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

Elifcan GÖÇMEN; Yusuf KUVVETLİ

doi:https://doi.org/10.18185/erzifbed.679531

Article Details

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

Authors:

Elifcan GÖÇMEN | Yusuf KUVVETLİ

Publisher:

Erzincan Binali Yildirim University

Main Topics:

Applied physics

Journal:

Erzincan University Journal of Science and Technology 2020 Vol.13 Issue 3, pp.972-983

Document Type:

Article

Publication Date:

12/2020

Document Language:

English

Document ID (Org No.):

2000414432

eISSN:

2149-4584

pISSN:

1307-9085

DOI:

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.

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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.

Article Details

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

Authors: Elifcan GÖÇMEN | Yusuf KUVVETLİ

Publisher: Erzincan Binali Yildirim University

Main Topics: Applied physics

Journal: Erzincan University Journal of Science and Technology 2020 Vol.13 Issue 3, pp.972-983

Document Type: Article

Publication Date: 12/2020

Document Language: English

Document ID (Org No.): 2000414432

eISSN: 2149-4584

pISSN: 1307-9085

DOI: https://doi.org/10.18185/erzifbed.679531

Issues

Authors:

Elifcan GÖÇMEN | Yusuf KUVVETLİ

Publisher:

Erzincan Binali Yildirim University

Main Topics:

Applied physics

Journal:

Erzincan University Journal of Science and Technology 2020 Vol.13 Issue 3, pp.972-983

Document Type:

Article

Publication Date:

12/2020

Document Language:

English

Document ID (Org No.):

2000414432

eISSN:

2149-4584

pISSN:

1307-9085

DOI:

https://doi.org/10.18185/erzifbed.679531