Standartlaştırılmış Bir Görüntüleme Düzeneği Altında Pamuk Yaprağı Fenotiplemesinde RGB-Türevli İndekslerin Değerlendirilmesi

Öz

Düşük maliyetli RGB görüntüleme fenotipleme için erişilebilir olsa da renk, cihaz ve aydınlatma koşullarına bağlı değişkenlik gösterebilir. Bu çalışmada, standart bir akıllı telefon çekim düzeniyle elde edilen RGB-türevli indekslerin, erken fide dönemindeki pamuk (Gossypium hirsutum L.) yaprak özellikleri için optik proxy olma potansiyeli test edildi. Üç büyüme odası deneyinden alınan yapraklar (n=80) kapalı bir çekim çadırında, kadraj içi gri/beyaz/siyah kart ile görüntülendi ve Adobe Photoshop’ta referans-temelli düzeltildi. Manuel ROI’lardan ortalama RGB değerleri çıkarılarak 15 RGB/CIELAB indeks hesaplandı; SPAD, spesifik yaprak alanı (SLA), damar yoğunluğu, su içeriği (WC), stoma yoğunluğu ve stoma boyutu ile Pearson r ve ikinci dereceden regresyon (adj. R², NRMSE) kullanılarak tarandı. En güçlü ilişkiler SLA–h_ab (adj. R²=0.666), damar yoğunluğu–TGI (adj. R²=0.610) ve SPAD–G (adj. R²=0.558) için bulundu. WC c_ab ile orta düzeyde ilişkiliyken (adj. R²=0.344), stoma özelliklerinde açıklayıcılık zayıftı tespit edildi. Sonuçlar, tüketici tipi RGB görüntülemenin pigment ve yapısal özellikler için hızlı bir ilk tarama aracı olabileceğini göstermektedir.

Anahtar Kelimeler

• pamuk
• RGB görüntüleme
• düşük maliyetli fenotipleme
• spesifik yaprak alanı (SLA)
• Triangular Greenness Index (TGI)

Evaluation of RGB-Derived Indices for Cotton Leaf Phenotyping under a Standardized Imaging Setup

Öz

Low-cost RGB imaging is accessible for phenotyping, but color varies with devices and illumination. We tested whether RGB-derived indices from a standardized smartphone setup can proxy cotton (Gossypium hirsutum L.) leaf traits at the early seedling stage. Leaves (n=80) from three growth-chamber experiments were imaged in a closed light-tent with an in-frame gray/white/black card, then corrected in Adobe Photoshop. Mean leaf RGB values (manual ROIs) were used to compute 15 RGB/CIELAB indices, which were screened against SPAD, specific leaf area (SLA), vein density, water content (WC), stomatal density, and stomatal size using Pearson r and second-order regression (adj. R², NRMSE). The strongest relationships were for SLA (h_ab; adj. R²=0.666), vein density (TGI; adj. R²=0.610), and SPAD (G; adj. R²=0.558). WC was moderately associated with c_ab (adj. R²=0.344), while stomatal traits were weakly explained, consistent with scale limits of top-down mean-color metrics. Standardized consumer RGB imaging can therefore support rapid first-pass screening of pigment- and structure-related leaf traits.

Anahtar Kelimeler

• cotton
• RGB imaging
• low-cost phenotyping
• specific leaf area (SLA)
• Triangular Greenness Index (TGI)

Kaynakça

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