# Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning

arXiv: https://arxiv.org/abs/2104.09808 · HTML: https://mlsanity.com/paper/2104.09808/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Adaptive Gradient Methods with Dynamic Bound of Learning Rate](https://mlsanity.com/paper/1902.09843/) (2019)
- [Some Improvements on Deep Convolutional Neural Network Based Image Classification](https://mlsanity.com/paper/1312.5402/) (2013)
- [Network Decoupling: From Regular to Depthwise Separable Convolutions](https://mlsanity.com/paper/1808.05517/) (2018)

## Built on by (most cited first, 12 of 12)

- [Wavelength-aware 2D Convolutions for Hyperspectral Imaging](https://mlsanity.com/paper/2209.03136/) (2022)
- [Tackling the Background Bias in Sparse Object Detection via Cropped Windows](https://mlsanity.com/paper/2106.02288/) (2021)
- [Hyperspectral Benchmark: Bridging the Gap between HSI Applications through Comprehensive Dataset and Pretraining](https://mlsanity.com/paper/2309.11122/) (2023)
- [SpectralZoom: Efficient Segmentation with an Adaptive Hyperspectral Camera](https://mlsanity.com/paper/2406.04287/) (2024)
- [HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone](https://mlsanity.com/paper/2605.17286/) (2026)
- [Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensor Network and Machine Learning](https://mlsanity.com/paper/2204.12415/) (2022)
- [Cost-efficient Active Illumination Camera For Hyper-spectral Reconstruction](https://mlsanity.com/paper/2406.19560/) (2024)
- [Label Semantics for Robust Hyperspectral Image Classification](https://mlsanity.com/paper/2510.07556/) (2025)
- [Adaptive Subspace Modeling With Functional Tucker Decomposition](https://mlsanity.com/paper/2603.25530/) (2026)
- [SSFT: A Lightweight Spectral-Spatial Fusion Transformer for Generic Hyperspectral Classification](https://mlsanity.com/paper/2604.15828/) (2026)
- [Transfer learning RGB models to hyperspectral images with trainable tensor decompositions](https://mlsanity.com/paper/2605.28331/) (2026)
- [Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction](https://mlsanity.com/paper/2608.01202/) (2026)

## Abstract (arXiv)

We present a system to measure the ripeness of fruit with a hyperspectral camera and a suitable deep neural network architecture. This architecture did outperform competitive baseline models on the prediction of the ripeness state of fruit. For this, we recorded a data set of ripening avocados and kiwis, which we make public. We also describe the process of data collection in a manner that the adaption for other fruit is easy. The trained network is validated empirically, and we investigate the trained features. Furthermore, a technique is introduced to visualize the ripening process.