Cost effective image-based phenotyping in Capsicum annuum germplasm for rapid assessment of traits

Park J. E., Mansoor S., Ku K., Le A. T., Tuan T. T., Ko H., ...Daha Fazla

Plant Biotechnology Reports, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1007/s11816-025-00968-y
  • Dergi Adı: Plant Biotechnology Reports
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Food Science & Technology Abstracts, INSPEC, Veterinary Science Database
  • Anahtar Kelimeler: Breeding, Image analysis, Machine learning, Phenotyping, Traits
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Hayır

Özet

Chili is a valuable crop known for its flavor and nutritional benefits. Developing new chili varieties with desirable traits requires analyzing phenotypic diversity and correlations within chili germplasm. This study utilized image-based methods to assess phenotypic trait diversity in chili germplasm in cost-effective manner. High-resolution imaging and computational algorithms were employed to extract quantitative data on traits, including leaf area, flower morphology, stem structure, and fruit characteristics. We analyzed 188 accessions of Capsicum annuum from 36 countries, with geographic origin data sourced from the National Seed Resources in Korea. The study focused on leaf, flower, stem, and fruit traits, and their interrelationships. Results indicated significant variability in leaf area, length, and width between two populations (K = 2), with CV values of 0.38, 0.18, and 0.22, respectively. The Principal Component Analysis (PCA) showed different variation among leaf area and leaf length (98.5%), stem angle and length (46.5%), fruit and flower traits (43.1%). Image-based phenotyping in Capsicum annuum germplasm provides a cost-effective, efficient, and comprehensive method for plant trait analysis. This technology reduces the need for labor-intensive manual measurements, optimizes resource use, and enables the rapid assessment of large plant populations.