# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 6 CNN-Based Approaches for Fruit Detection  
Another process to consider in the study of fruits is fruit detection for automatic harvesting and counting when they are in the greenhouses or orchards because it is a determining component for crop automation in agriculture. In this section, we present a review of automatic fruit detection by using convolutional neural networks. Table 3 summarizes of recent articles based on CNN for fruit detection. An example of automatic fruit detection is the work presented by [PERSON] et al. [84], where they show a multi-arm kiwi pickup robot, presenting the design and evaluation of the robot's performance. The authors aim to operate autonomously in pergola-style orchards. Their work is based on a modified CNN architecture of the VGG-16 called FCN-85 [39]. To detect the kiwi fruit in the canopy, each robotic \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Fruit** & **Data** & **CNN Model** & **Performance Results** \\ \hline Kiwi [84] & RGB images & \begin{tabular}{c} modified VGG-16 \\ called FCN-88 \\ \end{tabular} & Harvesting 51\% \\ Wine grapes [85] & RGB images & modified ResNet & \begin{tabular}{c} Segmentation \\ F1-Score 0.91 \\ \end{tabular} \\ Strawberry [86] & RGB images & Resnet-50 & \begin{tabular}{c} Detection 95.78\% \\ Recuperation 95.41\% \\ \end{tabular} \\ Orange [87] & RGB images & ResNet-101 & Detection 97.53\% \\ Kiwi [88] & RGB-D and NIR & VGG-16 & Detection 90.7\% \\ Strawberry [89] & RGB-D images & ResNet modified & Detection 94\% \\ Date Fruit [90] & RGB images & AlexNet and VGG-16 & 99.01–97.01\%–98.59\% \\ Sweet Peppers [91] & RGB-D images & ResNet Modified & \begin{tabular}{c} Training Loss 0.552 \\ Validation Loss 1.896 \\ \end{tabular} \\ Guava [92] & RGB-D images & \begin{tabular}{c} VGG-16 and \\ modified GoogLeNet \\ \end{tabular} & \begin{tabular}{c} Detection \\ 98.3\%–94.8\% \\ \end{tabular} \\ Passion Fruit & RGB-D images & VGG-16 model 5 & Detection 91.52\% \\ Strawberry [93] & RGB images & CNN model & Detection \\ Tomato [94] & Synthetic images & \begin{tabular}{c} Inception-ResNet \\ modified \\ \end{tabular} & \begin{tabular}{c} Detection \\ 91\%–93\% \\ \end{tabular} \\ Apple and Mangoes [28,95] & RGB images & VGG-16 & \begin{tabular}{c} Detection \\ F1-Score 0.791 \\ \end{tabular} \\ Apple and Orange [27] & RGB images & Two CNN model & \begin{tabular}{c} Segmentation \\ Oranges 0.813 \\ Apples 0.838 \\ \end{tabular} \\ Sweet Pepper [36] & RGB and NIR & \
\begin{tabular}{c} modified VGG-16 \\ images \\ modified VGG-16 \\ \end{tabular} &
\begin{tabular}{c} Detection \\ F1-Score 0.838 \\ Segmentation error \\ 1.36\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of state-of-the-art Convolutional Neural Network (CNN)-based approaches applied to the detection of fruits for automatic harvest. arm has a pair of centered color cameras. When testing the harvesting robot in commercial orchards, the results show a successful harvest of 51% of the total kiwi, with an average time of 5.5 s/fruit. [PERSON] et al. [85] showed that grape clusters can be successfully recognized, segmented and tracked using CNNs. They adopted a similar procedure to that proposed by [PERSON] et al. [86] based on ResNet, where the Mask R-CNN and a feature pyramid network (FPN) are the feature extractor. The evaluation was on the Embrapa Wine Grape Instance Segmentation Dataset (WGISD) dataset, which is composed of 300 RGB images which show 4432 grape clusters from five different varieties of grape. The results reached an F1-score up to 0.91 for segmentation, and also an appropriate separation of each cluster from other structures in the image, which allowed a more accurate assessment of fruit size and shape. [PERSON] at al. [86] applied the convolutional Neural Networks with the implementation of Mask R-CNN, aiming to improve the performance of computer vision in the detection of fruits for robotic strawberry harvesting. Their work is based on the Resnet-50 model combined with the Feature Pyramid Network (FPN) architecture.