## 1 Introduction  
One of the most important food industries is the grape growing and wine-making industry, which is currently introducing several enhanced vineyard management techniques, which involve automatic leaf estimation [1], fruit harvesting [2,3], yield estimation [4, 5, 6, 7], grape quality evaluation [8] and grapevine variety identification [9]. The wine industry has the challenges of performing accurate yield prediction, estimation and quality control [10], because such factors are affected by environmental variables (soil factors, climate, plant diseases), forecast and pollen concentration [11], farming factors, such as adding products (water, pesticide, fertilizer, herbicides) [12, 13], and agricultural tasks [14] (number of sprouts, informed pruning, shoot thinning, bunch thinning, number of bunches, prune weight, _etc._), which makes the feasible management of the vineyard more complicated. Crop management can be improved by using remote sensors [6] configured in airborne [14, 15] and terrestrial applications, such as crop classification, crop area estimation, canopy measurements, identification of harvest dates, crop yield estimation, detection of pest occurrence, detection of disease occurrence, mapping weed infestation and monitoring abiotic stress. For example, in [7], a terrestrial LIDAR device was proposed in order to obtain canopy volume and tree shape information in peach orchards and to analyze relationships between the measured LIDAR tree volume and yield and fruit weight. The conclusion obtained was that the LIDAR is a suitable technique to assess fruit tree production capacity. Another alternative is the use of specialized terrestrial vision systems. Another example is the location and detection of fruits on trees [16] by placing a camera at different positions and different viewing angles (azimuth and zenith angles). In this case, the best results were obtained when locating the camera in front of the fruit with a zenith angle of 60\({}^{\circ}\) upwards. Additionally, the maximum detection of fruit (90%) was achieved when using five multiple viewpoints positions. In [17], a new method based on segmenting the point cloud obtained by using a 3D camera into convex surfaces was implemented for individual fruit recognition and detection. The conclusions obtained were that the proposed method can be used for fruit detection, although this detection is extremely sensitive to changes in lighting conditions [4, 5, 18] and the color similarity between the fruit and the background [19]. In this direction, in [20], an image-processing technique was proposed to detect fruits of different degrees of ripeness by using RGB images in combination with automatic machine learning, obtaining classification ratios from 0.78 to 1.00 for different ripening conditions. In [21], the specific problems originating from daylight operation were identified: skylight transmission from the back side of trees, direct sunlight reflectance from non-fruit materials and variations in natural lighting. Similarly, in [22], it was proven that changing solar angles, cloud cover and leaf occlusions leads to lighting variations that complicate the segmentation process. In order to manage the fruit skin color variability in images, the proposals of [18, 23] were to address fruit skin color daylight variability by defining a linear color model in the RGB color space and computing the pixel color intensity distance to these models for direct fruit segmentation. Nevertheless, the general conclusion is that the same segmentation techniques cannot be applied to different scenarios [24]. Regarding the specific case of a vineyard, the problem is the definition of an automatic procedure to recognize and identify grapes or clusters of grapes [19] in order to estimate yield or the optimal harvesting period.