###### Abstract
[8] evaluated an experimental system for detecting the surface damages of apples based on the rotation of apples in front of the camera, and the camera took several images during the rotation. The camera included optical filters. The dark areas that were in a fixed position relative to the apples during the rotation indicated the peduncles. In contrast, other dark areas whose shape or position was changed from a frame to frame were considered as damages. Although the identification was relatively successful (90%), a false negative occurred where some healthy apples were graded as defective. They stated that the problem of identifying the peduncle has still not been adequately resolved and suggested that additional cameras were needed in conjunction with the peduncle inspection. [PERSON] et al. [9] found that using high-power light-emitting diodes (LEDs) in the applications related to detecting apple defects showed better results than other light sources. [PERSON] et al. [10] designed and evaluated a system for exploring the apple surface damages based on computer vision. This system has a conveyor for crop transport and positions the apple under the camera to obtain identical fruit images. The conveyor positioned the apples so that the peduncle and adjacent areas would not come into the camera line of sight. An expert system can be developed to perform quality control, measurement, and online defect detection using the computational intelligence algorithms, along with the image processing systems [6] in various industries, e.g., the design of aerial and ground moving object tracking systems. [PERSON] et al. [3] proposed an anthracose lesion detection method based on deep learning. Plant surface lesion images collected by an optical sensor and Cycle-Consistent Adversarial Network (CycleGAN) deep learning model were used to accomplish data augmentation. The proposed model outperformed three other state-of-the-art models in detection accuracy. [PERSON] [11] studied the color and classification of apples based on neural network methods. One of the neural network methods, which examined the pixels and helped to distinguish between the healthy and the infected and diseased apples, divided the pixels into six groups. The accuracy of this method was about 86%, but this method was unable to detect apple defects. However, when the model considered all the variables, the accuracy was sharply reduced. [PERSON] and [PERSON] [12] graded two Emperor and Golden Delicious apple varieties based on the surface qualitative conditions using the back-propagation neural network. The values of the pixels with a gray area and the texture characteristics obtained from the initial image were considered as the network input. Two categorization methods were implemented: the categorization method with two defective and healthy groups and the categorization method with five groups, including all defective and healthy cases. The network grading accuracy in the first case ranged from 89.2 to 100%, while in the second case, the grading accuracy in the Emperor variety was 93.8-100%. [PERSON] et al. [13] used neural networks to identify the apple pests. They stated that the usual methods of identifying apple pests are based on the visual observations of the inspector. The criteria for detecting the damage and pest of an apple tree are based on the color, shape, etc. This identification method requires considerable expertise in this area and a good understanding of different species and is very time-consuming. In recent years, numerous studies have been conducted on diagnosing pests and diseases of fruits and vegetables using advanced algorithms. These advanced algorithms have their advantages and disadvantages. [PERSON] et al. [14] (2018) proposed a new deep convolutional neural network model for the accurate detection of apple leaf diseases. The results were satisfactory, and the proposed model can achieve a detection accuracy of 97.62%. [PERSON] et al.