# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 5 CNN-Based Approaches for Fruit Quality Control Tasks
\begin{table}
\begin{tabular}{l l l l} \hline
**Fruit** & **Data Type** & **CNN Model** & **Performance Results** \\ \hline Apple [73] & Laser backscattering & Modified AlexNet & Defects identification-detection \\  & spectroscopic images & with 11-layers & accuracy 92.5\% \\ Lemons [74] & RGB images & Three CNN models & Defects detection \\  & & with 11-16-18 layers & accuracy 97.3\% \\ Grapevine [75] & Image capture & CNN model & Distribution of epicticular waxes \\  & with the LSL & & accuracy 97.3\% \\ Papaya [76] & RGB images & CNN model & Disease classification \\  & & & accuracy \(\sim\)92\% \\
10-class [77] & Quadtree segmentation & CNN model & Diseased region detection \\  & RGB images & & accuracy 93\% \\ Tomato [78] & RGB images & Inception-ResNet v2 & Classification of nutritional deficiencies \\  & & Autoencoder & Inception-ResNet v2 87.273\% \\  & & & Autoencoder 79.091\% \\  & & & Quality classification \\  & & & Baseline-CNN ss6.1\%\textasci{-73.33\%} \\  & & AlexNet, MobileNet, & AlexNet 96.48\%\textasci{-87.37\%} \\ Strawberry [79] & RGB images & GoogLeNet, VGGNet, & GoogLeNet 91.93\%\textasci{-85.26\%} \\  & & Xception and 2-layer CNN & VGGNet 96.49\%\textasci{-89.12\%} \\  & & & Xception 92.63\%\textasci{-87.72\%} \\  & & & MobileNet 83.51\%\textasci{-64.56\%} \\ Blueberry [80] & Hyperspectral & Internal damage detection \\  & transmittance data & ResNet and ResNeXt & accuracy and F1-score \\  & & & ResNet 0.8844/0.8784 \\  & & & esNeXt 0.8952/0.8905 \\ Banana [81] & RGB images & CNN model & Classification of ripening stages \\  & & & accuracy 95.6\% \\ Cucumber [82] & Hyperspectral imaging & Stacked Sparse Auto-Encoder & Defects detection \\  & & and CNN model & CNN-SSAE 91.1\% \\ Melon [83] & Infrared video & 5-layer CNN & Recognition of lesions on skin \\  & & LeNet-5 & accuracy 97.5\% and \\  & & B-LeNet-4 & recovery rate 98.5\% \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of state-of-the-art CNN-based approaches applied for fruit quality control tasks. showed accuracies up to 97.3%. Besides, the cuticle electrical impedance and its epicticular waxes (thickness indicator of the berry skin and its permeability) was correlated to the waxes proportion with \(r=0.76\). A CNN model for the identification of papaya disease is presented by [PERSON] et al. [76]. They collected diseased images using a digital camera under normal conditions of papaya farms. Some of the images were found from publicly available images on the Internet. The network can classify images into five main papaya diseases. The model achieved \(\sim\)92% of classification accuracy for new images, comparing their results against the use of Support Vector Machine. [PERSON] et al. [77] applied a 6-layer CNN model with the quadtree method, exploring to check homogeneity of the sub-tree image pixel of the quadtree. They aimed to detect the diseased region from the fruit to facilitate effective classification. Comparing the CNN results with SVM and kNN classifiers, CNN gives better results and accuracy of 93% after segmentation. The Autoencoder models and Inception-ResNet v2 were employed by [PERSON] et al. [78] to recognize, classify, and predict the nutritional deficiencies in plants of tomato. They used 571 images captured during the fruiting and leafing phases. Moreover, they applied a statistical structure called Ensemble Averaging with two aforementioned predictive models to improve the accuracy regarding the predictive validation. The predictive performance of the three models had accuracy rates of 79.09% and 87.27% for Autoencoder and Inception-ResNet v2, respectively, and 91% validity using Ensemble Averaging. [PERSON] et al. [79] evaluated the AlexNet, MobileNet, GoogLeNet, VGGNet, and Xception architectures against a 2-layer CNN architecture used as a baseline. They used a strawberry classification system for quality inspection, evaluating with two sets of data, two strawberry classes and other four classes.