Application of Hyperspectral Imaging for Identification of Melon Seed Variety Using Deep Learning Article Swipe
Zhiqi Hong
,
Chu Zhang
,
Wenjian Song
,
Xiangbo Nie
,
Hongxia Ye
,
Yong He
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15111139
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15111139
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture15111139
- https://www.mdpi.com/2077-0472/15/11/1139/pdf?version=1748167213
- OA Status
- gold
- References
- 27
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.3390/agriculture15111139Digital Object Identifier
- Title
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Application of Hyperspectral Imaging for Identification of Melon Seed Variety Using Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
- Publication date
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2025-05-25Full publication date if available
- Authors
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Zhiqi Hong, Chu Zhang, Wenjian Song, Xiangbo Nie, Hongxia Ye, Yong HeList of authors in order
- Landing page
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https://doi.org/10.3390/agriculture15111139Publisher landing page
- PDF URL
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https://www.mdpi.com/2077-0472/15/11/1139/pdf?version=1748167213Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2077-0472/15/11/1139/pdf?version=1748167213Direct OA link when available
- Concepts
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Hyperspectral imaging, Melon, Identification (biology), Variety (cybernetics), Biology, Artificial intelligence, Agronomy, Botany, Agroforestry, Computer science, HorticultureTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| primary_location.pdf_url | https://www.mdpi.com/2077-0472/15/11/1139/pdf?version=1748167213 |
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| primary_location.raw_source_name | Agriculture |
| primary_location.landing_page_url | https://doi.org/10.3390/agriculture15111139 |
| publication_date | 2025-05-25 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4312115189, https://openalex.org/W4309859119, https://openalex.org/W2770285436, https://openalex.org/W2118076719, https://openalex.org/W4406081072, https://openalex.org/W4388590888, https://openalex.org/W4390630114, https://openalex.org/W2965405319, https://openalex.org/W3164816634, https://openalex.org/W4401219056, https://openalex.org/W4225114772, https://openalex.org/W4321597780, https://openalex.org/W6801684201, https://openalex.org/W4393164526, https://openalex.org/W4394946239, https://openalex.org/W3179717400, https://openalex.org/W841202094, https://openalex.org/W1723129825, https://openalex.org/W2045256553, https://openalex.org/W2295598076, https://openalex.org/W3168997536, https://openalex.org/W4312469371, https://openalex.org/W3034552520, https://openalex.org/W2884963893, https://openalex.org/W4322627125, https://openalex.org/W3092667979, https://openalex.org/W3200138571 |
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