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Measuring the ripeness of fruit with Hyperspectral Imaging and Deep Learning
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cnn data-set deep-learning hyperspectral-image-classification hyperspectral-imaging pytorch

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Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning

Version 2:
    - The training is now more stable
    - Added new hyperspectral recordings
    - Added HyveConv++ model

Version 1 is still avaiable on branch 'paper_version'

Here you can find the dataset and the official implementation of the HS-CNN network with an optimized training pipeline.

Data set (v2)

The dataset is here or as torrent available. It contains recordings of:

Three hyperspectral cameras were use:

The data set covers four measurement series. Labels are provided as destructive measurements (fruit flesh firmness, sugar content and overall ripeness)

Requirements

How to train

If all packages are installed and the data set was downloaded, the training can start. This will train the HS-CNN model on the ripeness classification of avocados:

PYTHONPATH=$PYTHONPATH:. python3 classification/train.py --data_path /folder/of/downloaded/dataset/ --model deephs_net --fruit avocado --classification_type ripeness --seed 23312323

Loss
Accuracy
Confusion
Figure 1 - Training of HS-CNN:

And this will train HS-CNN + HyveConv++ on the same classification task:

PYTHONPATH=$PYTHONPATH:. python3 classification/train.py --data_path /folder/of/downloaded/dataset/ --model hyve --fruit avocado --classification_type ripeness --seed 23312323

Loss
Accuracy
Confusion
Figure 2 - Training of HS-CNN + HyveConv++:

PYTHONPATH=$PYTHONPATH:. python3 classification/train.py --help provides helpful information regarding the parameters. For more information about the training framework PyTorch-Lightning, we refer to the official documentation (https://pytorch-lightning.readthedocs.io/en/latest/).

Citation

The paper was presented on IJCNN 2021.

@inproceedings{Varga2021,
abstract = {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.},
archivePrefix = {arXiv},
arxivId = {2104.09808},
author = {Varga, Leon Amadeus and Makowski, Jan and Zell, Andreas},
booktitle = {2021 International Joint Conference on Neural Networks (IJCNN)},
doi = {10.1109/IJCNN52387.2021.9533728},
eprint = {2104.09808},
isbn = {978-1-6654-3900-8},
keywords = {Index Terms-hyperspectral,convolutional neu-ral network,deep learning,ripening fruit},
month = {jul},
pages = {1--8},
publisher = {IEEE},
title = {{Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning}},
url = {https://arxiv.org/abs/2104.09808v1 http://arxiv.org/abs/2104.09808 https://ieeexplore.ieee.org/document/9533728/},
year = {2021}
}

For HyveConv++ please check: https://github.com/cogsys-tuebingen/hyve_conv