Deep learning-based seed germination prediction using morphological traits and RGB images
Yazarlar (2)
Öğr. Gör. Olcay TOSUN Sinop Üniversitesi, Türkiye
Prof. Dr. Recep Eryiğit Ankara Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | BMC Plant Biology (Q1) | ||
| Dergi ISSN | 1471-2229 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2026 |
| Cilt / Sayı / Sayfa | 26 / 1 / 757–0 | DOI | 10.1186/s12870-026-08599-3 |
| Makale Linki | https://doi.org/10.1186/s12870-026-08599-3 | ||
| UAK Araştırma Alanları |
Yapay Zeka
|
||
| Özet |
| Seed selection constitutes the initial and one of the most critical steps in agricultural productivity. The identification of high-quality seeds is a labor-intensive and costly process that requires considerable expertise. Within the scope of smart farming applications, this study proposes a deep learning--based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images. The proposed model determines whether a seed will germinate using RGB images and morphological traits automatically extracted from these images. The dataset used in this study comprises a total of 3,645 images belonging to three different seed types. For each seed type (okra, eggplant, and tomato), 405 seed images were acquired from three distinct imaging sources (digital microscope, camera, and scanner), labeled, and subsequently sown in seed trays. The germination status of ... |
| Anahtar Kelimeler |
| CNN | deep learning | prediction | seed germination | smart agriculture |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |