# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 6 CNN-Based Approaches for Fruit Detection
The performance of the proposed approach, to guide the end-effector of a robotic arm to improve the view regarding occluded sweet peppers, showed that it is equivalent to the standard 3 DMTS baseline. The results concluded that the end-effector final position was within 11.4 mm of the baseline, and also a fruit size increasing in the image by a factor of 17.8 compared against to the baseline of 16.8 on average. [PERSON] et al. [92] introduced a sensing system composed of an RGB-D sensor (Microsoft Kinect V2) and a sensing algorithm based on CNN, to conduct automatic collision-free picking guava fruit. A dataset was acquired in an outdoor orchard. They used an FCNN model to segment guava fruits and branches. The authors modified the VGG-16 and GoogLeNet models to segment guava fruits and branches, then applied Euclidean clustering to obtain all individual fruits. They estimate the pose of the fruit relative to its mother branch. The results showed that the precision and recall regarding the guava fruit detection were 0.983 and 0.948, respectively, and the 3D pose error were 23.43%-14.18%, with an execution time of 0.565 s/fruit. [PERSON] et al. [97] developed a machine vision algorithm to detect and identify the maturity of the passion fruits, using RGB-D images from a Kinect sensor. Firstly, passion fruits were detected using faster R-CNN (VGG-16 model) by color and RGB-D. Then, the fruits were represented by the features of fruit maturity using the dense scale-invariant features transform (DSIFT) algorithm together with locality-constrained linear coding (LLC). The output of the above process was the input for a linear SVM classifier to identify the maturity of fruits. The method achieved 92.71% in detection accuracy and 91.52% in maturity classification accuracy. A CNN algorithm (with five convolutional and three fully connected layers) was used by [PERSON] et al. [93] for the recognition of the mature and immature stage of strawberry. Greenhouse images were taken under natural lighting conditions to evaluate the results of BBOL (bounding box overlap which measures localization accuracy) and AP (average precision). The developed deep learning model achieved BBOLs of 0.7394% and 0.7045% and AP of 88.03% and 77.21% for mature and immature classes respectively. [PERSON] and [PERSON] [94] presented a simulated deep CNN for automatic yield estimation based on robotic agriculture to help farmers in decisions for cultivation practices, plant disease prevention, and the size of the harvest labor force. They generated 24,000 synthetic tomato images ( images) to train the network and tested on real data. They used a modified Inception-ResNet architecture. Experimental results showed a 91% average test accuracy over real images and 93% over synthetic images, counting efficiently even in scenarios when fruits are under a shadow, occluded by foliage or branches, or if exist some degree of overlap between fruits. [PERSON] and [PERSON] [28; 95] presented a framework for apple and mangoes detection and counting, using orchards image data. They used a general-purpose image segmentation approach with two feature learning algorithms--CNN and multiscale multilayered perceptrons (MLP). Their approaches were designed to include contextual information about how the image data were captured. The robotic vehicle is composed of a Point Grey Ladybug3 Spherical Digital Video Camera, equipped with six 2 MP cameras, and oriented to capture a complete 360\({}^{\circ}\) panoramic view. They used the circular Hough transform (CHT) and watershed segmentation (WS) algorithms, to detect and count individual fruits, from the pixel-wise fruit segmentation. The CNN's results achieved a pixel-wise F1-score of 0.791. The results about count estimates, using CNN and WS, showed the best performance for this dataset, and also a squared correlation coefficient of \(r^{2}=0.826\). [PERSON] et al. [27] adopted deep learning to map from input images to total fruit counting.