Proxima Green: RGB color metrics for turfgrass phenotyping in controlled conditions

Authors: Conley, Matthew; Hejl, Reagan; Stiles, Julia; Serba, Desalegn; Wang, Dong; Williams, Clinton

Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/27/2026
Publication Date: 7/29/2026
Citation: Conley, M.M., Hejl, R.W., Stiles, J.F., Serba, D.D., Wang, D., Williams, C.F. 2026. Proxima Green: Color metrics for practical turfgrass phenotyping. Sensors. 26(15):4816. https://doi.org/10.3390/s26154816.
DOI: https://doi.org/10.3390/s26154816

Interpretive Summary: Turfgrass managers and researchers often rely on visual scores or expensive sensors to judge turfgrass color and stress, but these methods can be subjective, costly, or difficult to use consistently. This study shows that simple digital camera images, collected under controlled lighting, can provide reliable and interpretable measurements of turfgrass health. We developed new color-based indicators, including a distance from ideal green metric ('Eg), which expresses turf color change in human perception units. Using a greenhouse experiment with different fertilizer and irrigation treatments, we compared camera-based measurements against visual ratings, chlorophyll fluorescence, laboratory chlorophyll tests, and multispectral reflectance sensors. The camera-derived metrics proved highly effective where several predicted visual quality and chlorophyll content as well as, or even better than specialized instruments. We also demonstrated how these measurements can be used in a simple decision-support framework to estimate how management choices affect both turf quality and economic return. Results showed that supplying adequate fertilizer was essential for maintaining turf color, while irrigation could be moderately reduced without major losses in turf quality or value. This research highlights how affordable imaging tools can support practical turfgrass management, offering an accessible alternative to more complex phenotyping technologies.

Technical Abstract: Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce 'Eg, a perceptually anchored CIELAB 'E distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. 'Eg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation ('p2 = 0.63). CIELUV v* explained the greatest model variation (R2m = 0.94) and responded most to fertilizer ('p2 = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = -0.82 with VQ). An illustrative decision-support scenario using 'Eg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by 'Eg, offer a scalable alternative to VQ scoring and spectral systems.