Classification of Date Fruit Types Using CNN Algorithm Based on Type

M. Fajrun Nadhif, Saruni Dwiasnati
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Abstract

Date fruits are an important commodity in the agriculture and food industry. However, in the process of sales and distribution to ordinary people, there are often errors in identifying different types of date fruits. Therefore, this research aims to develop an automatic classification system to distinguish the types of date fruits based on their types using the Convolutional Neural Network (CNN) algorithm. The case study was conducted at Hamima Dates date shop. The data used are fruit images with 9 categories and a total of 1658 samples, which are divided into 1496 samples for training data and 162 samples for testing data. The test results show that the CNN algorithm has a high level of accuracy in classifying the type of date fruit, with an accuracy of 96%. In this study, feature analysis was also conducted to determine the contribution of each feature to the classification of date fruit types. The results of this study can be the basis for the development of a more sophisticated date fruit automatic classification system and can be applied to other types of fruits
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基于类型的CNN算法的枣果类型分类
枣子是农业和食品工业中的重要商品。然而,在向普通人销售和分销的过程中,往往会出现识别不同类型枣果的错误。因此,本研究旨在利用卷积神经网络(Convolutional Neural Network, CNN)算法开发一种基于枣果类型的自动分类系统。案例研究在Hamima Dates枣店进行。使用的数据为9类水果图像,共1658个样本,其中训练数据为1496个样本,测试数据为162个样本。测试结果表明,CNN算法在枣果类型分类方面具有较高的准确率,准确率达到96%。在本研究中,还进行了特征分析,以确定每个特征对枣果类型分类的贡献。本研究结果可作为开发更完善的枣果自动分类系统的基础,并可应用于其他类型的水果
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