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About Revue D'intelligence Artificielle: Received: 5 September 2021 Accepted: 13 October 2021
This document summarizes a research paper that proposes a hybrid deep learning model for classifying different types of mangosteen fruits. The model uses a convolutional neural network (CNN) architecture with convolutional, pooling, and fully connected layers. The CNN model is compared to other models like Xception, VGG16, and ResNet50 using different optimizers. The proposed CNN model achieves 94.99% accuracy on a dataset of four types of Malaysian mangosteen fruits, outperforming the next best Xception model which achieves 90.62% accuracy. The goal is to develop a machine learning system for precision agricultural applications to classify mangosteen fruits, as an alternative to traditional manual classification methods.
Detailed Information
| Author: | ['Abdelaali Bakhouche'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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