Identifying Irregular Potatoes by Developing an Intelligent Algorithm Based on Image Processing

Abstract

The objective of this study was to develop an algorithm based on image processing for detecting misshapen potatoes from the mass of potatoes and obtaining homogeneous products. The database used in this research included the digital images acquired from Agria variety of Ardabil (Iranian northern-west) potato with different sizes and shapes. A combination of morphological features including geometrical features like length, width and features related to shape such as roundness were taken into consideration in identifying irregular potatoes from others employing elongation and Fourier descriptors. Using statistical principal component analysis (PCA), seven features were selected as the most prominent for classification. The experimental results showed that the proposed method achieves a high level of accuracy with merely seven selected discriminative features, obtaining an average correct classification rate of 98% for training set. Additionally, regular potatoes were separated into small, medium and large categories with 100% accuracy. According to the results, the developed algorithm based on image processing can be used in classifying products with no proper shape.