Application of Hyperspectral Imaging for Identification of Melon Seed Variety Using Deep Learning

Zhiqi Hong, Chu Zhang, Wenjian Song, Xiangbo Nie, Hongxia Ye () and Yong He ()
Additional contact information
Zhiqi Hong: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Chu Zhang: School of Information Engineering, Huzhou University, Huzhou 313000, China
Wenjian Song: The Rural Development Academy & Agricultural Experiment Station, Zhejiang University, Hangzhou 310058, China
Xiangbo Nie: Shaoxing Jinshuo Agricultural Technology Co., Ltd., Shaoxing 312000, China
Hongxia Ye: Institute of Vegetable Science, Zhejiang University, Hangzhou 310058, China
Yong He: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Agriculture, 2025, vol. 15, issue 11, 1-17

Abstract: The accurate identification of melon seed varieties is essential for improving seed purity and the overall quality of melon production. In this study, hyperspectral imaging was used to identify six varieties of melon seeds. Both hyperspectral images and RGB images were generated during hyperspectral image acquisition. The spectral features of seeds were extracted from the hyperspectral images. The image features of the corresponding seeds were manually extracted from the RGB images. Five different datasets were formed using the spectral features and RGB images of the seeds, including seed spectral features, manually extracted seed image features, seed images, the fusion of seed spectral features with manually extracted features, and the fusion of seed spectral features with seed images. Logistic Regression (LR), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGBoost) were used to establish classification models using spectral features and the manually extracted image features. Convolutional Neural Network (CNN) models were established using the five datasets. The results indicated that the CNN models achieved good performance in all five datasets, with classification accuracies exceeding 90% for the training, validation, and test sets. Also, CNN using the fused datasets obtained optimal performance, achieving classification accuracies exceeding 97% for the training, validation, and test sets. The results indicated that both spectral features and image features can be used to identify the six varieties of melon seeds, and their fusion of spectral features and image features can improve classification performance. These findings provide an alternative approach for melon seed variety identification, which can also be extended to other seed types.

Keywords: hyperspectral imaging; image feature; data fusion; convolutional neural network; melon seeds (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/11/1139/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/11/1139/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:11:p:1139-:d:1664094

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().