# Date Fruit Classification Based on Surface Quality Using Convolutional Neural Network Models
## 2 Related Work  
Researchers in the field of date fruit production have sought to develop new systems to automate relevant processes. They have tried to replace manual methods with new methods that utilize technology. In our paper, we focus on one process--the classification process. This process is used in several stages and for many purposes. In the following, we present a summary of what other authors in the field have tried to classify date fruit by laying out the purposes of these systems and indicating which stage of date fruit production they have developed their classification systems for. Moreover, we summarize their development methods and techniques. [PERSON] et al. [6] proposed a system to solve one of the challenges facing the date industry, i.e., classification and sorting without any physical measurement. They proposed a classification system for date fruit to classify and sort four types of date fruit based on color, size, and texture. They trained six algorithms and compared their accuracy results and found that the Support Vector Machine algorithm achieved the best results in terms of accuracy with a small number of images in the training dataset. However, the system could be improved by obtaining more data, because the dataset only contained a few hundred images. [PERSON] [7] proposed a classification system for date fruit, and their system was built to automatically classify four types of date fruit based on shape, size, and texture. The system's method of extracting the pattern is to use a local binary pattern, the Weber local descriptor. Moreover, the Fisher discrimination ratio is utilized to reduce the dimensionality of the feature set. After extracting patterns from the date fruit type, they are classified by using the Support Vector Machine algorithm. The experiment results show that the system can classify two types of dates, Ajwah and Sukkary, correctly, but is confused between two types, Sagai and Sellaj. In their experiment, the total number of images in the dataset was 320. [PERSON] et al. [8] proposed a classification system for seven types of date fruit. The system extracts fifteen features, such as means and standard deviations of color, size, shape descriptors, and texture descriptors, by applying several image-processing algorithms, such as color threshold, color filtering, and region identification. The extracted features are used as classification factors for classification algorithms. To find the best classifier, several classification algorithms were tried: the nearest neighbor, artificial neural network, and linear discriminant analysis. The results show that artificial neural network achieved the highest accuracy. The system was trained on a dataset that contained only 140 images. [PERSON] et al. [9] developed a date fruit classification system to classify six types of date fruit to help people in Bangladesh and to inform consumers about the type of date fruit. Their system was developed to classify date fruit based on visual features. These features were identified using CNN models. The system was compared with several models--MobileNetv1 [10], MobileNetv2 [11], and Inception_Resnet [12]--to find which had the highest accuracy. The models were trained on a dataset that contained 2246 images. The comparison showed that MobileNet_V1 achieved the highest accuracy. [PERSON] et al. [13] developed an automatic system that helps the date fruit agricultural industry. The system was proposed to provide better quality, satisfying date fruit consumers, and also to reduce economic loss. In addition, the system has features that help to count and provide an appropriate food quantity. The proposed system has the ability to classify date fruit based on maturity status. Moreover, it can label and count the number of dates inside an image, and it can detect date fruit defects. The development method depends on the use of image-processing algorithms to extract features from the date size, color, and skin texture for the classification task.