Estimating Chlorophyll Concentration Index in Sugar Beet Leaves Using an Artificial Neural Network

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Çalışkan Ö., Kurt D., Camas N., ODABAŞ M. S.

POLISH JOURNAL OF ENVIRONMENTAL STUDIES, vol.29, no.1, pp.25-31, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.15244/pjoes/95031
  • Journal Name: POLISH JOURNAL OF ENVIRONMENTAL STUDIES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Central & Eastern European Academic Source (CEEAS), Environment Index, Greenfile, Public Affairs Index, Veterinary Science Database
  • Page Numbers: pp.25-31
  • Keywords: SPAD meter, Beta vulgaris, sugarbeet, artificial neural network, precision agriculture, METER, REFLECTANCE
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

The artificial neural network (ANN) method was used in this study for predicting sugar beet (Beta vulgaris L.) leaf chlorophyll concentration from leaves. The experiment was carried out in field conditions in 2015-2016. In this research, symbiotic mychorrhizae as Bio-one (Azotobacter vinelandii and Clostridium pasteurianum) in commercial preparation (10 kg/da) and ammonium sulfate (40 kg/da) were use used as a fertilizer. In order to measure the leaves' chlorophyll concentration we used a SPAD-502 chlorophyll meter. Artificial neural network, red, green, and blue components of the images were used which was developed to predict chlorophyll concentration. The results showed the ANN model able to estimate sugar beet leaf chlorophyll concentration. The coefficient of determination (R-2) was found to be 0.98 while mean square error (MSE) was obtained as 0.007 from validation.