# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 6 CNN-Based Approaches for Fruit Detection
The data were 2000 JPEG images of 2352 \(\times\) 1568 pixels, obtained from several strawberry orchards with a portable digital camera under different conditions. The results for fruit detection over 100 test images showed that the detection accuracy rate was 95.78% on average, the recovery rate was 95.41%, and the results of prediction over 573 ripe fruit collection points with an error average was \(\pm\)1.2 mm. In order to detect individual fruits and also to obtain pixel-wise mask for each detected fruit in an image, [PERSON] et al. [87] presented a deep learning approach, named Deep Orange, based on a segmentation framework with the implementation of Mask R-CNN by using ResNet-101. They use multi-modal input data comprising of HSV and RGB images retrieved from an orange grove in Citra, Florida, under natural lighting conditions. The algorithm performance is tested using RGB and RGB + HSV images. Their preliminary results showed that the inclusion of HSV data improves the precision from 0.8 to 0.9753. In 2019, [PERSON] et al. [88] applied the RGB-D sensors with fused aligned RGB and Near-Infrared (NIR) images in a deep CNN for fruit detection, aiming to develop a fruit detection system to estimate the yield of the fruit and the automatic harvest. They adopted a modified VGG-16 for the task of kiwifruit detection using images obtained from two modalities: NIR and RGB images using the Kinect v2 device. The modified VGG-16 was compared with the original VGG-16. They used two fusion methods to extract features: fusion of the NIR and RGB images on the input layer (Image-Fusion) and fusion of feature maps of two VGG-16 networks, where the NIR and RGB images were input (Feature-Fusion). The results showed that the precision of the original VGG-16 with NIR and RGB image was 89.2% and 88.4% on average, respectively. Besides, the 6-channel VGG-16 using the Feature-Fusion method achieved 0.5%, and using the Image-Fusion method achieved the highest average precision of 90.7% and the fastest speed of detection with 0.134 s/image. [PERSON] et al. [89] carried out a study of instance segmentation and strawberries localization in farm conditions for automatic harvesting based on a deep CNN (Mask R-CNN). They used four classes of strawberry conditions, three ripeness levels and one of shape. The image dataset contained 310 images, where two-thirds were captured by an iPhone 6s camera during the harvesting season, and the remaining were captured using a RGB-D camera (D415 and D435 by Intel). The results showed that ripe strawberries are the easiest to be identified. They proposed a bounding box refinement method to improve the localization accuracy by detecting occluded fruits. The experimental comparison showed that an overlap between the refined and ground truth was 0.87, and between raw detected bounding box and ground truth was 0.68. In 2019, based on the fact that there is no research in computer vision for date fruits detection in an orchard environment, [PERSON] et al. [90] proposed a machine vision framework for date fruit harvesting robots. Their approach consisted of three classification models for date fruit image classification in real-time according to their maturity, type and the harvesting decision. The dataset contained 8072 images of five date types, in different maturity and pre-maturity stages from more than 350 date bunches which belong to 29 date palms in an orchard. Three CNN models were used for classification: AlexNet, VGG-16, and a modified VGG-16. The classification models achieved accuracies of 99.01%, 97.25%, and 98.59% with classification times of 20.7, 20.6 and 35.9 ms for the maturity, type and harvesting decision classification tasks, respectively. [PERSON] and [PERSON] [91] proposed a multi-perspective (multi-camera) visual serving method for unstructured and occluded environments, called 3D Move To See (3 DMTS), for robotic crop harvesting environments. They created a Deep-3 DMTS model, which is comprised of 3 DMTS with a CNN.