Potential of radial basis function-based support vector regression for apple disease detection

Plant pathologists detect diseases directly with the naked eye. However, such detection usually requires continuous monitoring, which is time consuming and very expensive on large farms. Therefore, seeking rapid, automated, economical, and accurate methods of plant disease detection is very important. In this study, three different apple diseases appearing on leaves, namely Alternaria, apple black spot, and apple leaf miner pest were selected for detection via image processing technique. This paper presents three soft-computing approaches for disease classification, of artificial neural networks (ANNs), and support vector machines (SVMs). Following sampling, the infected leaves were transferred to the laboratory and then leaf images were captured under controlled light. Next, K-means clustering was employed to detect infected regions. The images were then processed and features were extracted. The SVM approach provided better results than the ANNs for disease classification.