Ziziphus mauritiana Hyperspectral-Image Open Dataset

Ziziphus mauritiana Hyperspectral-Image Open Dataset

Introduction

Ziziphus mauritiana Lam, known as jujube were utilized for sugar content prediction experiments. 205 “Tainung No.13 Shirley” jujubes were collected. Each fruit was sliced into slices. The hyperspectral data of each slice were measured by the coaxial heterogeneous HSI system [1]. A total of 2,123 jujube slices were collected. Each slice has one ground truth sugar content value (Brix). All hyperspectral data were calibrated with spatial calibration, white/dark light calibration, and Savitzky-Golay Filtering. For each slice, the spatial region of interest (ROI) in the hyperspectral data was selected and averaged. The dataset was divided into training, validation, and test sets. The jujube datasets are summarized in Table 1.

 

Table 1

°Brix

interval

 Mean STD Train. Valid. Test
[11,12] 11.53 0.26 82 18 18
[12,13] 12.53 0.28 176 38 38
[13,14] 13.50 0.29 326 70 70
[14,15] 14.42 0.29 294 63 64
[15,16] 15.40 0.29 238 51 52
[16,17] 16.42 0.29 188 40 41
[17,18] 17.38 0.28 106 23 23
[18,19] 18.43 0.37 72 16 16
Total 1482 319 322

 

Below is the file structure of the HIS data samples and corresponding labels. The dataset link is at the end of the page.

├── Hyper

│   ├── 3d_data

│   │   └── 400_1700

│   │       ├── data_test.pkl

│   │       ├── data_train.pkl

│   │       ├── data_val.pkl

│   │       ├── label_test.pkl

│   │       ├── label_train.pkl

│   │       └── label_val.pkl

└─  └──

 

 

For more details, please refer to the reference [4].

For related example code, please visit the following link: https://ebil.web.nycu.edu.tw/open-data/scientific-reports_202202/“

 

References

[1] Yu-Hsiang Tsai, Yung-Jhe Yan, Yi-Sheng Li, Chao-Hsin Chang, Chi-Cho Huang, Tzung-Cheng Chen, Shiou-Gwo Lin, and Mang Ou-Yang*, “Development and verification of the coaxial heterogeneous hyperspectral imaging system,” Review of Scientific Instruments, Vol.93, pp.063105-1-17, Jun. 2022. DOI: 10.1109/I2MTC.2019.8826836

[2] Chih-Jung Chen, Yung-Jhe Yan, Chi-Cho Huang, Jen-Tzung Chien, Chang-Ting Chu, Je-Wei Jang, Tzung-Cheng Chen, Shiou-Gwo Lin, Ruei-Siang Shih and Mang Ou-Yang*, “Sugariness Prediction of Syzygium samarangense using Convolutional Learning of Hyperspectral Images,” Scientific Reports, 12:2774, 17 Feb. 2022. DOI:10.1038/s41598-022-06679-6

 

[3] Yung-Jhe Yan, Weng-Keong Wong, Chih-Jung Chen, Chi-Cho Huang, Jen‑Tzung Chien and Mang Ou-Yang*, “Hyperspectral signature-band extraction and learning: an example of sugar content prediction of Syzygium samarangense,” Scientific Reports, 13:15100, 12 Sep. 2023. DOI: 10.1038/s41598-022-06679-6

 

[4] Yung-Jhe Yan, Jen-Tzung Chien, Zi-Yin Hong, Wen-Li Lee, Kuo-Dung Chiou, Chi-Cho Huang, Mang Ou-Yang, “Hyperspectral signature-band extraction and adaptation for sugar content prediction on Ziziphus mauritiana and Syzygium samarangense,” Smart Agricultural Technology, p. 101009, 2025/05/12/ 2025, doi: https://doi.org/10.1016/j.atech.2025.101009.

 

Acknowledgements

This work is supported by grants from Agricultural Research Institute, Ministry of Agriculture, Executive of Yuan, Taiwan, and by the National Science and Technology Council, Taiwan, and National Yang Ming Chiao Tung University, Taiwan. This work contributed by following authors: Chih-Jung Chen, Yung-Jhe Yan, Weng-Keong Wong, Zi-Yin Hong, Wen-Li Lee, Kuo-Dung Chiou, Chi-Cho Huang, Jen-Tzung Chien, Chang-Ting Chu, Je-Wei Jang, Tzung-Cheng Chen, Shiou-Gwo Lin, Ruei-Siang Shih and Mang Ou-Yang.

 

Open data set download link:

https://drive.google.com/drive/folders/1S83roGwfiXbJQfeUylWlgcE35NDvMP0b?usp=sharing