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

被引:32
|
作者
Salim, Farsana [1 ]
Saeed, Faisal [1 ]
Basurra, Shadi [1 ]
Qasem, Sultan Noman [2 ]
Al-Hadhrami, Tawfik [3 ]
机构
[1] Birmingham City Univ, Sch Comp & Digital Technol, Dept Comp & Data Sci, DAAI Res Grp, Birmingham B4 7XG, W Midlands, England
[2] Imam Mohammad Ibn Saud Islamic Univ IMSIU, Coll Comp & Informat Sci, Comp Sci Dept, Riyadh 11432, Saudi Arabia
[3] Nottingham Trent Univ, Sch Sci & Technol, Nottingham NG11 8NS, England
关键词
DenseNet; fruit recognition; food security; MobileNetV3; pre-trained models; ResNet; Xception; VEGETABLE CLASSIFICATION;
D O I
10.3390/electronics12143132
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the dramatic increase of the global population and with food insecurity increasing, it has become a major concern for both individuals and governments to fulfill the need for foods such as vegetables and fruits. Moreover, the desire for the consumption of healthy food, including fruit, has increased the need for applications in the field of agriculture that help to achieve better methods for fruit sorting and fruit disease prediction and classification. Automated fruit recognition is a potential solution to reduce the time and labor required to identify different fruits in situations such as retail stores during checkout, fruit processing centers during sorting, and orchards during harvest. Automating these processes reduces the need for human intervention, making them cheaper, faster, and immune to human error and biases. Past research in the field has focused mainly on the size, shape, and color features of fruits or employed convolutional neural networks (CNNs) for their classification. This study investigates the effectiveness of pre-trained deep learning models for fruit classification using two distinct datasets: Fruits-360 and the Fruit Recognition dataset. Four pre-trained models, DenseNet-201, Xception, MobileNetV3-Small, and ResNet-50, were chosen for the experiments based on their architecture and features. The results show that all models achieved almost 99% accuracy or higher with Fruits-360. With the Fruit Recognition dataset, DenseNet-201 and Xception achieved accuracies of around 98%. The good results exhibited by DenseNet-201 and Xception on both the datasets are remarkable, with DenseNet-201 attaining accuracies of 99.87% and 98.94%, and Xception attaining 99.13% and 97.73% accuracy, respectively, on Fruits-360 and the Fruit Recognition dataset.
引用
收藏
页数:23
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