# Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks
## 2 Related Works
### Revision of Patents  
In addition to the aforementioned items, there are several intellectual property solutions (patents) that attempt to improve the fruit quality estimation stage. In particular, the following patents can be mentioned:  
WO2012039597 [29] relates to an oil palm fruit ripeness grading system using color detection by computer vision. US6610953B1 [30] discloses a method and apparatus incorporating NIR and visible cameras for simultaneously capturing images of apples passing through a packing line to be sorted according to their defects. CN10112533A [31] describes a method for sorting fruit according to the colors presented. CN102928357B [32] presents a technical solution of a non-destructive in-line detection system, from near-infrared and visible light, to establish fruit quality. The solution comprises a mechanical transport device, an illumination device and a detection device. CN205484102U [33] proposes a method using computer vision processing but focuses specifically on finding scars on the fruit and reducing these elements in the packing line.  
Another solution is proposed by CN106568784A [34], which discloses a multispectral imaging system for in-line detection of surface defects of fruit and vegetables. On the other hand, CN101832941B [35] describes a quality assessment device based on multispectral images, which tries to detect a certain content of a component inside some fruit. The idea  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Article** & **Data Type** & **Model** \\ \hline [PERSON] et al. [12] & RGB video & YOLOv4, YOLOv4-CSP, YOLOv4-tiny. \\ [PERSON] et al. [13] & RGB video & YOLO v4, R-FCN and Faster R–CNN \\ [PERSON] et al. [14] & RGB video & YOLOv3 \\ [PERSON] et al. [15] & RGB video & YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 \\ [PERSON] et al. [16] & RGB image & Faster R–CNN \\ [PERSON] et al. [17] & RGB image & Faster R-CNN, SSD, YOLOv3, R–FCN \\ [PERSON] et al. [18] & RGB images & YOLOv3 modified: YOLO-Tomato-A, \\ [PERSON] et al. [19] & RGB video & YOLOv2 \\ [PERSON] et.al. [20] & RGB video & Mask R–CNN \\ [PERSON] et al. [21] & RGB images & Faster R–CNN \\ [PERSON] et al. [22] & RGB images & Mask R–CNN, YOLOv2 and YOLOv3 \\ [PERSON] et al. [23] & RGB and & Mask R–CNN \\ [PERSON] et al. [24] & RGB video & RetinaNet (ResNet-18 feature extractor) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the reviewed studies.  
is to perform an inspection that allows automatic sorting and packing. CN107833223A [36] describes an image-processing method, and specifically a method of segmenting hyperspectral images of fruit based on spectral information. Developed primarily to assist post-harvest processes and promote the development of post-harvest fruit treatments and address quality, standardization and industrialization.  
The solutions described above are related to the dynamic stage of the fruit, i.e., they present control systems or elements associated with the stage of fruit movement in some transport lines. However, there are no solutions associated with static fruit control, which corresponds to the stage after harvesting and before the fruit packing line, specifically in the quality control of fruit arranged in boxes or harvesting trays.