Fruits and vegetables recognition based on color and texture features

An intelligent fruit and vegetable recognition system utilizing image recognition can accurately and rapidly indentify different kinds of fruits and vegetables, which can improve supermarket and market sales efficiency. The feature extracting method is a very important issue in an intelligent fruit and vegetable recognition system. However, traditional fruit and vegetable recognition algorithms either ignore the texture feature of fruits and vegetables, or used texture features that couldn't better represent the texture of fruit and vegetable images. In order to represent the texture feature of fruit and vegetable images better and improve the intelligent fruit and vegetable recognition system recognition rate, we proposed a novel texture feature extraction algorithms called color completed local binary pattern (CCLBP) in this paper. By extracting different kinds of color channels completed by a local binary pattern (CLBP) texture feature, the CCLBP constructed a new texture feature extraction algorithm. The Fruit and vegetable recognition system model uses CCLBP to extract an image texture feature, and uses a HSV color histogram and Border/interior pixel classification (BIC) color histogram to extract image color features. Then it uses a matching score fusion algorithm to fuse color and texture features, and finally, a nearest neighbor (NN) classifier is used to realize fruit and vegetable recognition. To verify the effectiveness of the algorithms, two different fruit and vegetable databases, called an interior database and an outdoor database, were constructed in this paper. The interior database acquired in a laboratory contains 13 kinds of fruits and vegetables, which is used to verify algorithms recognition performance under different kinds of illumination. The outdoor database acquired in the market contains 47 kinds of fruits and vegetables, which is used to verify algorithms recognition performance under a different number of training sets. A Fruit and vegetable recognition experiment under different kinds of illumination showed that, only by the texture feature indentifying the kinds of fruits and vegetables, the recognition rate of the CCLBP was 5% higher than the traditional fruit and vegetable texture features (such as Unser, TestA), which means that the CCLBP is more suitable for fruit and vegetable recognition; besides, compared with other texture algorithms, the CCLBP fused with HSV color histogram and BIC color histogram can achieve a 73.93% highest mean recognition rate, which takes about 1.1 seconds indentifying an image. A fruit and vegetable recognition experiment under a different number of training sets had similar results as the experiment under different kinds of illumination. The recognition rate of the CCLBP was still higher than the traditional fruits and vegetables texture features. What's more, the CCLBP fused with a HSV color histogram and a BIC color histogram can achieve 94.26%, the highest recognition rate. The experiments under different kinds of illumination and under different number of fruits and vegetables confirm the feasibility of our algorithm. Our algorithm can be used in intelligent fruit and vegetable recognition system, which improves the system accuracy rate.