# Fruit Ripeness Classification: a Survey

arXiv: https://arxiv.org/abs/2212.14441 · HTML: https://mlsanity.com/paper/2212.14441/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 143 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Is Attention Interpretable?](https://mlsanity.com/paper/1906.03731/) (2019)
- [Video Transformer Network](https://mlsanity.com/paper/2102.00719/) (2021)
- [MinneApple: A Benchmark Dataset for Apple Detection and Segmentation](https://mlsanity.com/paper/1909.06441/) (2019)
- [Deep Fruit Detection in Orchards](https://mlsanity.com/paper/1610.03677/) (2016)
- [Fruit recognition from images using deep learning](https://mlsanity.com/paper/1712.00580/) (2017)
- [Sample Efficient Text Summarization Using a Single Pre-Trained Transformer](https://mlsanity.com/paper/1905.08836/) (2019)
- [A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels](https://mlsanity.com/paper/1901.00711/) (2019)

## Built on by (most cited first, 7 of 7)

- [Advances in Deep Concealed Scene Understanding](https://mlsanity.com/paper/2304.11234/) (2023)
- [Stop overkilling simple tasks with black-box models and use transparent models instead](https://mlsanity.com/paper/2302.02804/) (2023)
- [Privacy Preserving Ordinal-Meta Learning with VLMs for Fine-Grained Fruit Quality Prediction](https://mlsanity.com/paper/2511.01449/) (2025)
- [MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection](https://mlsanity.com/paper/2511.12810/) (2025)
- [YOLO26-RipeLoc Lite: A lightweight architecture for tomato ripeness detection and picking point localization in greenhouse robotic harvesting](https://mlsanity.com/paper/2605.27129/) (2026)
- [Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction](https://mlsanity.com/paper/2608.01202/) (2026)
- [Selective Spatio-Temporal Fruit Inspector (S2FI): A Mamba-based Framework for Rotational Fruit Quality Assessment](https://mlsanity.com/paper/2610.12868/) (2026)

## Abstract (arXiv)

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by field experts, making it a labor-intensive and error-prone process. Thus, there is an arising need for automation in fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on.