Estimation of Chlorophyll Concentration Index at Leaves using Artificial Neural Networks

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

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, cilt.26, sa.2, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 2
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1142/s0218126617500268
  • Dergi Adı: JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Medicinal and aromatic plants, artificial neural network, SPAD, MLP, ANFIS, GRNN, GROWTH, ANFIS
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

In this study, the effectiveness of an SPAD-502 portable chlorophyll (Chl) meter was evaluated for estimating the Chl contents in leaves of some medicinal and aromatic plants. To predict the individual chlorophyll concentration indexes of St. John's wort (Hypericum perforatum L.), mint (Mentha angustifolia L.), melissa (Melissa offcinalis L.), thyme (Thymus sp.), and echinacea (Echinacea purpurea L.), models were developed using SPAD value. Multi-layer perceptron (MLP), adaptive neuro fuzzy inference system (ANFIS), and general regression neural network (GRNN) were used for determining the chlorophyll concentration indexes.