# Fruit recognition from images using deep learning

arXiv: https://arxiv.org/abs/1712.00580 · HTML: https://mlsanity.com/paper/1712.00580/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2017.

## Built on by (most cited first, 30 of 35)

- [EMR-Merging: Tuning-Free High-Performance Model Merging](https://mlsanity.com/paper/2405.17461/) (2024)
- [CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets](https://mlsanity.com/paper/2302.02551/) (2023)
- [A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU](https://mlsanity.com/paper/2305.17473/) (2023)
- [Wasserstein Iterative Networks for Barycenter Estimation](https://mlsanity.com/paper/2201.12245/) (2022)
- [HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding](https://mlsanity.com/paper/2311.14064/) (2023)
- [Localized Adaptive Risk Control](https://mlsanity.com/paper/2405.07976/) (2024)
- [GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER](https://mlsanity.com/paper/2204.00294/) (2022)
- [Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentation](https://mlsanity.com/paper/2104.05647/) (2021)
- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [HRel: Filter Pruning based on High Relevance between Activation Maps and Class Labels](https://mlsanity.com/paper/2202.10716/) (2022)
- [Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain](https://mlsanity.com/paper/2207.10002/) (2022)
- [Fruit Ripeness Classification: a Survey](https://mlsanity.com/paper/2212.14441/) (2022)
- [Organizing Unstructured Image Collections using Natural Language](https://mlsanity.com/paper/2410.05217/) (2024)
- [Sheaves as a Framework for Understanding and Interpreting Model Fit](https://mlsanity.com/paper/2105.10414/) (2021)
- [A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification](https://mlsanity.com/paper/2110.07097/) (2021)
- [A Novel lightweight Convolutional Neural Network, ExquisiteNetV2](https://mlsanity.com/paper/2105.09008/) (2021)
- [Deep Models for Multi-View 3D Object Recognition: A Review](https://mlsanity.com/paper/2404.15224/) (2024)
- [NL-CNN: A Resources-Constrained Deep Learning Model based on Nonlinear Convolution](https://mlsanity.com/paper/2102.00227/) (2021)
- [Conceptual Views of Neural Networks: A Framework for Neuro-Symbolic Analysis](https://mlsanity.com/paper/2209.13517/) (2022)
- [Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark](https://mlsanity.com/paper/2303.06596/) (2023)
- [Synthetic data enables faster annotation and robust segmentation for multi-object grasping in clutter](https://mlsanity.com/paper/2401.13405/) (2024)
- [Text-Guided Alternative Image Clustering](https://mlsanity.com/paper/2406.18589/) (2024)
- [Training-free Task Classification for Multi-Task Model Merging](https://mlsanity.com/paper/2606.22589/) (2026)
- [One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations](https://mlsanity.com/paper/2106.01423/) (2021)
- [Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning](https://mlsanity.com/paper/2110.03127/) (2021)
- [Adaptive Transfer Learning: a simple but effective transfer learning](https://mlsanity.com/paper/2111.10937/) (2021)
- [Banana Sub-Family Classification and Quality Prediction using Computer Vision](https://mlsanity.com/paper/2204.02581/) (2022)
- [A Case for Dataset Specific Profiling](https://mlsanity.com/paper/2208.03315/) (2022)
- [Carrot Cure: A CNN based Application to Detect Carrot Disease](https://mlsanity.com/paper/2304.03511/) (2023)
- [Long-Tailed Recognition on Binary Networks by Calibrating A Pre-trained Model](https://mlsanity.com/paper/2404.00285/) (2024)

## 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.