ArticlePloS one2022
Fruit classification using attention-based MobileNetV2 for industrial applications.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- AMCD: A multi-domain agricultural crop and flower image dataset for deep learning-based classification.Data in brief · 2026Article
- AgroDualNet: a dual deep learning-based crop disease forecasting and fruit ripening detection.Scientific reports · 2026Article
- Physiological and image-based characterization ofFrontiers in plant science · 2026Article
- An Efficient Fine-Grained Recognition Method Enhanced by Res2Net Based on Dynamic Sparse Attention.Sensors (Basel, Switzerland) · 2025Article
- A Smartphone-Based Non-Destructive Multimodal Deep Learning Approach Using pH-Sensitive Pitaya Peel Films for Real-Time Fish Freshness Detection.Foods (Basel, Switzerland) · 2025Article
- High precision banana variety identification using vision transformer based feature extraction and support vector machine.Scientific reports · 2025Article
- BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8.Scientific reports · 2025Article
- Towards sustainable solutions: Effective waste classification framework via enhanced deep convolutional neural networks.PloS one · 2025Article
- Article
- Apple varieties, diseases, and distinguishing between fresh and rotten through deep learning approaches.PloS one · 2025Article
- EA-CNN: Enhanced attention-CNN with explainable AI for fruit and vegetable classification.Heliyon · 2024Article
- Article
- Non-Destructive Monitoring of External Quality of Date Palm Fruit (Sensors (Basel, Switzerland) · 2024Article
- Prediction of lumpy skin disease virus using customized CBAM-DenseNet-attention model.BMC infectious diseases · 2024Article
- Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification.PloS one · 2024Article
- Recognition of Wheat Leaf Diseases Using Lightweight Convolutional Neural Networks against Complex Backgrounds.Life (Basel, Switzerland) · 2023Article
- BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model.Journal of imaging · 2023Article
- Understanding the black-box: towards interpretable and reliable deep learning models.PeerJ. Computer science · 2023Article
- Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI.Computers in biology and medicine · 2022Article
- Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.Journal of medical systems · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer. Evaluation of our proposed method, which leverages transfer learning approach, on three public fruit-related benchmark datasets shows that our proposed method outperforms the four latest deep learning methods with a smaller number of trainable parameters and a superior classification accuracy. Our model has a great potential to be adopted by industries closely related to the fruit growing and retailing or processing chain for automatic fruit identification and classifications in the future.
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