Bruise detection on red bayberry (Myrica rubra Sieb. & Zucc.) using fractal analysis and support vector machine

A new method to sort red bayberries based on the presence of bruises was proposed. Principal component-support vector machine (PC-SVM) and support vector machine (SVM) models combined with fractal analysis were developed and compared with classification models based on RGB intensity values. The results of this study show the classification models based on fractal parameters achieved 100% total accuracy rate, but the models based on RGB values was only 85.29%. In addition, the performance of the SVM model in terms of iteration time and the number of support vectors was better than the PC-SVM model. Therefore, the SVM model based on fractal analysis is recommended for detecting bruises on red bayberries.