ODABAS, Mehmet Serhat Ondokuz Mayis University - Bafra Vocational School, Turkey , BAJWA, Sreekala North Dakota State University - Department of Agricultural and Biosystems Engineering, USA , LEE, Chiwan North Dakota State University - Department of Plant Science, USA , MARAŞ, Erdem Emin Ondokuz Mayis University - School of Aviation, Turkey

The Prediction of Saint John’s Wort Leaves’ Chlorophyll Concentration Index using Image Processing with Artificial Neural Network

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There are several methods for detecting plant nitrogen content including plant analysis like leaf chlorophyll measurement and remote sensing techniques. In this study, image-processing method was used to predict St. John’s wort (Hypericum perforatum L.) leaf chlorophyll concentration from leaves. The experiment was carried out in greenhouse conditions. The Hougland solution was used as a fertilizer. It was applied at 5 different levels to the St. John’s wort grown in pots. SPAD-502 chlorophyll meter was used for measuring the chlorophyll concentration of the leaves. The chlorophyll-a (chl-a) and chlorophyllb (chl-b) of the leaves were measured by UV spectrometer. Artificial Neural Network (ANN) model was developed based on the RGB (red, green, and blue) components of the color image captured with a digital camera for estimating the chlorophyll concentration. According to the obtained results, the neural network model is capable of estimating the St. John’s wort leaf chlorophyll concentration with a reasonable accuracy. The coefficient of determination (R2) was 0.99 and mean square error (MSE) was obtained 0.005 from validation.

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Image processing , Artificial neural network , Chlorophyll , Hypericum , St. John’s Worth

Yuzuncu Yil University Journal Of Agricultural Sciences

292