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Estimation of Chlorophyll Concentration Index at Leaves using Artificial Neural Networks

Mehmet Serhat Odabas

Bafra Vocational School, Ondokuz Mayis University, 55270 Samsun, Turkey

E-mail Address: msodabas@omu.edu.tr

E-mail Address: msodabas@gmail.com

Corresponding author.

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Nurettin Senyer

Department of Computer Engineering, Faculty of Engineering, Ondokuz Mayis University, 55270 Samsun, Turkey

E-mail Address: nurettin.senyer@bil.omu.edu.tr

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Gokhan Kayhan

Department of Computer Engineering, Faculty of Engineering, Ondokuz Mayis University, 55270 Samsun, Turkey

E-mail Address: gokhan.kayhan@bil.omu.edu.tr

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, and

Erhan Ergun

Department of Computer Engineering, Faculty of Engineering, Ondokuz Mayis University, 55270 Samsun, Turkey

E-mail Address: erhan.ergun@bil.omu.edu.tr

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https://doi.org/10.1142/S0218126617500268Cited by:10 (Source: Crossref)

Abstract

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.

This paper was recommended by Regional Editor Masakazu Sengoku.

Keywords:

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