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American Journal of Potato Research

, Volume 92, Issue 3, pp 426–434 | Cite as

Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

  • Sara Rajabi Hamedani
  • Misbah Liaqat
  • Shahaboddin ShamshirbandEmail author
  • Othman Saleh Al-Razgan
  • Eiman Tamah Al-Shammari
  • Dalibor Petković
Article

Abstract

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to predict potato production in Iran. Data related to potato yield from 2010 to 2011 was collected from 50 potato producers in Hamedan, Iran. The resulting ANFIS network has an input layer with eight neurons and an output layer with a single neuron (potato yield). The energy inputs were manual labor, diesel, chemical fertilizers, and manure from farm animals, chemicals, machinery, water, and seed. The most significant and influential inputs were selected from the eight initial inputs and the ANFIS network was used to choose the parameters that have the most influence on potato yield. A new ANFIS model was created after the three most influential parameters were selected. The new ANFIS model was then utilized to estimate yield using the three energy inputs. Next, the ANFIS model results were compared with the results from the support vector regression (SVR) technique. The end results revealed that ANFIS provided more accurate predictions and had the capacity to generalize. The Pearson correlation coefficient (r) for ANFIS potato yield prediction was 0.9999 in the training and testing phases, while the SVR model had a correlation coefficient of 0.8484 in training and 0.9984 in testing.

Keywords

Potato yield Input energy Prediction ANFIS SVR 

Resumen

En este estudio se desarrolló un sistema de inferencia adaptativa de lógica difusa (ANFIS) para predecir la producción de papa en Irán. Se colectaron datos relacionados con el rendimiento de papa de 2010 a 2011 de 50 productores en Hamedan, Irán. La red ANFIS resultante tiene una capa de insumos con ocho neuronas y una capa de salidas con una única neurona (rendimiento de papa). Los insumos de energía fueron mano de obra, diésel, fertilizantes químicos y estiércol de animales de granja, químicos, maquinaria, agua y semilla. Se seleccionaron los insumos más significativos y de influencia de los ocho insumos iniciales, y se usó la red ANFIS para escoger los parámetros que tienen la mayor influencia en el rendimiento de papa. Se creó un nuevo modelo ANFIS después que se seleccionaron los tres parámetros de mayor influencia. Entonces se utilizó el nuevo modelo ANFIS para estimar rendimiento usando los tres insumos de energía. Después, los resultados del modelo ANFIS se compararon con los resultados de la técnica de regresión de vector de respaldo (SVR). Los resultados finales revelaron que ANFIS suministró predicciones más precisas y tuvo la capacidad de generalizar. El coeficiente de correlación de Pearson (r) para la predicción del rendimiento de papa por ANFIS fue 0.9999 en las fases de formación y de prueba, e el modelo SVR tuvo un coeficiente de correlación de 0.8484 en formación y 0.9984 en prueba.

Notes

Acknowledgments

This work is supported by the Bright Spark Unit, University of Malaya, Malaysia.

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Copyright information

© The Potato Association of America 2015

Authors and Affiliations

  • Sara Rajabi Hamedani
    • 1
  • Misbah Liaqat
    • 2
  • Shahaboddin Shamshirband
    • 2
    Email author
  • Othman Saleh Al-Razgan
    • 3
  • Eiman Tamah Al-Shammari
    • 4
  • Dalibor Petković
    • 5
  1. 1.Department of Agricultural SciencePayame Noor UniversityTehranIran
  2. 2.Department of Computer System and Information Technology, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  3. 3.Kuwait Institute for Scientific Research (KISR)Kuwait CityKuwait
  4. 4.Department of Information ScienceCollege of Computing Sciences and Engineering, Kuwait UniversityKuwait CityKuwait
  5. 5.Faculty of Mechanical Engineering, Department for MechatronicsUniversity of NišNišSerbia

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