RGB Visible Indices for Image Analysis

Computes RGB-based vegetation, color, and spectral indices from digital images for applications in agriculture, crop phenotyping, and remote sensing. The methods are based on digital image processing and plant phenotyping approaches (Singh et al. (2023) ).

rgbIndices

An R package for computing RGB-based vegetation and color indices from digital images.

Installation

devtools::install_local("rgbIndices")

Example

library(rgbIndices)
library(raster)

# ---------------------------
# Fast example (CRAN-safe)
# ---------------------------
r <- raster(matrix(runif(30*30), 30, 30))
g <- raster(matrix(runif(30*30), 30, 30))
b <- raster(matrix(runif(30*30), 30, 30))
img <- 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)

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

# ---------------------------
# Real image example
# ---------------------------
img_real <- stack(rgb_example())
plotRGB(img_real)
rgb_basic(img_real)

Applications

• Crop phenotyping
• Disease detection
• Precision agriculture
• Image-based modeling

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

Authors

RN Singh Bappa Das Sonam Anil Kumar Santosha Rathod (Maintainer)

Email: [email protected]