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Fruit recognition from images using deep learning

arXiv 1712.00580 ↗First posted 2 Dec 2017cs.CVcited by 35

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Abstract (arXiv)

In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use this kind of neural network.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2017.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 5; 2024: 8; 2025: 2; 2026: 3.

35 papers.

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  1. 2024EMR-Merging: Tuning-Free High-Performance Model Mergingcited by 108
  2. 2023CHiLS: Zero-Shot Image Classification with Hierarchical Label Setscited by 79
  3. 2023A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRUcited by 25
  4. 2022Wasserstein Iterative Networks for Barycenter Estimationcited by 23
  5. 2023HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understandingcited by 21
  6. 2024Localized Adaptive Risk Controlcited by 16
  7. 2022GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWERcited by 15
  8. 2021Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentationcited by 9
  9. 2021A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessmentcited by 7
  10. 2022HRel: Filter Pruning based on High Relevance between Activation Maps and Class Labelscited by 7
  11. 2022Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domaincited by 7
  12. 2024Organizing Unstructured Image Collections using Natural Languagecited by 7
23 more
  1. 2022Fruit Ripeness Classification: a Surveycited by 6
  2. 2021Sheaves as a Framework for Understanding and Interpreting Model Fitcited by 5
  3. 2021A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classificationcited by 4
  4. 2021A Novel lightweight Convolutional Neural Network, ExquisiteNetV2cited by 2
  5. 2024Deep Models for Multi-View 3D Object Recognition: A Reviewcited by 2
  6. 2021NL-CNN: A Resources-Constrained Deep Learning Model based on Nonlinear Convolutioncited by 1
  7. 2022Conceptual Views of Neural Networks: A Framework for Neuro-Symbolic Analysiscited by 1
  8. 2023Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmarkcited by 1
  9. 2024Synthetic data enables faster annotation and robust segmentation for multi-object grasping in cluttercited by 1
  10. 2024Text-Guided Alternative Image Clusteringcited by 1
  11. 2026Training-free Task Classification for Multi-Task Model Mergingcited by 1
  12. 2021One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representationscited by 0
  13. 2021Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learningcited by 0
  14. 2021Adaptive Transfer Learning: a simple but effective transfer learningcited by 0
  15. 2022Banana Sub-Family Classification and Quality Prediction using Computer Visioncited by 0
  16. 2022A Case for Dataset Specific Profilingcited by 0
  17. 2023Carrot Cure: A CNN based Application to Detect Carrot Diseasecited by 0
  18. 2024Long-Tailed Recognition on Binary Networks by Calibrating A Pre-trained Modelcited by 0
  19. 2024MANGO: Learning Disentangled Image Transformation Manifolds with Grouped Operatorscited by 0
  20. 2025Impact of Tuning Parameters in Deep Convolutional Neural Network Using a Crack Image Datasetcited by 0
  21. 2025Resonant-Tunnelling Diode Reservoir Computing System for Image Recognitioncited by 0
  22. 2026Learning to Recover Task Experts from a Multi-Task Merged Modelcited by 0
  23. 2026Multi-Scale Fruit Capsules: Dilated Convolutions and Dynamic Routing for In-the-Wild Explainable Fruit Recognitioncited by 0