# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 4 CNN-Based Approaches for Fruit Classification Tasks  
We present a summary of the most recent articles that CNNs are applied for accurate classification of fruits according to their specific type. Table 1 shows a summary of these articles. In this case, classification is understood as the fact of identifying the specific type of fruit observed in an image containing a single type or several types of fruits. [PERSON] [24] applied CNNs with data expansion techniques to select a total of 5822 color images in ten-class food items from the ImageNet [64], comparing its method against bag-of-feature (BoF) and support vector machine (SVM) models. BoF with SVM showed accuracy results of 56%, \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Data Type** & **CNN Model** & **Performance Results** \\ \hline ImageNet [24] & RGB Images & \multirow{2}{*}{5-layer CNN model} & 74\% without data augmentation \\  & \(128\times 128\times 3\) & & 90\% with data augmentation \\  & & & \\  & RGB Images & \multirow{2}{*}{13-layer CNN model} & \multirow{2}{*}{Accuracy 94.94\%,} \\  & \(256\times 256\times 3\) & & 88.15\% with Pseudo-RGB images \\ Own [26] & Hyperspectral images & \multirow{2}{*}{Modified GoogLeNet} & 85.93\% with linear combinations \\  & \(256\times 256\times 3\) & & 92.23\% with convolutional kernels \\ Own [53] & RGB Images & \multirow{2}{*}{9-layer CNN model} & \multirow{2}{*}{Accuracy 99.78\%.} \\  & \(150\times 150\times 3\) & & \\  & RGB Images & \multirow{2}{*}{Proposed CNN models} & \multirow{2}{*}{Accuracy 100\%} \\  & \(100\times 100\times 3\) & & \\  & RGB Images & \multirow{2}{*}{Proposed CNN models} & \multirow{2}{*}{Training accuracy 99.79\%} \\  & \(100\times 100\times 3\) & & \\  & RGB Images & AlexNet, GoogLeNet & Accuracy \(\sim\)99\% \\  & \(100\times 100\times 3\) & & \\  & RGB Images & \multirow{2}{*}{AlexNet model} & \multirow{2}{*}{Accuracy 92.1\%} \\  & \(224\times 224\times 3\) & & \\ Own [57] & RGB Images & \multirow{2}{*}{Proposed CNN models} & \multirow{2}{*}{Accuracy 99\%.} \\  & \(150\times 150\times 3\) & & \\ Own [58] & RGB Images & \multirow{2}{*}{6-layer CNN model} & \multirow{2}{*}{Accuracy 91.44\%} \\  & \(256\times 256\times 3\) & & \\ Supermarket Data [59] & RGB Images & \multirow{2}{*}{Fruit-AlexNet} & \multirow{2}{*}{Accuracy of 99.56\%} \\  & \(48\times 64\times 3\) & & \\  & RGB Images & \multirow{2}{*}{8-layer CNN model} & \multirow{2}{*}{Accuracy 95.67\%} \\  & \(150\times 150\times 3\) & & \\ Own [61] & RGB-image Saliency & \multirow{2}{*}{Modified VGG} & \multirow{2}{*}{Accuracy 95.6\%} \\  & \(224\times 224\times 3\) & & \\  & RGB Images & \multirow{2}{*}{CBP-CNN, VGGNet} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & \(\mathrm{N/A}\) & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \end{tabular}
\end{table} 
Table 1: Summary of state-of-the-art CNN-based approaches applied for fruit classification tasks. while the CNN model performed an accuracy of 74% and 90% without and with data augmentation techniques, respectively. The study of fruit classification performed by [PERSON] et al. [25] compared the effect of three types of data augmentation method and max-pooling techniques on the accuracy, as well as using GPU and CPU hardware platforms. They used the same dataset and image pre-processing procedure done by [PERSON] and [PERSON] [60]. They obtained an accuracy of 94.94%, which is higher than the other methods of machine learning that they applied, that yielded accuracies for the PCA + kSVM [65] of 88.20%, the PCA + FSCABC [30] of 89.11%, WE + BBO [32] of 89.47%, FKFE + BPNN [66] of 88.99%, and FRFE + IHGA [67] of 89.59%. The max-pooling performed slightly better than average-pooling. This work is derived from the article by [PERSON] and [PERSON] [60], where they used the same dataset and preprocessing procedure. [PERSON] and [PERSON] [60] created an 8-layer CNN by using a parametric ReLU and placing a dropout layer before each FC layer.