# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 4 CNN-Based Approaches for Fruit Classification Tasks
The fruit dataset contained 3600 images of 18 types of fruits, which were collected on the site with a digital camera and also with downloaded images from the Internet. In a pre-processing procedure, the fruit was moved to the center of the image, then it is trimmed and resized. Then, the background is removed and finally, each image is labeled. The 8-layer CNN overall accuracy was of 95.67%, better than 6-layer CNN [58] with 94.94%, HWE + GA [68] with 81.11%, HIGA [67] with 89.06%, BBO [31] with 87.94%, SANN [69] with 88.22%, and IABC [30] with 89.11%. [PERSON] et al. [26] used a modified GoogLeNet [50] for fruit and vegetable classification. The dataset consisted of 2700 images from a total of 13 kinds of fruits and vegetables taken with a 16-band hyperspectral camera, covering the visible range of 470-630 nm. The images were reorganized into 3D matrices with 2D cuts for each spectral bandThree models of network architectures were used to adjust the intermediate NN model: Pseudo-RGB, linear combination, and convolutional kernel. The CNN average accuracy was 88.15% with Pseudo-RGB images, 85.93% with linear combinations, and 92.23% with convolutional kernels. [PERSON] and [PERSON] [53] evaluated two 9-layer CNNs with the same architecture, aiming to carry out the fruit classification for application in retail. Both networks had different weights, the first one classifies fruits images with background, and the second used images containing a single fruit. The number of original images was 6161, which were segmented by using a recognition algorithm to identify a single apple in the original image. Each apple object was saved as a separate image, and all apples in each original image were identified and recorded, producing a dataset comprised of 23,662 images from six apple varieties. The evaluation results on this dataset showed an overall accuracy of 99.78%. [PERSON] and [PERSON] [55] introduce a new dataset of fruit images called Fruits-360 [70]. They also present the results of the evaluation of a basic CNN model, as well as, AlexNet and GoogLeNet models for fruit recognition. They conclude the CNN-based techniques achieve very good accuracy results and basic CNN is the more efficient in terms of processing time. [PERSON] et al. [56] adopted an AlexNet network model for vegetable image classification by using Caffe framework [52]. The data set was obtained from ImageNet. The authors train their CNN model on different datasets by varying the number of vegetable images. The classification results showed that accuracy decreases as the number of images decreases. Besides, they compared the accuracy rate of the CNN-based method (92.1%) against BP neural network (78%) and SVM classifier (80.5%). [PERSON] et al. [54] designed and evaluated several CNN architectures for fruit classification using the Fruits-360 dataset [70]. They used various combinations of hidden layers and epochs for different cases and made a comparison between them. The initial CNN model is comprised of two convolutional layers, each one followed by a pooling layer, and two FC layers. The input RGB images have a size of \(100\times 100\) pixels. They achieved an accuracy of 100% and a training accuracy of 99.79%. [PERSON] et al. [57] proposed a fruit recognition algorithm based on Deep Convolution Neural Network (DCNN). They used a fruit image database with 15 different categories comprising of 44,406 images. The network has three convolutional-pooling layers, one FC layer, and finally a dense output layer. The input shape is \(150\times 150\times 3\). The experimental results of the proposed approach showed a high accuracy of 99%. [PERSON] et al. [58] designed a 6-layer CNN for fruit classification. The fruit dataset contained 1800 images from 9 types of fruits, which were obtained using a digital camera. They compared the proposed CNN model against voting-based-SVM (VB-SVM), wavelet entropy (WE), and genetic algorithm (GA). The accuracy results were 86.56% for VB-SVM, 89.78% for WE, 82.33% for GA, and 91.44% for 6-layer CNN. [PERSON] et al.