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Energy indices estimation of greenhouse cucumber production using artificial neural networks

Credit to Download: 1 | Page Numbers 12 | Abstract Views: 81
Year: 2019
COI code: ICSDA04_0532
Paper Language: English

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Authors Energy indices estimation of greenhouse cucumber production using artificial neural networks

  Fatemeh Hosseini-Fashami - M.Sc Student, Department of Mechanics of Biosystem Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
  Ali Motevali - Assistant Professor, Department of Mechanics of Biosystem Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
  Ashkan Nabavi-Pelesaraei - PhD Graduated, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
  Seyed Jafar Hashemi - Associated Professor, Department of Mechanics of Biosystem Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

Abstract:

The main aim of this study was to determine modeling of energy indices of greenhouse cucumber production using artificial neural networks in Alborz province of Iran. For this purpose the initial date were collected from 30 greenhouse cucumber producers by a face-to-face questionnaire in the studied area. Total energy consumption and greenhouse cucumber yield were 972247.47 MJ ha-1 and 133227.40 kg ha-1, respectively. Diesel fuel with 83.84% had the highest share of energy use among all of the inputs. The energy indices analysis indicated that energy ratio, energy productivity, specific energy, net energy and energy intensiveness were about 0.11, 0.14 kg MJ-1, 7.30 MJ kg-1, -865665.55 MJ ha-1 and 7.55 MJ $-1, respectively. The Levenberg-Marquardt learning algorithm was trained for calculation of prediction models for energy indices based energy inputs and area. The results of the ANN model revealed the 9-13-5 structure belonged to the best topology with highest R2 and lowest RMSE and MAPE. The rate of R2, RMSE and MAPE was computed between 0.951-0.994, 0.139-0.270 and 0.005-0.019, respectively. Generally, the results of present study illustrated that ANN model can model the energy indices with high accuracy.

Keywords:

Artificial neural network, Cucumber, Energy, Modeling

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COI code: ICSDA04_0532

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Hosseini-Fashami, Fatemeh; Ali Motevali; Ashkan Nabavi-Pelesaraei & Seyed Jafar Hashemi, 2019, Energy indices estimation of greenhouse cucumber production using artificial neural networks, 4th International Congress of Developing Agriculture, Natural Resources, Environment and Tourism of Iran, تبريز-دانشگاه هنر اسلامي تبريز, دبيرخانه دائمي-دانشگاه ميعاد و با همكاري دانشگاه شيراز،دانشگاه ياسوج و دانشگاه مازندران, https://www.civilica.com/Paper-ICSDA04-ICSDA04_0532.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Hosseini-Fashami, Fatemeh; Ali Motevali; Ashkan Nabavi-Pelesaraei & Seyed Jafar Hashemi, 2019)
Second and more: (Hosseini-Fashami; Motevali; Nabavi-Pelesaraei & Hashemi, 2019)
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Type: state university
Paper No.: 5712
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