INTRDUCTIONThe computer vision strategies used to recognize a fruit using basic features like intensity, color, shape and texture.An important problem in pattern analysis is the automatic rec- ognition of an object in a scene regardless of its position, size, and orientation .Zernike moments are the projection of the image function onto orthogonal basis functions. This paper proposes an efficient fusion of color and texture features along with zernike moment for fruit recognition. Color is a useful property which adds the information to images. The color perceived by human is a combination of three color stimului such as red (R), green (G), and blue (B), which forms a color space . However, many color models are used to represent the colors in various representations such as RGB (red, green, blue), HSV (hue, saturation, intensity), and CMY (cyan, magenta, yellow) . As compared to monochrome images, color images have the information of brightness, hue and saturation for each pixel.

The fruit recognition system has so many applications. The fruit recognition system can be applied for educational purpose to enhanced learning, especially for small kids and Down syndrome patients, of fruits pattern recognition based on the fruit recognition result. It can be used in grocery store which makes the customers label their purchases using automatic fruit recognition based on computer vision.it is very difficult to enable a system for automatic fruit recognition based on images taken from camera.Many kind of fruits are subject to significant variation in color and texture, depending on how ripe they are. For example, Bananas range from being uniformly green, to yellow, to patchy and brown.

The current approaches to invariant two-dimensional shape recognition include extraction of global image information using regular moments , boundary-based analysis via Fourier descriptors or autoregressive models , image representation by circular harmonic expansion, and syntactic approaches. A fundamental element of all these schemes is definition of a set of features for image representation and data reduction. Normally additional transformations are needed to achieve the desired invariant properties for the selected features. After invariant features are computed, they are input to a designed classification rule to decide a labeling for the underlying image.

The fruits recognition system could be applied as an image contents descriptor which is able to describe the low level visual features or contents of the fruit images for the CBIR system. The most popular analys techniques that have been used for both recognition and classifications of two dimensional (2D) fruit images are color-based and shape-based analysis methods. However, different fruit images may have similar or identical color and shape values. Hence, using color or shape features analysis methods are still not robust and effective enough to identify and distinguish fruits images

Fruit detection system is primarily developed for robotic fruit harvesting. However this technology can easily be tailored for other applications such as on tree yield monitoring, crop health status monitoring, disease detection, maturity detection and other operations which require vision as a sensor. For fruit harvesting system, it is very necessary to detect the fruit on the tree more efficiently. The vision based fruit harvesting system for the fruit detection basically depend on the contribution of different features in the image. The four basic features which characterize the fruit are: intensity, color, edge and orientation. This paper proposes an efficient multiple features based algorithm for the fruit detection on tree. Color features in image could be successfully used to segment defects on „Jonagold‟ apples are demonstrated in [8]. Texture features are found to contain useful information for quality evaluation of fruit and vegetables, e.g., classification of grade of apples after dehydration with the accuracy of 95% . Color and texture features are used to locate green and red apples . Combining many features and classifiers, where all features are concatenated and fed independently to each classification algorithm