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Title: A Quantitative Spectra Analysis Framework Combining Mixup and Band Attention for Predicting Soluble Solid Content of Blueberries.

The original paper can be found at https://doi.org/10.1007/978-3-031-40292-0_30.

Abstract: Hyperspectral imaging can rapid and non-destructive monitor physical characteristics and intrinsic chemical information of food. In recent years, many studies have applied hyperspectral imaging to evaluate the internal quality of fruits. However, due to the influence of environmental factors, there are abnormal samples in the collected data. Furthermore, the model faces challenges such as limited data availability and insufficient diversity in the dataset. In this study, we collected a total of 1010 hyperspectral images of blueberries and measured their soluble solid content ( SSC). To reduce the influence of abnormal samples and increase the diversity of samples, we propose a deep learning framework combining mixup and band attention to predict blueberry SSC. The mixup module performs data augmentation on both spectra and SSC values, enhancing sample diversity and improving the generalization performance of the model. The band attention module captures cross-band information and learns band weights, enabling the model to focus on the bands relevant to SSC. Furthermore, we find that bands with higher weights are consistent with SSC-sensitive bands in existing knowledge, which improves the interpretability of the model.

MBA framework:

requirements.txt

"""
--MBA-2023/
----checkpoints/
----data/
----images/
----logger/: Output experimental results, yaml files, logs
----models/: 
--------ablation_1.py
--------ablation_2.py
--------MBA.py
--------mix.py: mixup module
--------model_loader.py
----plot/: 
--------PLOT.py: plot utils
----calculate_band_weight.py: Load the pre trained model, calculate the average weight of each band, and draw a graph
----calculate_result.py: Load yaml file to calculate experimental results
----config.py: Hyperparameter configuration
----dataloader.py: Read data, define Dataset classes, load DataLoaders, etc
----init.py
----load_model_for_test.py: Load the pre trained model
----main.py
----metrics.py: Calculate regression evaluation indicators(R2/MSE/...)
----schedulers.py
----utils.py
----yaml_utils.py
"""

The first column represents Brix (soluble solid content), the second column represents hardness, and the other columns represent different bands. Each row represents a sample, and the values from the third to last columns represent the reflectance in that band. In the data folder we present some sample data.

For your own data, you need to customize the function in dataloader.py ->read_single_csv_data. And make corresponding modifications to other relevant parts(config.py -> Constant.train_csv_path_list, etc.).

If you use this code, or otherwise found our work valuable, please cite:

@InProceedings{10.1007/978-3-031-40292-0_30,
author="Li, Zhaokui
and Zhang, Jinen
and Li, Wei
and Li, Fei
and Bi, Ke
and Li, Hongli",
editor="Jin, Zhi
and Jiang, Yuncheng
and Buchmann, Robert Andrei
and Bi, Yaxin
and Ghiran, Ana-Maria
and Ma, Wenjun",
title="A Quantitative Spectra Analysis Framework Combining Mixup and Band Attention for Predicting Soluble Solid Content of Blueberries",
booktitle="Knowledge Science, Engineering and Management",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="366--373",
isbn="978-3-031-40292-0"
}