Application of RGB-CCM and GLCM texture analysis to predict chlorophyll content in Vernonia amygdalina

Abstract

Vernona amygdalina has been scientifically proven to have activity against various diseases i.e. anti-inflammatory, antimicrobial, antioxidant, and anti-allergic. Detection of chlorophyll content in the leaves by non-invasive sensing is very important to estimate the antioxidant content. The purpose of this study was to predict the chlorophyll content of Vernona amygdalina leaves using computer vision as non-invasive sensing method. Artificial neural network (ANN) was used to model RGB colour co-occurrence matrix (CCM) and grey level co-occurrence matrix (GLCM) textural features as input and leaf chlorophyll content as output. Performance comparisons in each ANN model were carried out to find the best model in predicting leaf chlorophyll content, indicated by the smallest prediction error value. The results showed that ANN can describe the relationship between textural features and leaf chlorophyll content. Red CCM textural features-subset showed the best results when compared to Green CCM, Blue CCM, and GLCM. The learning process in the training set data showed the MSE value of 0.0099, while the MSE value of the validation set data was 0.0472. The ANN model structure that can be used to predict chlorophyll content in Vernona amygdalina leaves consisted of 3 layers with 30 hidden nodes.