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首页> 外文会议>East Indonesia Conference on Computer and Information Technology >Strawberry Ripeness Identification Using Feature Extraction of RGB and K-Nearest Neighbor
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Strawberry Ripeness Identification Using Feature Extraction of RGB and K-Nearest Neighbor

机译：使用RGB和K最近邻居特征提取的草莓成熟度识别

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Nowadays, Indonesia has not been playing an active role in fulfilling the demand for strawberries in foreign markets yet. One of the reasons is the low quality of fruit selection that still uses conventional methods. Therefore, a proper method to group strawberries automatically is considered necessary. This research aims to identify the ripeness of strawberries using RGB feature extraction and the K-Nearest Neighbor (k-NN) algorithm. The strawberry image data used in this study is divided into two, namely training data consisting of 30 images, and test data with 20 images which is classified into four categories, i.e., ripe, unripe, raw, and not strawberry. Based on the test results obtained, incorrect classification is discovered happened on the unripe strawberry images due to the tendency of the red or green dominantly but not uniformly distributed. However, the accuracy of the ripeness classification is 85% for value of k used is 7. Therefore, it can be concluded that the system is able to detect the image of the strawberry category as well as the non-strawberry category.
机译：如今，印度尼西亚尚未发挥积极作用，以满足外国市场对草莓的需求。其中一个原因是仍然使用常规方法的水果选择的低质量。因此，必须考虑对草莓组的适当方法被认为是必要的。该研究旨在使用RGB特征提取和K最近邻（K-NN）算法来识别草莓的成熟。本研究中使用的草莓图像数据被分为两个，即由30个图像组成的培训数据，以及使用20个图像的测试数据分为四个类别，即成熟，未成熟，未加工，而不是草莓。基于所获得的测试结果，由于红色或绿色的趋势，未经红色或绿色的趋势，发现了不正确的分类，而不是均匀分布。然而，MIDENSICS分类的准确性为85％，所用的k值为7.因此，可以得出结论，该系统能够检测草莓类别的图像以及非草莓类别。

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