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Fruit and vegetables classification system using image saliency and convolutional neural network

https://doi.org/10.1109/itoec.2017.8122370

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10599-4_29openalex-snapshot

Food-101 – Mining Discriminative Components with Random Forests

inferredcites →https://doi.org/10.1007/s00371-013-0867-4openalex-snapshot

SalientShape: group saliency in image collections

inferredcites →https://doi.org/10.1016/j.jfoodeng.2014.07.001openalex-snapshot

Fruit classification using computer vision and feedforward neural network

inferredcites →https://doi.org/10.1109/34.730558openalex-snapshot

A model of saliency-based visual attention for rapid scene analysis

inferredcites →https://doi.org/10.1109/cvpr.2007.383267openalex-snapshot

Saliency Detection: A Spectral Residual Approach

inferredcites →https://doi.org/10.1109/cvpr.2008.4587715openalex-snapshot

Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform

inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot

Going deeper with convolutions

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-snapshot

Feature Pyramid Networks for Object Detection

inferredcites →https://doi.org/10.1109/iceei.2009.5254804openalex-snapshot

A new method for fruits recognition system

inferredcites →https://doi.org/10.1145/2986035.2986039openalex-snapshot

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.3390/s120912489openalex-snapshot

Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.5120/1756-2395openalex-snapshot

Fruit Detection using Improved Multiple Features based Algorithm

inferredcites →https://doi.org/10.7551/mitpress/7503.003.0073openalex-snapshot

Graph-Based Visual Saliency

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Investigating the Freshness of Selected Fruits with Deep Learning Method

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Fruit Classification Using Traditional Machine Learning and Deep Learning Approach

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A Survey of Deep Learning Methods for Fruit and Vegetable Detection and Yield Estimation

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104 Fruits Classification Using Transfer Learning and DenseNet201 Fine-Tuning

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Plant Disease Categorization Through Hybrid Machine Learning Methods of Deep Learning

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Hyperspectral Fruit and Vegetable Classification Using Convolutional Neural Networks with EfficientNetB3

inferred← citeshttps://doi.org/10.1007/978-981-13-9042-5_10openalex-snapshot

A Robust Multi-label Fruit Classification Based on Deep Convolution Neural Network

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Deep Learning Modeling for Gourd Species Recognition Using VGG-16

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Freshness Identification of Fruits Through the Development of a Dataset

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Convolutional Neural Network Based Image Processing Model for Supply Chain Management

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Plant disease detection using deep learning based Mobile application

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Grading Methods for Fruit Freshness Based on Deep Learning

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An integrated conceptual framework for AI-driven fruit supply chain quality management: pathways toward circularity and sustainability

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Machine learning-based detection of freezing events using infrared thermography

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Apple damages classification: Using the best convolutional neural network to discard low surface quality fruit in packing plants

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A comprehensive review of fruit and vegetable classification techniques

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A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: A survey

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Smart refrigerator model for food safety and health promotion using YOLOv10

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Identification of Fruits and Vegetables using Embedded Sensor

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Multi-Task Cascaded Convolutional Networks Based Intelligent Fruit Detection for Designing Automated Robot

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Real-Time Visual Localization of the Picking Points for a Ridge-Planting Strawberry Harvesting Robot

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Circular Fruit and Vegetable Classification Based on Optimized GoogLeNet

inferred← citeshttps://doi.org/10.1109/acoit66109.2025.11437033openalex-snapshot

Deep Learning-Based Convolutional Neural Network Approach for Smart Automated Classification and Sorting of Vegetables

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Image Saliency Analysis in Agricultural Environments: A Survey

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DishDelight: Your Friendly Indian Culinary Assistant

inferred← citeshttps://doi.org/10.1109/ecice55674.2022.10042838openalex-snapshot

Multi-Category Fruit Image Classification Based on Interactive Segmentation

inferred← citeshttps://doi.org/10.1109/globecom54140.2023.10437595openalex-snapshot

An E-health System Recognizing Vegetable Images Using Extreme Learning Machine

inferred← citeshttps://doi.org/10.1109/gucon50781.2021.9573704openalex-snapshot

Computer Vision Based Local Vegetables Recognition

inferred← citeshttps://doi.org/10.1109/iatmsi68868.2026.11465558openalex-snapshot

Deep Learning-Based Convolutional Neural Network Approach for Smart Automated Classification and Sorting of Vegetables

inferred← citeshttps://doi.org/10.1109/ic-etite58242.2024.10493799openalex-snapshot

A Fruit and Vegetable Detection Based on Pytorch Machine Learning Frame Work Model

inferred← citeshttps://doi.org/10.1109/icabcd59051.2023.10220482openalex-snapshot

Identification and classification of Green Leafy Vegetables using CNN models

inferred← citeshttps://doi.org/10.1109/iccci50826.2021.9402513openalex-snapshot

Automatic Fruit Detection System using Multilayer Deep Convolution Neural Network

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DCNN-Based Vegetable Image Classification Using Transfer Learning: A Comparative Study

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A Novel and Effective Ensemble Machine Learning Model for Identifying Healthy and Rotten Fruits

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Design of an IoT Based Ensemble Machine Learning Model for Fruit Classification and Quality Detection

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AI&ML-based smart non-disruptive methods for post-harvest fruit grading: State-of-the-Art

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Deep Learning-Based CNN Approach for Smart Automated Classification and Sorting of Vegetables

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Peer Review #1 of "Block-based compressive sensing in deep learning using AlexNet for vegetable classification (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.1551v0.1/reviews/2openalex-snapshot

Peer Review #2 of "Block-based compressive sensing in deep learning using AlexNet for vegetable classification (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.1551v0.2/reviews/1openalex-snapshot

Peer Review #1 of "Block-based compressive sensing in deep learning using AlexNet for vegetable classification (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.1551v0.2/reviews/2openalex-snapshot

Peer Review #2 of "Block-based compressive sensing in deep learning using AlexNet for vegetable classification (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.1551v0.3/reviews/1openalex-snapshot

Peer Review #1 of "Block-based compressive sensing in deep learning using AlexNet for vegetable classification (v0.3)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.1551v0.3/reviews/2openalex-snapshot

Peer Review #2 of "Block-based compressive sensing in deep learning using AlexNet for vegetable classification (v0.3)"

inferred← citeshttps://doi.org/10.7717/peerj-cs.1551openalex-snapshot

Block-based compressive sensing in deep learning using AlexNet for vegetable classification

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