## 5 Results
### Yield Estimation Results  
Finally, the area of the clusters of grapes obtained with the best segmentation method (applying a threshold to the \(\mathrm{H}\) color layer) can be converted into a yield estimate by applying the regression curves obtained in the calibration stage. Figure 7 shows the error obtained when estimating the individual weight of a sequence of 25 clusters of grapes computed from the volume and area of the pixels of the grapes; with individual error values from 15.1% to \(-\)21.1% and 18.8% to \(-\)16.5%, respectively. Figure 7: Individual error obtained when comparing the real and estimated weight of 25 clusters of grapes in consecutive images. The grape weight was predicted by using the estimated area and deduced volume of the cluster of grapes. The cluster of grapes shown in Figure 7 is not affected by occlusion, because the hypothesis is that the table-grape variety is properly trimmed for a fresh marker production. However, any occlusion in the grapes will reduce the area of the grapes in the images, reduce the volume of the solid of revolution and then reduce the weight estimated with both methods. Finally, Table 2 shows the total weight estimate and the average error produced for the cases of using the grape cluster area and the grape cluster volume obtained from the images analyzed. In the case of estimating the weight from the area of the cluster of grapes, the total weight computed for 25 non-occluded clusters was 18.382 kg, which represents an error of 16% relative to the real weight measured. Alternatively, in the case of performing this estimate from the estimated volume of the cluster of grapes; the total weight computed for these 25 non-occluded clusters was 13.183 kg, which represents an error of -17% relative to the real weight measured. Table 2 also shows that the average estimations obtained with both methods can be compensated, although this effect can be a numerical coincidence, and future extensive validation experiments performed with large datasets will be needed to validate this compensation effect. The calibration results obtained in Section 3 showed a linear relationship between the weight and the area of the clusters and between the weight and the deduced volume of the clusters (obtained as a solid of revolution of the area of the grapes), but the yield error results obtained in this experimental measurement (16%, -17%) are not conclusive of which method is most suitable for this estimation. Initially, the expectation was to obtain better yield estimates when using the volume of the grapes (obtained as a solid of revolution from the segmented area), but the results have not confirmed this expectation, and other, similar proposals also concluded that a volume estimate will be very sensitive to cluster overlapping and occlusion [28]. The relative yield error obtained when segmenting the vineyard images with the other methods assessed in this paper is worse than the results shown in Figure 7 and Table 2, because the calibration curves are very sensitive to the estimated cluster size; a higher error in the size estimate after segmentation is converted into a higher yield estimate error. The final vineyard yield error obtained with the two methods proposed in this paper in the case of analyzing a limited number of clusters has a similar error range as similar works found in the literature. In [28], the yield error obtained in a realistic experimentation with a controlled illumination was between 3% and 11% when evaluating the information corresponding to several years, hundreds of vines and four grape varieties. In [44], the absolute yield errors reported were in a range between 9 and 15% when using a contact-based estimator and historical data, whereas in [30], the yield error reported in a row of vines was of 9.8%. In the case of other fruits, a yield error within a 10% range is considered valuable in terms of crop management [32].