###### Abstract
Convolutional neural networks (CNNs) are being widely used in computer vision scientists because it is a type of deep learning (a part of the machine learning framework) for processing structured arrays of data, such as images. They are able to properly recognize patterns in the input image such as circles, lines, gradients and even faces of higher lever structures with great success. Another advantage of this kind of CCN networks is that, unlike previous computer vision algorithms, they do not need pre-processing, segmentation and feature extraction and can work directly on an original image, but taking care that overfitting to the training set is not present after training, since this would greatly limit the generalization capability of the neural network to properly classify unseen input samples belonging to the test set [15]. The inputs of CNN structure are directly the input images of interest to be automatically analyzed. CNNs have 1D, 2D and 3D types, among others. In 1D-CNN, the network moves in one direction. They are commonly used on time series data analysis. In 2D-CNNs, the kernels move in two spatial independent directions, and they are often used in image labeling and image processing (computer vision). 3D-CNN has a kernel that moves in three independent spatial directions. Those are used in 3D (volumetric) images of interest, such as MRI and CT body scans in the field of medicine. [PERSON] [16] et al. studied on early detection of mango mechanical damage using machine learning and near infrared (NIR) hyperspectral images. The inner properties of healthy and damaged areas of mango were obtained in the range of 650-1100 nm and then further analyzed to select the most effective wavelengths for proper discrimination (classification). Results showed that the correct classification rate was 97.9% inside the three-day time gap after the damage was done. Following the study conducted by [17] on persimmon fruit to detect bruising using hyperspectral imaging in the range of 450-1040 nm, it is found that an algorithm based on principal component analysis (PCA) was able to identify healthy (90%) and damaged (90.8%) fruit at high correct classification rates. Also, they developed a model based on partial least squares discriminant analysis (PLS-DA) that was able to classify the fruits based the moment when bruising happened and thus the fruit classes corresponding to 0, 1, 2 and 3 days after bruising took place were discriminated with an overall mean accuracy of 99.4%. [PERSON] [18] et al. developed a pixel-based method to detect bruised area in apple fruit. Hyperspectral images of 60 apples were taken at time intervals of 0, 12, and 18 h after an artificial bruising process was conducted. After selecting the region of interest (ROI), different recognition models were developed. Results stated that the random forest (RF) model had the highest correct classification rate (99.9%). [PERSON] [19] et al. developed a bruise detection system for blueberry fruit using a near-infrared (NIR) hyperspectral (950-1650 nm) system. They captured images at 30 min, 2 h, 6 h, and 12 h intervals after mechanical impact on blueberries had happened. A least squares support vector machine (LS-SVM) based model was developed to compute the distribution of spatial bruising. The overall classification accuracy for 30 min, 2 h, 6 h, and 12 h gaps was 77.5%, 83.8%, 92.5% and 95.0%, respectively. [PERSON] [20] et al., captured hyperspectral images of peaches at 12, 24, 36, and 48 h after impact. To create a discrimination model, the spectrum and properties of the samples were extracted. The results showed that according to the PLS-DA algorithm, the correct classification rates were 96.67%, 96.67%, 93.33% and 83.33% for 12, 24, 36 and 48 h, respectively. [PERSON] et al. [21] proposed a classifier to separate healthy and bruised pear. A thermal imaging system at the ranges of 8-14 um was used to capture images. The thermal images were analyzed by the gray-level co-occurrence matrix.