DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition

Farsana Salim; Faisal Saeed; Shadi Basurra; Sultan Noman Qasem; Tawfik Al-Hadhrami

doi:10.3390/electronics12143132

DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition

Farsana Salim, Faisal Saeed^*, Shadi Basurra, Sultan Noman Qasem, Tawfik Al-Hadhrami

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

47 Citations (SciVal)

Original language	English
Article number	3132
Journal	Electronics (Switzerland)
Volume	12
Issue number	14
DOIs	https://doi.org/10.3390/electronics12143132
Publication status	Published (VoR) - Jul 2023

Funding

The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) for funding and supporting this work through Research Partnership Program no. RP-21-07-09.

Funders	Funder number
Al-Imam Muhammad Ibn Saud Islamic University	RP-21-07-09
Deanship of Scientific Research, Imam Mohammed Ibn Saud Islamic University

Keywords

DenseNet
food security
fruit recognition
MobileNetV3
pre-trained models
ResNet
Xception

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10.3390/electronics12143132

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title = "DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition",

keywords = "DenseNet, food security, fruit recognition, MobileNetV3, pre-trained models, ResNet, Xception",

author = "Farsana Salim and Faisal Saeed and Shadi Basurra and Qasem, {Sultan Noman} and Tawfik Al-Hadhrami",

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AU - Saeed, Faisal

AU - Basurra, Shadi

AU - Qasem, Sultan Noman

AU - Al-Hadhrami, Tawfik

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DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition

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Keywords

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