ORIGINAL RESEARCH
Estimating Chlorophyll Concentration Index
in Sugar Beet Leaves Using an Artificial
Neural Network
1
Ondokuz Mayis University, Bafra Vocational High School, Department of Crop and Animal Production,
Samsun, Turkey
2
Ondokuz Mayis University, Faculty of Agriculture, Samsun, Turkey
Submission date: 2018-05-14
Final revision date: 2018-09-06
Acceptance date: 2018-09-10
Online publication date: 2019-08-02
Publication date: 2019-10-23
Corresponding author
Dursun Kurt
Ondokuz Mayıs University, Ondokuz Mayıs University, Bafra Vocational School, Department of Crop and Animal Production, Bafra, 55420 Samsun, Turkey
Ondokuz Mayıs University, Ondokuz Mayıs University, Bafra Vocational School, Department of Crop and Animal Production, Bafra, 55420 Samsun, Turkey
Pol. J. Environ. Stud. 2020;29(1):25–31
KEYWORDS
TOPICS
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 (R2) was found to be 0.98 while mean square error (MSE) was obtained as 0.007 from
validation.
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