Correlation Analysis of RGB Image-Based Vegetation Indices to Chlorophyll Content in Leafy Vegetables with Nutrient-Stressed Condition: Analisis Korelasi Indeks Vegetasi Berbasis Citra RGB Terhadap Kandungan Klorofil pada Tanaman Sayuran Daun dengan Kondisi Cekaman Hara - Suharto, Yohanes Bayu; Suhardiyanto, Herry; Messaline, Theressa (Universitas Brawijaya, 2025)
Nutrient stress is one of the main factors affecting the growth and productivity of leafy vegetables. Chlorophyll content is often used to indicate plant nutrient status, but conventional measurement methods are destructive and inefficient. This study aims to analyze the correlation between various RGB camera image-based vegetation indices and chlorophyll content in hydroponically cultivated leafy vegetables under nutrient-stress treatment. The six vegetation indices used in this study are Excess Green (EXG), Visible-band Difference Vegetation Index (VDVI), RGB Vegetation Index (RGBVI), Normalized Green Blue Difference Index (NGBDI), Green-Red Vegetation Index (GRVI), and Visible Atmospherically Resistant Index (VARI). RGB image data were captured using an RGB digital web camera sensor (Xiaovv XVV-6320S) on a photo box set under controlled lighting conditions. At the same time, chlorophyll content was measured using a SPAD-502 Chlorophyll Meter. Pearson correlation analysis showed that the vegetation indices VARI (r = 0.90, R² = 0.82) and GRVI (r = 0.89, R² = 0.80) had robust correlations with chlorophyll content, making them the best indices for RGB image-based estimation of chlorophyll content in leafy vegetables. The results of this study indicate that RGB image-based vegetation indices can be an efficient, non-destructive method for detecting nutrient stress in leafy vegetables and have the potential to be applied in precision agriculture systems and automated monitoring in greenhouses.