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., ...Daha Fazla

CENTRAL EUROPEAN JOURNAL OF BIOLOGY, cilt.9, sa.12, ss.1226-1234, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9 Sayı: 12
  • Basım Tarihi: 2014
  • Doi Numarası: 10.2478/s11535-014-0356-4
  • Dergi Adı: CENTRAL EUROPEAN JOURNAL OF BIOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1226-1234
  • Anahtar Kelimeler: Hypericum, Multiple Linear Regression (MLR), ANN, ANFIS, Stress Condition, ARTIFICIAL NEURAL-NETWORKS, LOGISTIC-REGRESSION, NORTHERN TURKEY, SALINITY, POPULATIONS, PERFORATUM, GROWTH
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

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.