A Completion on Fruit Recognition System Using K-Nearest Neighbors Algorithm

Recognition of several fruit images is major challenges for the computers. Mostly fruit recognition techniques which combine different analysis method like color-based, shaped-based, size-based and texture-based. Different fruit images color and shape values are same, but not robust and effective to recognize and identify the images. We introduce new fruits recognition techniques. This combines four features analysis method shape, size and color, texture based method to increase accuracy of recognition. Proposed method used is nearest neighbor classification algorithm. These methods classify and recognize the fruit images from the nearest training fruit example. In this paper it takes the fruit images as input and then recognition system shows the fruit name. Proposed fruit recognition system analyses, classifies and identifies the Fruit recognition system improves the educational learning purpose sharply for small kids and used grocery store to automate labeling and computing the price.