analyze_crop_vegetation: Specialized crop vegetation analysis

In geospatialsuite: Comprehensive Geospatiotemporal Analysis and Multimodal Integration Toolkit

Specialized crop vegetation analysis

Description

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

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

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

Examples

## Not run: 
# 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"
)

## End(Not run)


# 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

geospatialsuite documentation built on Nov. 6, 2025, 1:06 a.m.