Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

Temizel K. E., ODABAŞ M. S., Senyer N., KAYHAN G., Bajwa S. G., Çalışkan Ö., ...More

CENTRAL EUROPEAN JOURNAL OF BIOLOGY, vol.9, no.12, pp.1226-1234, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 12
  • Publication Date: 2014
  • Doi Number: 10.2478/s11535-014-0356-4
  • Journal Name: CENTRAL EUROPEAN JOURNAL OF BIOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1226-1234
  • Keywords: Hypericum, Multiple Linear Regression (MLR), ANN, ANFIS, Stress Condition, ARTIFICIAL NEURAL-NETWORKS, LOGISTIC-REGRESSION, NORTHERN TURKEY, SALINITY, POPULATIONS, PERFORATUM, GROWTH
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

Lack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John's wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.