2026-04-30T21:23:15Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/00007366 2022-03-24T23:15:25Z 1582963674932 1582963674932:1597397050488 user-miit

Fruit Recognition Using Color and Morphological Feature Fusion Myint San Mie Mie Aung Phyu Phyu Khaing t is still difficult to recognize the kind of fruit which are of different colors, shapes, and textures. This paper proposes a features fusion method to recognize five different classes of fruits that are the images from the fruit360 dataset. We are processed with four stages: preprocessing, boundary extraction, feature extractions, and classification. Pre-processing is performed to remove the noise by using the median filter, and boundary extraction are operated with the morphological operation. In feature extraction, we have extracted two types of features: color, and morphological features of the image. Color features are extracted from the RGB color channel, and morphological features are extracted from the image that detected the boundary of fruit by using morphological operations. These two types of features are combined in a single feature descriptor. These features are passed to five different classifiers: Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF). In the study, the accuracy that classified with Random Forest (RF) classifier for the proposed feature fusion method is better than the other classifiers, such as Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN). 2019-10-11 http://hdl.handle.net/20.500.12678/0000007366 https://meral.edu.mm/records/7366