# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 7 Discussion on the Review of CNN-Based Approaches for Fruit Image Processing  
In this paper, we present a comprehensive review of state-of-the-art CNN-based approaches for fruit image processing. We have analyzed the main latest contributions of three important application areas--fruit classification, fruit quality control, and fruit detection. Regarding the CNN architectures used in collected studies, we can group them into two general categories--\"own\" and and \"pre-trained\" networks. In the first group, the authors build the network from scratch, and in the second they use well-known CNN models such as AlexNet, GoogLeNet, among others. Additionally, this second group is subdivided into two subgroups, one is where the pre-trained networks are taken for a transfer learning process, and the other subgroup made modifications on the pre-trained network adapting them to the objective of the study. Figure 7 shows the overall distribution of the CNN models used by the authors. It is observed that in 70% of the cases correspond to pre-trained models. Generally, this is because the primary aim in the agro-industry is to use CNNs as a support tool in the development of applications. According to the above, it is worth noticing that the number of layers depends on the kind of task to be performed. If the task is more complex and a greater number of characteristics must be extracted, then the number of layers and filters must be increased. Figure 7: General distribution of CNN architectures used for fruit image processing. Besides, Figure 8 shows the CNN model's distribution used for each case study. For cases of classification and quality control, we observe that the relationships between the \"own\" and \"pre-trained\" models are quite similar. However, in the case of fruit detection, the use of \"own\" models is reduced to less than 6%. According to the above, it is worth noticing that the number of layers depends on the kind of task to be performed. If the task is more complex and a greater number of characteristics must be extracted, then the number of layers and filters must be increased. From Figure 8a, we note that the use of \"own\" models is common in fruit classification tasks. In Table 1, it is noticeable that CNNs with less than 13 layers ([24; 25; 53; 58]) obtain excellent results of about 99%. We also show this behavior with the examples presented in Section 8.2.1. Moreover, it should be highlighted that CNN models reach higher improvement for fruit classification against other traditional methods (i.e., ML and computer vision). From the review and as seen in Table 1, the datasets contain RGB images without any general pre-processing. In some rare cases, the images were processed with different techniques before being used on CNN. The most common process was to resize the images. Furthermore, it is not necessary to highlight any particular characteristic because all these studies aim at the control, selection, and classification of fruits for the wholesale and retail markets, to develop practical applications that facilitate this process. In this same context, researchers commonly develop CNN models adapted to their own needs for fruit quality control, as shown in Figure 8b. From a practical point of view, the problem is to classify the images whether the fruit is sick or healthy, whether the fruit is damaged or not, among other similar decisions. Therefore, a complicated CNN architecture is not required to achieve the stated objectives. Generally, the studies implement a wide variety of CNNs models, varying the dimensions of the convolutional filters and pooling types to improve the results as much as possible. It should be noted that more complex approaches are required to classify particular defects of the fruits. Regarding that, we observe that since these studies require a great amount of information about the fruit, most of them use datasets composed of Hyperspectral images, Laser backscattering spectroscopic images, among other types.