri Mühendisliği XXIV Ulusal Kongresi, Gaziantap- Adana.
  • Sofu, A.B. (2006). “Yoğurtların depolama esnasında mikrobiyal ve kimyasal değişimlerinin bilgisayarlı görüntüleme sistemiyle belirlenmesi ve elde edilen verilerin yapay sinir ağlarıyla değerlendirilmesi”, Gıda Mühendisliği Anabilim Dalı, Isparta.
  • Tamari, S., Ruiz-Sudrez, J.C., and Wösten, J.H.M. (1996). “Testing an artificial neural network for predicting soil hydraulic conductivity”, Proceedings of 6th Intern. Conf. On Computers In Agriculture, Mexico: 912-919.
  • Torkashvand, A. M., Ahmadi, A., and Nikravesh, N. L. (2017). “Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR)”, Journal of integrative agriculture, 16(7), 1634-1644.
  • Tsenga, F.M., Yu, H.C., and Tzeng, G.H. (2002). “Combining neural network model with seasonal time series arima model”, Technol. Forecasting Soc. Change 69, 71-87.
  • Tuik, 2017. http://www.tuik.gov.tr, (15.12.2019)
  • Turkish State Meteorological Service (TSMS), 2018, Rain and temperature data, (15.12.2019)
  • Wang, J.H. and Leu, J.Y. (1996). “Stock market trend prediction using Arima-based neural networks”, IEEE Int. Conf. Neural Networks 4, 2160-2165.
  • Yarar, A. (2004). “Beyşehir su gölü su seviyesi değişimlerinin yapay sinir ağları ile belirlenmesi,” Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, İnşaat Mühendisliği Ana Bilim Dalı, Konya.
  • Yu, L., Wang, S.Y., and Laic, K.K. (2005). “A novel nonlinear ensemble forecasting model incorporating glar and Ann for foreign exchange rates”, Comput. Oper. Res. 32, 2523-2541.
  • Yurtoğlu, H. (2005). “Yapay sinir ağları metodolojisi ile öngörü modellemesi:bazı makroekonomik değişkenler için türkiye örneği”, Uzmanlık Tezi,Ekonomik Modeller Ve Stratejik Araştırmalar Genel Müdürlüğü, Ankara. Zou, H.F, Xia, G.P, Yang, F.T, and Wang, H.Y. (2007). “An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting”. Neurocomputing 70, 2913-2923.

Prediction of Citrus Fruits Production using Artificial Neural Networks and Linear Regression Analysis

Year 2020, Volume: 13 Issue: 3, 972 - 983, 31.12.2020
https://doi.org/10.18185/erzifbed.679531

Abstract

Accurate and timely prediction of fruits production plays a significant role in the agriculture industry. Therefore, it is very important to predict of citrus fruits production. In this study, prediction the production amount of different citrus fruits for a city of Turkey (Adana) is aimed. Orange, mandarin and bigarade are included as citrus products and the production amounts of ten years are used as dataset. Artificial neural network (ANN) and linear regression analysis are performed for predicting the production amounts. A feed forward neural network is proposed with regarding some inputs such as districts of Adana, product types, product specific plant area, average yield per tree, number of fruitless trees, number of fruit trees, total number of trees, population, inflation rate, total fruit area, temperature, average rainfall. The obtained results in which the R2 values are greater than 0.98 for all datasets show us that the proposed method can predict the production amount accurately regarding the input parameters.

References

  • Alp, M. and Cığızoğlu, K. (2004). “Farklı yapay sinir aği metotları ile yağış-akiş ilişkisinin modellenmesi”. İTÜ Dergisi/ D Mühendislik 3(1), 80-88.
  • Baş, N. (2006). “Yapay sinir ağları yaklaşımı ve bir uygulama.” Mimar sinan güzel sanatlar üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul.
  • Çolak, C., Çolak, C.M., and Atıcı, M.A. (2005). “Ateroskloroz’un tahmini için bir yapay sinir ağı”, Ankara Üniversitesi Tıp Fakültesi Mecmuası , 58, 159-162.
  • Elizondo, D.A., McClendon, R.W., Hoongenboom, G. (1994). “Neural network models for predicting flowering and physiological maturity of soybean”. Transactions of the ASABE 37, 981–988
  • Fiona, M. R., Thomas, S., Maria, I. J., and Hannah, B. (2019). “Identification Of Ripe And Unripe Citrus Fruits Using Artificial Neural Network”. In Journal of Physics: Conference Series, 1362(1), 012033.
  • Grossberg, S. (1988). “Nonlinear neural networks: principles. Mechanisms, and architectures”, Neural Networks 1(1), 17-61.
  • Ho, S.L., Xie, M., and Goh, T.N. (2002). “A comparative study of neural network and box-jenkins arima modeling in time series prediction”, Comput. Ind. Eng. 42, 371-375.
  • Kisi, Ö. (2004). “Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt,” Hydrological Sciences Journal, 49(6),1025-1040.
  • Kohzadi, N., Boyd, M.S., Kermanshahi, B., and Kaastra, I. (1996). “A comparison of artificial neural network and time series model for forecasting commodity price”, Neurocomputing 10, 169-181.
  • Luxhoj, J.T, Riis, J.O, and Stensballe, B. (1996). “Neural network models for forecasting univariate time series”, Neural Networks World 6, 747-772.
  • Movagharnejad, K. and Nikzad, M. (2007). “Modeling of tomato drying using artificial neural network”, Journal Computers and Electronics in Agriculture, 59(1), 2.
  • Parmar, R.S. (1997). “All estimation of aflatoxin contamination in preharvest peanuts using neural networks”, Transactions of the ASAE, 40(3), 809-813.
  • Priddy, K. L., and Keller, P. E. (2005). “Artificial neural networks: an introduction” SPIE press, 68.
  • Prasetyo, D., and Bimantaka, R. D. M. (2018). “Identification of Red Dragon Fruit Using Backpropagation Method Based on Android”. International Journal of Applied Business and Information Systems, 2(2), 40-45.
  • Prybutok, V.R, Yi, J, and Mitchell, D. (2000). “Comparison of neural network models with arima and regression models for prediction of Houston’s daily maximum ozone concentrations”, Eur. J. Oper. Res. 122, 31-40.
  • Şahin, C., Oğulata, S.N., Kırım, S., and Koçak, M. (2004). “Troid bezi bozukluklarının yapay sinir ağları ile teşhisi,” YA/EM’2004.,Yöneylem Araştırması/ Endüstri Mühendisliği XXIV Ulusal Kongresi, Gaziantap- Adana.
  • Sofu, A.B. (2006). “Yoğurtların depolama esnasında mikrobiyal ve kimyasal değişimlerinin bilgisayarlı görüntüleme sistemiyle belirlenmesi ve elde edilen verilerin yapay sinir ağlarıyla değerlendirilmesi”, Gıda Mühendisliği Anabilim Dalı, Isparta.
  • Tamari, S., Ruiz-Sudrez, J.C., and Wösten, J.H.M. (1996). “Testing an artificial neural network for predicting soil hydraulic conductivity”, Proceedings of 6th Intern. Conf. On Computers In Agriculture, Mexico: 912-919.
  • Torkashvand, A. M., Ahmadi, A., and Nikravesh, N. L. (2017). “Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR)”, Journal of integrative agriculture, 16(7), 1634-1644.
  • Tsenga, F.M., Yu, H.C., and Tzeng, G.H. (2002). “Combining neural network model with seasonal time series arima model”, Technol. Forecasting Soc. Change 69, 71-87.
  • Tuik, 2017. http://www.tuik.gov.tr, (15.12.2019)
  • Turkish State Meteorological Service (TSMS), 2018, Rain and temperature data, (15.12.2019)
  • Wang, J.H. and Leu, J.Y. (1996). “Stock market trend prediction using Arima-based neural networks”, IEEE Int. Conf. Neural Networks 4, 2160-2165.
  • Yarar, A. (2004). “Beyşehir su gölü su seviyesi değişimlerinin yapay sinir ağları ile belirlenmesi,” Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, İnşaat Mühendisliği Ana Bilim Dalı, Konya.
  • Yu, L., Wang, S.Y., and Laic, K.K. (2005). “A novel nonlinear ensemble forecasting model incorporating glar and Ann for foreign exchange rates”, Comput. Oper. Res. 32, 2523-2541.
  • Yurtoğlu, H. (2005). “Yapay sinir ağları metodolojisi ile öngörü modellemesi:bazı makroekonomik değişkenler için türkiye örneği”, Uzmanlık Tezi,Ekonomik Modeller Ve Stratejik Araştırmalar Genel Müdürlüğü, Ankara. Zou, H.F, Xia, G.P, Yang, F.T, and Wang, H.Y. (2007). “An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting”. Neurocomputing 70, 2913-2923.
There are 26 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Elifcan Göçmen 0000-0002-0316-281X

Yusuf Kuvvetli 0000-0002-9817-1371

Publication Date December 31, 2020
Published in Issue Year 2020 Volume: 13 Issue: 3

Cite

APA Göçmen, E., & Kuvvetli, Y. (2020). Prediction of Citrus Fruits Production using Artificial Neural Networks and Linear Regression Analysis. Erzincan University Journal of Science and Technology, 13(3), 972-983. https://doi.org/10.18185/erzifbed.679531