Date: 2026-04-28

# RGB Visible Indices for Image Analysis

####

RN Singh
Bappa Das
Sonam
Anil Kumar
Santosha Rathod*

#### 2026-04-28

Corresponding author: santoshagriculture@gmail.com

# 1. Introduction

The `rgbIndices` package provides a comprehensive set of RGB-based indices derived from digital images. These indices are widely used in agriculture, crop phenotyping, vegetation monitoring, and image-based modeling.

The package includes multiple groups of indices such as basic, difference, ratio, normalized difference, vegetation, and color indices.

# 2. RGB Components

An RGB image consists of three channels:

- R: Red (0–255)
- G: Green (0–255)
- B: Blue (0–255)

# 3. Basic Indices

Index | Formula
--- | ---
Normalized Red (r) | R / (R + G + B)
Normalized Green (g) | G / (R + G + B)
Normalized Blue (b) | B / (R + G + B)
Intensity (INT) | (R + G + B) / 3

# 4. Difference Indices

Index | Full Form | Formula
--- | --- | ---
GRD | Green Red Difference | G − R
BGD | Blue Green Difference | B − G
RGD | Red Green Difference | R − G
RBD | Red Blue Difference | R − B
GBD | Green Blue Difference | G − B
BRD | Blue Red Difference | B − R

# 5. Ratio Indices

Index | Full Form | Formula
--- | --- | ---
GRRI | Green Red Ratio Index | G / R
GBRI | Green Blue Ratio Index | G / B
RBRI | Red Blue Ratio Index | R / B
RGRI | Red Green Ratio Index | R / G
BGRI | Blue Green Ratio Index | B / G
BRRI | Blue Red Ratio Index | B / R

# 6. Normalized Difference Indices

Index | Full Form | Formula
--- | --- | ---
NGRDI | Normalized Green Red Difference Index | (G − R) / (R + G + B)
NRGDI | Normalized Red Green Difference Index | (R − G) / (R + G + B)
NBRDI | Normalized Blue Red Difference Index | (B − R) / (R + G + B)
NRBDI | Normalized Red Blue Difference Index | (R − B) / (R + G + B)
NGBDI | Normalized Green Blue Difference Index | (G − B) / (R + G + B)
NBGDI | Normalized Blue Green Difference Index | (B − G) / (R + G + B)

Note: Some normalized difference indices are sign-inverted counterparts of each other (e.g., NGRDI vs NRGDI).

# 7. Vegetation Indices

Index | Full Form | Formula
--- | --- | ---
WI | Woebbecke Index | (G − B) / (R − G)
GRVI | Green Red Vegetation Index | (G − R) / (G + R)
IKAW | Kawashima Index | (R − B) / (R + B)
NDTI | Normalized Difference Turbidity Index | (R − G) / (R + G)
GBI | Green Blue Index | (G − B) / (G + B)
GLI | Green Leaf Index | (2G − R − B) / (2G + R + B)
VARI | Visible Atmospherically Resistant Index | (G − R) / (G + R − B)
NDI | Normalized Difference Index | (g − r) / (g + r)
ExG | Excess Green Index | 2g − r − b
ExR | Excess Red Index | 1.4r − g
ExGR | Excess Green minus Red | 3g − 2.4r − b
MxEG | Modified Excess Green | 1.262G − 0.884R − 0.311B
ExB | Excess Blue | 1.4b − g
RGBVI | RGB Vegetation Index | (G² − RB) / (G² + RB)

# 8. Color Indices

Index | Full Form | Formula
--- | --- | ---
Grey | Gray Intensity | 0.2898r + 0.5870g + 0.1140b
BI | Brightness Index | √((R² + G² + B²)/3)
HI | Hue Index | (2R − G − B) / (G − B)
RI | Redness Index | R² / (B × G³)
SI | Saturation Index | 2(R − G − B) / (G − B)
CI | Coloration Index | (R − B) / R
CIVE | Color Index of Vegetation | 0.441R − 0.811G + 0.385B + 18.78745
VEG | Vegetative Index | G / (R^0.667 × B^0.333)
SAT | Overall Saturation Index | (
OHI | Overall Hue Index | atan(2(R − G − B)/(30.5(G − B)))
TCVI | True Color Vegetation Index | 1.4(2R − 2B)/(2R − G − 2B + 255×0.4)

# 9. Example Workflow

    library(rgbIndices)
    library(raster)

    ## Loading required package: sp

    # ---------------------------
    # Example
    # ---------------------------
    set.seed(123)
    r <- raster::raster(matrix(runif(30*30), 30, 30))
    g <- raster::raster(matrix(runif(30*30), 30, 30))
    b <- raster::raster(matrix(runif(30*30), 30, 30))
    img <- raster::stack(r, g, b)

    # Compute indices
    idx  <- rgb_basic(img)
    idx1 <- rgb_diff(img)
    idx2 <- rgb_ratio(img)
    idx3 <- rgb_normdiff(img)
    idx4 <- rgb_veg(img)
    idx5 <- rgb_color(img)

    # Summary statistics
    rgb_indices_to_mean(idx)

    ## # A tibble: 1 × 7
    ##       R     G     B     r     g     b   INT
    ##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    ## 1 0.496 0.500 0.493 0.334 0.336 0.330 0.497

    # Convert to table
    tbl <- rgb_indices_to_tbl(idx)
    head(tbl)

    ## # A tibble: 6 × 7
    ##       R      G     B     r     g      b   INT
    ##   <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>
    ## 1 0.288 0.924  0.155 0.210 0.676 0.114  0.456
    ## 2 0.963 0.887  0.864 0.355 0.327 0.318  0.904
    ## 3 0.665 0.508  0.575 0.380 0.291 0.329  0.583
    ## 4 0.131 0.0872 0.541 0.172 0.115 0.713  0.253
    ## 5 0.648 0.461  0.784 0.342 0.244 0.414  0.631
    ## 6 0.847 0.709  0.136 0.501 0.419 0.0804 0.564

# —————————

# Real image example

# —————————

    img_real <- raster::stack(rgb_example())
    raster::plotRGB(img_real)
    rgb_basic(img_real)

# 10. Applications

- Crop health monitoring
- Disease detection
- Plant phenotyping
- Precision agriculture
- Weed detection and classification

# 11. Reference

Singh, R. N., Krishnan, P., Singh, V. K., & Das, B. (2023).
Estimation of yellow rust severity in wheat using visible and thermal imaging coupled with machine learning models.
Geocarto International.
https://www.tandfonline.com/doi/full/10.1080/10106049.2022.2160831