{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:58:28Z","timestamp":1785473908720,"version":"3.56.0"},"reference-count":49,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Arid Land Agricultural Research Center","award":["CRIS: 2020 21500 001 000D"],"award-info":[{"award-number":["CRIS: 2020 21500 001 000D"]}]},{"award":["CRIS: 2020 21500 001 000D"],"award-info":[{"award-number":["CRIS: 2020 21500 001 000D"]}],"id":[{"id":"https:\/\/ror.org\/03qx0n513","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u0394Eg, a perceptually anchored CIELAB \u0394E 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 \u00d7 3 nitrogen \u00d7 irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon \u00d7 C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. \u0394Eg 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 (\u03b7p2 = 0.63). CIELUV v* explained the greatest model variation (R2m = 0.94) and responded most to fertilizer (\u03b7p2 = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = \u22120.82 with VQ). An illustrative decision-support scenario using \u0394Eg 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 \u0394Eg, offer a scalable alternative to VQ scoring and spectral systems.<\/jats:p>","DOI":"10.3390\/s26154816","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T13:53:31Z","timestamp":1785333211000},"page":"4816","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Proxima Green: RGB Color Metrics for Turfgrass Phenotyping in Controlled Conditions"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5221-7576","authenticated-orcid":false,"given":"Matthew M.","family":"Conley","sequence":"first","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service (USDA ARS), U.S. Arid Land Agricultural Research Center, Maricopa, AZ 85138, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reagan W.","family":"Hejl","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service (USDA ARS), U.S. Arid Land Agricultural Research Center, Maricopa, AZ 85138, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2268-5501","authenticated-orcid":false,"given":"Julia","family":"Farias","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service (USDA ARS), U.S. Arid Land Agricultural Research Center, Maricopa, AZ 85138, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Desalegn D.","family":"Serba","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service (USDA ARS), U.S. Arid Land Agricultural Research Center, Maricopa, AZ 85138, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service (USDA ARS), U.S. Arid Land Agricultural Research Center, Maricopa, AZ 85138, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2554-5061","authenticated-orcid":false,"given":"Clinton F.","family":"Williams","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service (USDA ARS), U.S. Arid Land Agricultural Research Center, Maricopa, AZ 85138, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kior, A., Yudina, L., Zolin, Y., Sukhov, V., and Sukhova, E. 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