Estimation of chlorophyll concentration index at leaves using artificial neural networks

Odabaş M. S., ŞENYER N., Kayhan G., Ergün E.

Journal of Circuits, Systems and Computers, cilt.26, sa.2, 2017 (SCI-Expanded, Scopus) 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: ANFIS, Artificial neural network, GRNN, Medicinal and aromatic plants, MLP, SPAD
  • Samsun Üniversitesi Adresli: Hayır

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 officinalis 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.