## 5 Results and Discussion
### Classification Performance  
In this section, classification performance results using the three well-known methodologies are exposed, see Figures 8-10. Table 4 shows the classification performance metric under 100 iterations of these three methodologies studied, namely recall, false positive rate, and accuracy through Equations (20)-(22), respectively. Please note that this classification performance metric is directly related to the Receiver operating characteristic (ROC) curve, which is an output performance of the learning system. Remember that the database has 786 albinism disease labels, 164 fungus rust disease labels, 115 over-ripeness defect labels, and 244 peduncle defect labels. Figure 14: Feature ranking from the raspberries diseases and defects using statistical modeling. (**a**) Albinism. (**b**) Fungus-Rust. (**c**) Over-ripeness. (**d**) Peduncle. Figure 13: Feature ranking from the raspberries diseases and defects using descriptive statistics. (**a**) Albinism. (**b**) Fungus-Rust. (**c**) Over-ripeness. (**d**) Peduncle. These results are very interesting. Please note that all the classifiers have difficulty in detecting the over-ripeness defect, this can be due to two reasons. The first is because the color of the raspberry is preserved, but darker, and the second is the over-ripeness is the defect with the least number of labels. Faster R-CNN is the best methodology with respect to the statistical-based learning methods coupled with an ANN. However, they also perform very well for all the defective raspberries studied. Faster R-CNN is very sensitive (TPR) with high fall-out response (FPR), which makes it have excellent detection accuracy. Second, the statistical-based learning methods are very sensitive (TPR) too, but with low fall-out response (FPR), nevertheless, they have good accuracy. This suggests that they may need a regularization parameter to help tune the FPR. Faster R-CNN detects fungus rust disease fairly well, even though the number of tags is low, while albinism disease and peduncle defect achieved a high detection performance, 98% and 100%, respectively. This is consistent with the results of our previous work [2]. The performance detection using the statistical-based models coupled with an ANN are very similar, they are all around 80%, except for the over-ripeness defect. For an illustration, Table 5 shows some recent works on fruit-based quality detection. CNN-based methods are currently at the forefront of developments to detect diseases or defects in different fruit. However, there are still works with other types of techniques, without using neural networks, for example, in [35] the classification was estimated directly using the segmentation technique in olives yielding a reasonable detection rate. Note that the detection accuracy rate is high in all studies, suggesting that there is a concern to improve the quality detection of diverse fruit in agribusiness. Because Table 5 shows different studies, with different techniques, for different fruit, with different amounts of images in their datasets, a comparative analysis should be made regarding fruit-based quality detection. Remarkable that CNN-based methods are currently at the forefront of developments in fruit quality detection. These methods generally outperform conventional feature-based extraction methods, but the computational cost can be sufficiently high. Therefore, feature-based extraction methods must not be detected from them. They continue to prove themselves invaluable in several applications. Furthermore, the computational cost is usually pretty low. The performance detection rate of this work regarding those exposed in Table 5 shows excellent results in the detection of the quality of the fruit, both for the statistical-based learning methods and for the Faster R-CNN. These results suggest that we are accomplishing the parameters and standards of the industry to discriminate between a healthy raspberry and a raspberry with a disease or defect.