# Fruit Quality Assessment with Densely Connected Convolutional Neural Network

arXiv: https://arxiv.org/abs/2212.04255 · HTML: https://mlsanity.com/paper/2212.04255/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification](https://mlsanity.com/paper/2109.02394/) (2021)
- [Two Decades of Bengali Handwritten Digit Recognition: A Survey](https://mlsanity.com/paper/2206.02234/) (2022)
- [Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling](https://mlsanity.com/paper/2005.06089/) (2020)

## Built on by (most cited first, 9 of 9)

- [Rethinking Cooking State Recognition with Vision Transformers](https://mlsanity.com/paper/2212.08586/) (2022)
- [AQUA20: A Benchmark Dataset for Underwater Species Classification under Challenging Conditions](https://mlsanity.com/paper/2506.17455/) (2025)
- [An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification](https://mlsanity.com/paper/2304.06520/) (2023)
- [MangoLeafViT: Leveraging Lightweight Vision Transformer with Runtime Augmentation for Efficient Mango Leaf Disease Classification](https://mlsanity.com/paper/2505.23961/) (2025)
- [DExNet: Combining Observations of Domain Adapted Critics for Leaf Disease Classification with Limited Data](https://mlsanity.com/paper/2506.18173/) (2025)
- [Huruf: An Application for Arabic Handwritten Character Recognition Using Deep Learning](https://mlsanity.com/paper/2212.08610/) (2022)
- [Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review](https://mlsanity.com/paper/2504.10527/) (2025)
- [TriAlignXA: An Explainable Trilemma Alignment Framework for Trustworthy Agri-product Grading](https://mlsanity.com/paper/2510.01990/) (2025)
- [SpectroFusion-ViT: A Lightweight Transformer for Speech Emotion Recognition Using Harmonic Mel-Chroma Fusion](https://mlsanity.com/paper/2603.00746/) (2026)

## Abstract (arXiv)

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.