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- W4285193640 abstract "Identification and classification of fruits of different qualities are vital for fruit industries. Traditional techniques, such as visual inspection of fruits by handpicking, are time-consuming, tiresome, and error-prone. To automate the fruit inspection process and segregating them into different classes, computer vision and machine learning approaches has been applied and researched. For segregating fruits into different classes or qualities, Transfer Learning is one of the popular technique to build a fruit classifier. This paper evaluates the performance of VGG16, InceptionV3, Xception, ResNet152V2, and DenseNet by training and testing models using transfer learning. The experiment is conducted on two fruits datasets. Further, to improve the accuracy of fruit classification, the pre-trained model DenseNet is partially unfreezed and retrained. The results show that this model archives accuracy of 99.61% for fruit classification." @default.
- W4285193640 created "2022-07-14" @default.
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- W4285193640 date "2022-01-01" @default.
- W4285193640 modified "2023-09-27" @default.
- W4285193640 title "Fruit Classification Using Deep Convolutional Neural Network and Transfer Learning" @default.
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- W4285193640 doi "https://doi.org/10.1007/978-3-031-07012-9_26" @default.
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