Using Artificial Neural Network and Multiple Linear Regression for Predicting the Chlorophyll Concentration Index of Saint John's Wort Leaves

ODABAŞ M. S., KAYHAN G., Ergün E., Senyer N.

COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, cilt.47, sa.2, ss.237-245, 2016 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 47 Sayı: 2
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1080/00103624.2015.1104342
  • Dergi Adı: COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.237-245
  • Anahtar Kelimeler: Artificial neural network, chlorophyll concentration index, Hypericum perforatum L, modeling, precision agriculture, SYSTEM
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

This research investigates and compares artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John's wort leaves (Hypericum perforatum L.). Plants were fertilized with 0, 30, 60, 90, and 120kgha(-1) nitrogen [34% nitrogen ammonium nitrate (NH4NO3)]. Chlorophyll concentration index of each leaf was measured using SPAD meter. Afterwards, rgb (red, green, and blue color) values of all leaf images were determined by image processing. Values obtained were modeled using both multiple regression analysis and artificial neural networks. Using multiple regression analysis R-2 values were between 0.61 and 0.97. Coefficient of determination values (R-2) using artificial neutral network values were found to be 0.99. Artificial neutral network modeling successfully described the relationship between actual chlorophyll concentration index values and predicted chlorophyll concentration index values.