SapBark-64: A dataset of bark images for 64 fruit-tree sapling classes

ALIZADEH S., Shamsi H.

Data in Brief, cilt.64, 2026 (ESCI, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 64
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.dib.2025.112354
  • Dergi Adı: Data in Brief
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, BIOSIS, Chemical Abstracts Core, Compendex, Directory of Open Access Journals
  • Anahtar Kelimeler: Bark texture analysis, Deep learning, Horticulture, Sapling image, Tree bark images, Tree dataset, Tree identification
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

We present SapBark-64, a curated dataset of 5742 close-range bark images from 64 fruit-tree sapling classes (species/cultivar). Images were acquired in situ at three commercial nurseries in Trabzon (Türkiye) in 2025, targeting 1–2-year saplings routinely traded in nurseries. Photographs were captured with an iPhone 16 Pro Max at approximately 10 cm from the trunk under near-uniform illumination, using a white background to occlude scene clutter and preserve fine-scale morphology. For each class, a nursery label photo was recorded to support ground truth, and class-level characteristics were collected at the time of recording under expert supervision. The repository is organized as two parallel image folders plus a structured metadata workbook: (i) raw images (JPG) and (ii) background-removed images (WebP) that mirror the same 64 class folders named by species/cultivar, enabling one-to-one pairing across versions; and (iii) an Excel (XLSX) metadata file list- ing standardized fields (family, scientific/common name, cultivar/variety, sapling height, trunk diameter, best planting season, growth rate, fruit-bearing age, average yield, production region, propagation method). This organization facilitates fine- grained identification and retrieval tasks and supports trait-conditioned analyses linking visual texture to horticultural attributes. The dataset is publicly available in an open repository under a permissive license; acquisition conditions, directory layout, and the metadata schema are documented to enable unambiguous reuse.