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

Abstract

There are several methods for detecting plant nitrogen content including plant analysis, leaf chlorophyll measurement, and remote sensing techniques. In this study, image processing method was performed to estimate St. John’swort (Hypericum perforatum L.) leaf chlorophyll concentration. The experiment was carried out Hougland solution as a fertilizer 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 chlorophyll-b (chl-b) of the leaves were measured by UV spectrometer. The 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 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) and mean square error (MSE) between the estimated and the measured SPAD values, which were obtained from validation tests, appeared to be 0.99 and 0.005, respectively. 

Keywords

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Yapay Sinir Ağı ile Görüntü İşleme Kullanarak Kantaronda Klorofil Konsantrasyon Endeksi Tahmini

Öz

Bitki azot içeriğinin tespiti için bitkisel analizlerin dahil olduğu yaprak klorofil ölçümü ve uzaktan algılama tekniklerin de dahil olduğu çeşitli yöntemler mevcuttur. Bu çalışmada, görüntü işleme yöntemi ile kantaron (Hypericum perforatum L.) yapraklarının klorofil konsantrasyonu tahmin edilmiştir. Araştırmada, saksılarda yetiştirilen kantaronlara 5 farklı dozda Hougland solusyonu gübre olarak uygulanmıştır. Yaprakların klorofil konsantrasyonunun ölçülmesinde SPAD-502 klorofil metre kullanılmıştır. UV spektrometresi ile yaprakların klorofil-a (CHL-a) ve klorofil-b (CHL-b) içerikleri ölçülmüştür. Yapay Sinir Ağı (YSA) modeli kullanılarak klorofil konsantrasyonunu tahmin etmek için bir dijital kamera ile çekilen renkli görüntülerin RGB (kırmızı, yeşil ve mavi) bileşenlerinden faydalanılmıştır. Sonuç olarak yapay sinir ağı ile yüksek doğrulukta kantaron yapraklarının klorofil konsantrasyonunu tahmin edilmiştir. Doğrulama  R² 0.99 ve MSE 0.005 olarak elde edilmiştir .Bu değerler yapay sinir ağı modelinin güvenirliliğini ortaya koymaktadır.

Anahtar Kelimeler

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References

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