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Specialized crop vegetation analysis

Source: R/02-vegetation-indices.R
analyze_crop_vegetation.Rd

Perform comprehensive vegetation analysis specifically designed for crop monitoring including growth stage detection, stress identification, and yield prediction support. Handles test scenarios properly with better input validation.

Usage

analyze_crop_vegetation(
  spectral_data,
  crop_type = "general",
  growth_stage = "unknown",
  analysis_type = "comprehensive",
  cdl_mask = NULL,
  reference_data = NULL,
  output_folder = NULL,
  verbose = FALSE
)

Arguments

spectral_data

Multi-band spectral data (file, directory, or SpatRaster)

crop_type

Crop type for specialized analysis ("corn", "soybeans", "wheat", "general")

growth_stage

Growth stage if known ("early", "mid", "late", "harvest")

analysis_type

Type of analysis: "comprehensive", "stress", "growth", "yield"

cdl_mask

Optional CDL mask for crop-specific analysis

reference_data

Optional reference data for validation

output_folder

Optional output folder for results

verbose

Print detailed progress

Value

List with comprehensive vegetation analysis results:

  • vegetation_indices: SpatRaster with calculated indices

  • analysis_results: Detailed analysis results by type

  • metadata: Analysis metadata and parameters

Details

Crop-Specific Index Selection:

  • Corn: NDVI, EVI, GNDVI, DVI, RVI, PRI

  • Soybeans: NDVI, EVI, SAVI, GNDVI, PRI

  • Wheat: NDVI, EVI, SAVI, DVI

  • General: NDVI, EVI, SAVI, GNDVI, DVI, RVI

Analysis Types:

  • comprehensive: All analyses (stress, growth, yield)

  • stress: Focus on stress detection indices

  • growth: Growth stage analysis

  • yield: Yield prediction support

Output Structure:

The function returns a list with three main components:

1. vegetation_indices (SpatRaster)

Multi-layer raster with calculated indices (NDVI, EVI, etc.)

2. analysis_results (List)

stress_analysis (if requested):

  • Percentage of pixels in each stress category

  • Categories: healthy (NDVI 0.6-1.0), moderate stress (0.4-0.6), severe stress (0.0-0.4)

  • Includes mean, median, std_dev, and thresholds used

growth_analysis (if requested):

  • Predicted growth stage based on NDVI patterns

  • Stage confidence (0-1 scale)

  • Detailed statistics for each index

  • Growth stages: emergence, vegetative, reproductive, maturity (crop-specific)

yield_analysis (if requested):

  • Composite Yield Index: Normalized 0-1 score combining multiple indices

    • 0.0 = Very low yield potential

    • 0.5 = Medium yield potential

    • 1.0 = Maximum yield potential

  • Yield Potential Class: Categorical (Low, Medium, High, Very High)

  • Index Contributions: How each index contributed to composite score

  • Calculation: Each index (NDVI, EVI, GNDVI, DVI, RVI) is normalized to 0-1, then averaged to create composite score

summary_statistics:

  • Basic stats (mean, std, min, max, percentiles) for all indices

  • Coverage percentage and pixel counts

3. metadata (List)

Processing information: crop_type, indices_used, processing_date, spatial properties

Example Interpretation:

result <- analyze_crop_vegetation(data, crop_type = "corn")

# Stress Assessment
stress <- result$analysis_results$stress_analysis$NDVI
cat(sprintf("Healthy: %.1f%%, Stressed: %.1f%%\n",
            stress$healthy_percentage,
            stress$severe_stress_percentage))

# Growth Stage
stage <- result$analysis_results$growth_analysis$predicted_growth_stage
cat(sprintf("Growth stage: %s\n", stage))

# Yield Potential
yield <- result$analysis_results$yield_analysis
cat(sprintf("Yield potential: %s (score: %.2f)\n",
            yield$yield_potential_class,
            yield$composite_yield_index))

Important Notes:

  • Composite Yield Index is a vegetation-based proxy, not a direct yield prediction

  • Thresholds are based on literature and may need regional calibration

  • Results should be validated with ground truth data

  • For detailed output documentation, see package vignette

Analysis Types:

  • comprehensive: All analyses (stress, growth, yield)

  • stress: Focus on stress detection indices

  • growth: Growth stage analysis

  • yield: Yield prediction support

Examples

if (FALSE) { # \dontrun{
# These examples require actual spectral data
# Comprehensive corn analysis
corn_analysis <- analyze_crop_vegetation(
  spectral_data = sentinel_data,
  crop_type = "corn",
  analysis_type = "comprehensive",
  cdl_mask = corn_mask
)

# Access results
corn_analysis$vegetation_indices        # SpatRaster with indices
corn_analysis$analysis_results$stress_analysis  # Stress detection results
corn_analysis$metadata$indices_used     # Which indices were calculated

# Stress detection in soybeans
stress_analysis <- analyze_crop_vegetation(
  spectral_data = landsat_stack,
  crop_type = "soybeans",
  analysis_type = "stress",
  growth_stage = "mid"
)
} # }

# \donttest{
# Example with mock spectral data
# Create mock multi-band raster (simulating satellite data)
red_band <- terra::rast(nrows = 5, ncols = 5, crs = "EPSG:4326")
nir_band <- terra::rast(nrows = 5, ncols = 5, crs = "EPSG:4326")
terra::values(red_band) <- runif(25, 0.1, 0.3)   # Typical red values
terra::values(nir_band) <- runif(25, 0.4, 0.8)   # Typical NIR values
spectral_stack <- c(red_band, nir_band)
names(spectral_stack) <- c("red", "nir")

# Analyze with mock data
result <- analyze_crop_vegetation(spectral_stack, crop_type = "general")
print(names(result))  # Should show analysis components
#> [1] "vegetation_indices" "analysis_results"   "metadata"          
# }