Image‐based plant phenotyping involves the quantitative determination of complex plant traits using image analysis. One important parameter to assess is the degree of greenness of photosynthetic tissues, as it may reflect plant health, development, or the pigment‐depleting impact of stressful environments. Various attempts have been made to quantify leaf greenness scores, but each has shown restricted utility and efficacy. Often, these methods overlooked the precision needed to represent greenness differences. Here, we developed an improved method, the ‘Green Index’ (GI), to quantitatively score the greenness of photosynthetic tissues and track smooth transitions in seedling greening during de‐etiolation. GI is open‐source, uses widely available RGB values from image pixels, and does not require advanced computational skills (available at www.foodandplantbiology.com). We describe the conception of the GI formula and evaluate its superiority over existing methods using both literature‐derived and new datasets. Furthermore, we demonstrated the utility of the GI in addressing common issues encountered in assessing plant phenotype in biology experiments, underscoring its potential as a reliable and accessible tool. Based on greenness, GI quantitatively discriminates leaf health, developmental stages, and stress sensitivity. We also report that GI significantly correlates with chlorophyll content, and can thus serve as a proxy for tracking chlorophyll trends.