Recognition of fruits using hybrid features and machine learning

Recognition of fruits automatically using machine vision is considered as challenging task as fruits exist in various colors, sizes, shapes and textures. Additionally, when images are acquired of them, variation is introduced due to imaging conditions also. In this paper we have recognized nine different classes of fruits. Fruit image dataset are obtained from web as well as certain images are acquired by using mobile phone camera. These images are pre-processed to subtract the background and extract the blob representing fruit. For representing fruits and capturing their visual characteristics, combination of color, shape and texture features are used. These feature dataset is further passed to two different classifiers; multiclass SVM and KNN. The experimental results obtained are used to draw various conclusions. The best accuracy obtained by us in the study is 91.3% with KNN (K=2), classifier whereas with multiclass SVM (one-versus-all), the best accuracy obtained is 86.96%.