Color‐distance modeling improves differentiation of colors in digital images of hybrid bermudagrass

Abstract

Identifying and reporting color differences is important in turfgrass experimentation. Finding ways to improve color differentiation may be desirable. Research was conducted to determine if color‐distance modeling (∆E*ₐb) improves differentiation of color in images of hybrid bermudagrass [Cynodon dactylon (L.) Pers. × Cynodon transvaalensis Burtt‐Davy]. Colors differ perceptibly if ∆E*ₐb >1.0; magnitude of difference increases as ∆E*ₐb increases. Hue angle (θ) and dark green color index (DGCI) of untreated shoots; shoots treated with petroleum hydrocarbons; and turf treated with N, herbicides, or a growth regulator were determined by digital image analysis. Hue and DGCI means were separated using orthogonal contrasts; ∆E*ₐb was computed for each contrast. Delta E*ₐb was a good predictor of color difference magnitude. Association between p‐values for contrasts of θ or DGCI and ∆E*ₐb were strong; smaller p‐values corresponded to larger ∆E*ₐb. When log₁₀ p‐values for θ contrasts for ‘Tifway’ turf treated with N, herbicides, and a growth regulator were regressed on ∆E*ₐb, r² = .9657 (y = − 0.602 − 0.9512x); for DGCI, r² = .8743 (y = − 1.6097 − 0.8743x). Computing ∆E*ₐb helped avoid making false conclusions. Neither θ nor DGCI differed for Tifway treated with mesotrione or glyphosate (p = .2049, .6195) but ∆E*ₐb was 3.8 due to differences in saturation (p = .0137) and brightness (p = .0067). Computing ∆E*ₐb improved differentiation of turfgrass color by enhancing the accuracy of difference detection. Improving the ability to differentiate colors and avoidance of making false conclusions has advanced reporting of turfgrass color data.

Plant phenotyping relevance

デジタル画像から芝草の色という植物形質を抽出し、色差モデルの有効性と測定精度を検証することが研究の中心であるため、植物フェノタイピング手法に該当する。

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