Symbolic regression of crop pest forecasting using genetic programming

Symbolic Regression Of Crop Pest Forecas GENETİC

Makale detayları

Symbolic regression of crop pest forecasting using genetic programming

Yazarlar:

Symbolic Regression Of Crop Pest Forecas GENETİC

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2012 Vol.20 Issue Sup.2, pp.1332-1342

Belge Tipi:

Makale

Yayın Tarihi:

11/2011

Belge Dili:

Türkçe

Document ID (Org No.):

2000743696

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

In this paper, we propose and evaluate a mathematical model  that describes the reported data on crop pests to get an accurate  prediction of production costs, food safety, and the protection of the environment.  Meteorological factors are not the only things that  affect a bumper harvest; it is also affected by crop plant diseases  and insect pests.  Studies show that relying solely on the naked-eye  observations of experts to forecast well-planned agriculture is not  always sufficient to achieve effective control.  Providing fast,  automatic, cheap, and accurate artificial intelligence-based solutions for that task can be of great realistic significance.  The proposed approach is genetic programming (GP)-based and is explicitly directed at solving the symbolic regression of crop pest forecasting.  The GP approach is used to create a fitted crop pest model.  Our experimental results indicate that the GP model can significantly support an accurate and automatic building of a reliable mathematical model. Furthermore, a comparison between the GP model and a linear regression model is also provided.  The developed GP model can successfully achieve a precision of approximately 0.0557.

 

In this paper, we propose and evaluate a mathematical model  that describes the reported data on crop pests to get an accurate  prediction of production costs, food safety, and the protection of the environment.  Meteorological factors are not the only things that  affect a bumper harvest; it is also affected by crop plant diseases  and insect pests.  Studies show that relying solely on the naked-eye  observations of experts to forecast well-planned agriculture is not  always sufficient to achieve effective control.  Providing fast,  automatic, cheap, and accurate artificial intelligence-based solutions for that task can be of great realistic significance.  The proposed approach is genetic programming (GP)-based and is explicitly directed at solving the symbolic regression of crop pest forecasting.  The GP approach is used to create a fitted crop pest model.  Our experimental results indicate that the GP model can significantly support an accurate and automatic building of a reliable mathematical model. Furthermore, a comparison between the GP model and a linear regression model is also provided.  The developed GP model can successfully achieve a precision of approximately 0.0557.

Makale detayları

Symbolic regression of crop pest forecasting using genetic programming

Yazarlar: Symbolic Regression Of Crop Pest Forecas GENETİC

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2012 Vol.20 Issue Sup.2, pp.1332-1342

Belge Tipi: Makale

Yayın Tarihi: 11/2011

Belge Dili: Türkçe

Document ID (Org No.): 2000743696

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Symbolic Regression Of Crop Pest Forecas GENETİC

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2012 Vol.20 Issue Sup.2, pp.1332-1342

Belge Tipi:

Makale

Yayın Tarihi:

11/2011

Belge Dili:

Türkçe

Document ID (Org No.):

2000743696

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632