Automatic Fruits Freshness Classification Using CNN and Transfer Learning
نویسندگان
چکیده
Fruit Freshness categorization is crucial in the agriculture industry. A system, which precisely assess fruits’ freshness, required to save labor costs related tossing out rotten fruits during manufacturing stage. subset of modern machine learning techniques, are known as Deep Convolution Neural Networks (DCNN), have been used classify images with success. There recently many changed CNN designs that gradually added more layers achieve better classification accuracy. This study proposes an efficient and accurate fruit freshness method. The proposed method has several interconnected steps. After data gathered, preprocessed using color uniforming, image resizing, augmentation, labelling. Later, AlexNet model loaded we use eight layers, including five convolutional three fully connected layers. Meanwhile, transfer fine tuning performed. In final stage, softmax classifier for classification. Detailed simulations performed on publicly available datasets. Our achieved highly favorable results all datasets 98.2%, 99.8%, 99.3%, accuracy aforesaid datasets, respectively. addition, our developed also computationally consumes 8 ms average yield result.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13148087