Papers › Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning
Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning
Leon Amadeus Varga, Jan Makowski, Andreas Zell
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Hyperspectral Image-Based Fruit Ripeness Prediction | DeepHS Fruit v2 | DeepHS-Net | Overall Classification Accuracy | 58.28 % | #2 of 3 | Archive leaderboard | report |
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