###### Abstract
In [14], a best color segmentation method for the grapes in the vineyard images captured at night was developed by extracting the H component of the HSV color space to estimate vineyard yield. In [15], the conventional RGB color images were transformed into the YIQ color space, and then the threshold of color intensity was obtained by a trial and error manual operation. In [16], the threshold of color intensity was obtained by analyzing the histogram of the transformed images. To detect the red peaches in orchard images, a classification method was proposed in [17] based on different linear color models in the RGB vector color space, and then the segmentation of the pixels of the image was performed by the classifier. In [18], a grape bunch segmentation algorithm combining color and texture information and the use of a support vector machine(SVM) was proposed, one new filter and one new feature were designed for eliminating false detected bunches. In [19], a grape berry detection method based on both visual texture and shape was developed, which could detect the green berries against a green leaf background. From the above fruit detection approaches we can know that most of current studies on grape detection in the vineyard have focused on the pixel-based color segmentation and the texture-based detection approaches, whose main goals were to estimate the vineyard yield [10; 18; 19; 20] or to identify the targets for automatic selective vineyard sprayer [9]. These approaches relied more on some effective color components and the descriptor of the texture. However, in actual cases, selecting the best color component was still one of the difficulties in color image segmentation [21]. Moreover, it was also difficult to identify a target only by relying on one kind of color space classification in the dynamic and complex vineyard environment. With the rapid development of machine learning technology, more and more vision learning approaches have started to be used for detecting fruits in outdoor scenes [22]. [PERSON] et al. [23] developed a grape detection approach based on support vector machine (SVM) by selecting Zernike moments and color information as features. In [8], an algorithm to detect tomatoes in greenhouse scenes was proposed, in which the possible tomato objects were firstly identified by extracting the Haar-like features of grey scale image and classifying by the AdaBoost classifier, and then the false classification were eliminated by color analysis approach. In [24], a two-step coarse-to-fine wheat ear detection mechanism was proposed. To improve the detection accuracy, the machine learning technology was used to identify candidate targets in the coarse-detection step. In [25], a pedestrian detection approach was designed by establishing coupled strong classifiers based on AdaBoost framework. To predict the class of berry color, in [26], the mean of the RGB values of all grape berries detected in one image were used for statistical analysis using linear discriminant analysis. Inspired by the machine learning methods and the pixel-based color segmentation methods, this paper focused mainly on the combination of two methods to find a robust detection approach for the grape harvesting robots. In the vineyard, the environments conditions are variable, so the robustness of unsupervised machine learning methods may be at risk [27]. Therefore supervised machine learning methods are of special interest in this field. Since the training set of the supervised algorithm is prepared a priori, which establishes what features will correspond to the elements of a class, this reduces the uncertainty of the complex vineyard [20]. In the supervised machining learning methods, one of the most successful paradigms is ensemble learning [28], and AdaBoost [29] is one of the most influential ensemble methods. In contrast to ordinary machine learning approaches which try to generate one learner from training data, AdaBoost methods try to construct a set of base learners and combine them.