Research on Multi-class Fruits Recognition Based on Machine Vision and SVM

Abstract

Picking fruit using by machine is an important research direction to liberate labor force and the first step of automatic picking is to recognize fruit. In order to improve the adaptability and reduce the cost of fruit picking robot, it is necessary to recognize multi-class fruit. In this paper, the recognition of multi-class fruit was studied with 6 kinds of fruit, such as apple, banana, citrus, carambola, pear, pitaya and so on. Firstly, the obtained fruit images were processed with Gaussian filter, histogram equalization and other image preprocessing. Secondly, the Otsu segmentation algorithm was used to segment the fruit image, and the edge of the image is extracted by the Canny edge detection operator. Thirdly, the shape invariant moment and other methods were used to synthesize the color and shape characteristics of the fruit to extract feature. Finally, the SVM classifier was applied to classify and recognize fruits according to the extracted feature vectors. The results showed that the recognition rate of 6 fruits, such as apples, bananas, citrus, carambola, pear and pitaya, were 95%, 80%, 97.5%, 86.7%, 92.5% and 96.7%, which could meet the needs of the fruit picking robot and lay the foundation for picking multi-class fruit by the picking robot.