# Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks
## 2 Related Works  
### Scientific Publications  
There are numerous previous studies using CNN-based DL models and algorithms such as object detection, semantic segmentation, and instance segmentation applied to different areas of the agricultural process, harvesting, disease control, and quality control, among other applications [10; 11]. Some of the recent scientific publications applying object-detection models in agribusiness are reviewed below. In [12] the authors propose to produce a real-time pear counter system for mobile applications using RGB video and the variants of the YOLOv4 object detector model and the multiple object-tracking algorithm Deep SORT, obtaining an accuracy of 98% with a [EMAIL_ADDRESS] with YOLOv4-CSP. They obtained better computational cost and speed with YOLOv4-tiny, concluding that the best model is the YOLOv4 in accuracy and speed. In [13], the authors properly evaluate various detector models based on the CNN, such as YOLOv4, region-based fully convolutional network (R-FCN) and Faster R-CNN, in order to detect visible mechanical damage in sugar beet during harvesting in a harvester machine from RGB videos images. The better experimental results showed a recall, precision and F1-score of about 92, 94 and 93% respectively, and higher speed of around 29 frames per second. In [14], the authors propose an improved object-detection algorithm based on YOLOv3 for early real-time detection of tomato diseases and pests from video images. They used a dilated convolution layer to replace the convolution layer in the backbone network. The results obtained were an F1 value of 94.77%, an AP value of 91.81%, a false detection rate of only 2.1%, and a detection time of 55 ms. In [15], in order to automatically recognize the graspable and ungraspable apples in an apple tree image, an apple target detection method was proposed for a picking robot using improved YOLOv5s and compared with original YOLOv5s, YOLOv3, YOLOv4, and EfficientDet-D0 model. The experimental results indicated that the graspable apples and the ungraspable apples could be identified effectively using the proposed improved network model, with an average recognition time of 0.015 s per image. In [16], a synthetic image dataset of soybean leaf disease synthetic images is developed to first address the problem of an insufficient dataset. The study designs a fusion of multiple Faster R-CNN (MF3 R-CNN) functions. The results show average mean accuracy of 83.34% on the actual test dataset. In [17] the improved Faster R-CNN algorithm with the ResNet-50 network is proposed for the detection of maturity stages for coconuts. The detection performance achieved using the improved Faster R-CNN model was better than that single-shot detector (SSD), YOLO-V3 and Region-based Fully Convolutional Networks (R-FCN). In [18], in order to detect tomatoes in environmental conditions, the authors use a modified YOLOv3 model called YOLO-Tomato to be used in a harvesting robot. Results show YOLO-Tomato-A at AP 98.3%, YOLO-Tomato-B at AP 99.3%, and YOLO-Tomato-C at AP 99.5% with detection times 48 ms, 44 ms and 52 ms, respectively. In [19], the authors apply YOLO v2 model (with different optimization algorithm and backbones), YOLOv3 model and Kalman filter, in order to automatically detect pear and apple in videos, managing to automatically count pears and apples in the videos, with an absolute error of 10%. A YOLO v2 network with a larger input image size and data augmentation method contributed to average precision of 0.97 in the pear and apple detection. In [20], the authors apply instance segmentation on strawberry crops based on the use of a fully convolutional neural network, adding two new channels to the network output so that each strawberry pixel predicts the centroid of its strawberry comparing the results with the Mask R-CNN algorithm, in order to improve the development of automatic harvesting machines. The results show mAP and mean instance intersection over a union of 52.61 and 93.38, respectively, with a processing speed of 30 fps.