Estimating Chlorophyll Concentration Index in Sugar Beet Leaves Using an Artificial Neural Network
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Ondokuz Mayis University, Bafra Vocational High School, Department of Crop and Animal Production, Samsun, Turkey
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
Pol. J. Environ. Stud. 2020;29(1):25–31
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.