Progressive feature enrichment and ensemble learning for enhanced rice seed purity classification
This study introduces an effective method for classifying rice seed purity varieties, addressing the challenge of identifying mixed seeds in real-world scenarios. Initially, conventional feature extraction methods such as Gray Level Co-occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIF...
保存先:
| 主要な著者: | , , |
|---|---|
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Elsevier
2025-10-01
|
| シリーズ: | Alexandria Engineering Journal |
| 主題: | |
| オンライン･アクセス: | http://www.sciencedirect.com/science/article/pii/S1110016825009184 |
| タグ: |
タグなし, このレコードへの初めてのタグを付けませんか!
|