###### Abstract  
The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination. g r 201616209810.3390/s1612209810. In recent decades, scholars from different parts of the world have proposed a large number of target detection methods for harvesting robots, but so far, an ideal method to completely solve the difficulties posed by the complex vineyard environment has yet to be found. To overcome the influences caused by uneven illumination conditions and color similarities, various vision sensors have been used for detecting the fruits [2]. In early years, [PERSON] et al. [3] used the spectral characteristics of grapes to identify and locate the harvesting target. [PERSON] et al. [4] adopted a laser-based computer vision system for fruit detection, which is based on an infrared laser range-finder sensor that provides range and reflectance images. [PERSON] et al. [5] and [PERSON] et al. [6] used a binocular stereo vision to recognize and locate fruits in an unstructured environment with varying illumination. To detect apples in the canopy, [PERSON] et al. [7] designed a machine vision system consisting of a time-of-flight (ToF) camera and a digital color charge coupled device (CCD) camera. The detection methods based on the advanced vision sensors and fusion of multiple image data can achieve higher correct detection rates. However, the high-cost of the sensors is the key shortcoming for commercial application [8]. In the aspect of fruit detection in images, [PERSON] et al. [9] utilized the difference in edge distribution between the grape clusters and the foliage to detect the grape clusters for an automatic selective vineyard sprayer. In [10], the number of grape berries were counted by detecting specular spherical reflection peaks in RGB images captured at night under artificial illumination. In [11], a detection approach for the grapes in the vineyard images captured at night was proposed by defining a region of pixel intensities in the RGB color space based on a trial and error procedure. [PERSON] et al. [12] proposed an image segmentation approach for grape in the vineyard based on the improved fuzzy clustering method by artificial swarm and the H-I color component, its accuracy was 90.33%. In [13] recognized the kiwifruit at nighttime by extracting R-G color components. The success rate of the recognition algorithm was 88.3%.