Betel leaf classification using color-texture features and machine learning approach

The existence of machine learning has been exploited to solve difficulties in various fields, including the classification leaf species agriculture. Betel is one plants that provide health advantages. objective using a approach classify betel species. This study involved several processes: image acquisition, region interest (ROI) detection, pre-processing, feature extraction, and classification. extraction used combination features color texture. Furthermore, applied four classifiers, artificial neural network (ANN), K-nearest neighbors (KNN), Naive Bayes, support vector (SVM). evaluation this implemented cross-validation with K-fold value 5. method performance produced highest accuracy 100% texture SVM classifier.