# Agricultural Applications with geospatialsuite

#### geospatialsuite Development Team

# Agricultural Applications with geospatialsuite

This vignette demonstrates comprehensive agricultural analysis capabilities including USDA CDL integration, crop monitoring, stress detection, and yield assessment workflows.

## Enhanced NDVI Analysis for Agriculture

### Time Series NDVI Monitoring

Use enhanced NDVI calculation for multi-temporal crop monitoring:

    # Enhanced NDVI with quality filtering
    ndvi_enhanced <- calculate_ndvi_enhanced(
      red_data = red_band,
      nir_data = nir_band,
      quality_filter = TRUE,
      temporal_smoothing = FALSE,  # Single date
      verbose = TRUE
    )

    # Visualize enhanced NDVI
    create_ndvi_map(
      ndvi_data = ndvi_enhanced,
      title = "Enhanced NDVI with Quality Filtering",
      ndvi_classes = "none"
    )

    print("Enhanced NDVI Analysis:")
    ndvi_values <- terra::values(ndvi_enhanced, mat = FALSE)
    valid_ndvi <- ndvi_values[!is.na(ndvi_values)]
    print(paste("Mean NDVI:", round(mean(valid_ndvi), 3)))
    print(paste("NDVI range:", paste(round(range(valid_ndvi), 3), collapse = " - ")))

### Multi-Index Crop Assessment

Calculate multiple vegetation indices for comprehensive crop assessment:

    # Calculate multiple indices relevant to agriculture
    agricultural_indices <- calculate_multiple_indices(
      red = red_band,
      nir = nir_band,
      indices = c("NDVI", "SAVI", "MSAVI", "DVI", "RVI"),
      output_stack = TRUE,
      verbose = TRUE
    )

    print("Agricultural Indices Calculated:")
    print(names(agricultural_indices))

    # Visualize key indices
    if (terra::nlyr(agricultural_indices) >= 2) {
      # NDVI visualization
      create_spatial_map(
        spatial_data = agricultural_indices[[1]],
        title = "NDVI for Agricultural Assessment",
        color_scheme = "ndvi"
      )

      # SAVI visualization (soil-adjusted)
      if ("SAVI" %in% names(agricultural_indices)) {
        plot_raster_fast(
          agricultural_indices[["SAVI"]],
          title = "SAVI (Soil Adjusted Vegetation Index)",
          color_scheme = "viridis"
        )
      }
    }

## Yield Prediction Support

### Yield Potential Assessment

Assess yield potential using vegetation indices:

    # Extract yield analysis if available
    if (!is.null(crop_analysis$analysis_results$yield_analysis)) {
      yield_results <- crop_analysis$analysis_results$yield_analysis

      print("Yield Potential Analysis:")
      if (!"error" %in% names(yield_results)) {
        print(paste("Composite yield index:", round(yield_results$composite_yield_index, 3)))
        print(paste("Yield potential class:", yield_results$yield_potential_class))
        print(paste("Classification confidence:", round(yield_results$classification_confidence, 3)))

        print("Index contributions to yield assessment:")
        for (idx in names(yield_results$index_contributions)) {
          contrib <- yield_results$index_contributions[[idx]]
          print(paste("  ", idx, "- Normalized:", round(contrib$mean_normalized, 3),
                      "Raw mean:", round(contrib$raw_mean, 3)))
        }
      }
    }

    # Create yield potential map visualization
    yield_potential_raster <- agricultural_indices[[1]]  # Use NDVI as proxy
    terra::values(yield_potential_raster) <- ifelse(terra::values(yield_potential_raster) > 0.6, 3,
                                                   ifelse(terra::values(yield_potential_raster) > 0.4, 2, 1))
    names(yield_potential_raster) <- "Yield_Potential"

    plot_raster_fast(
      yield_potential_raster,
      title = "Yield Potential Classification",
      color_scheme = "terrain"
    )

### Crop Performance Metrics

Calculate key performance indicators for agricultural monitoring:

    # Calculate comprehensive vegetation statistics
    if (inherits(agricultural_indices, "SpatRaster")) {
      veg_stats <- list()

      for (i in 1:terra::nlyr(agricultural_indices)) {
        index_name <- names(agricultural_indices)[i]
        values <- terra::values(agricultural_indices[[i]], mat = FALSE)
        valid_values <- values[!is.na(values)]

        if (length(valid_values) > 0) {
          veg_stats[[index_name]] <- list(
            mean = mean(valid_values),
            median = median(valid_values),
            sd = sd(valid_values),
            cv = sd(valid_values) / mean(valid_values),
            min = min(valid_values),
            max = max(valid_values),
            q25 = quantile(valid_values, 0.25),
            q75 = quantile(valid_values, 0.75)
          )
        }
      }

      print("Crop Performance Metrics:")
      for (index in names(veg_stats)) {
        stats <- veg_stats[[index]]
        print(paste(index, "- Mean:", round(stats$mean, 3),
                    "CV:", round(stats$cv, 3),
                    "Range:", paste(round(c(stats$min, stats$max), 3), collapse = "-")))
      }
    }

## Precision Agriculture Applications

### Field-Level Analysis

Analyze individual fields or management zones:

    # Create sample field boundaries
    field_1 <- sf::st_polygon(list(matrix(c(
      -83.5, 40.0, -83.3, 40.0, -83.3, 40.2, -83.5, 40.2, -83.5, 40.0
    ), ncol = 2, byrow = TRUE)))

    field_2 <- sf::st_polygon(list(matrix(c(
      -83.3, 40.0, -83.1, 40.0, -83.1, 40.2, -83.3, 40.2, -83.3, 40.0
    ), ncol = 2, byrow = TRUE)))

    fields_sf <- sf::st_sf(
      field_id = c("Field_1", "Field_2"),
      crop_type = c("Corn", "Soybeans"),
      geometry = sf::st_sfc(field_1, field_2, crs = 4326)
    )

    # Extract vegetation statistics by field using spatial join
    field_analysis <- universal_spatial_join(
      source_data = agricultural_indices,
      target_data = fields_sf,
      method = "zonal",
      summary_function = "mean",
      verbose = TRUE
    )

    print("Field-Level Analysis Results:")
    if (inherits(field_analysis, "sf")) {
      print(sf::st_drop_geometry(field_analysis))
    }

### Variable Rate Application Support

Support precision agriculture through spatial variability analysis:

    # Calculate coefficient of variation for management zones
    if (inherits(agricultural_indices, "SpatRaster") && terra::nlyr(agricultural_indices) > 0) {
      # Use NDVI for variability analysis
      ndvi_layer <- agricultural_indices[[1]]

      # Calculate local variability using focal statistics
      local_cv <- terra::focal(ndvi_layer, w = matrix(1, 3, 3),
                               fun = function(x) sd(x, na.rm = TRUE) / mean(x, na.rm = TRUE))
      names(local_cv) <- "Local_CV"

      # Classify variability zones
      variability_zones <- terra::classify(local_cv,
                                           matrix(c(0, 0.1, 1,     # Low variability
                                                   0.1, 0.2, 2,    # Medium variability
                                                   0.2, 1, 3),     # High variability
                                                  ncol = 3, byrow = TRUE))
      names(variability_zones) <- "Variability_Zones"

      # Visualize variability
      plot_raster_fast(
        local_cv,
        title = "Spatial Variability (Coefficient of Variation)",
        color_scheme = "plasma"
      )

      plot_raster_fast(
        variability_zones,
        title = "Management Zones (1=Low, 2=Medium, 3=High Variability)",
        color_scheme = "categorical"
      )
    }

## Crop Health Monitoring

### Disease and Stress Detection

Use vegetation indices to detect crop stress and disease:

    # Create stress detection workflow
    stress_indices <- c("NDVI", "SAVI", "DVI")

    # Calculate stress thresholds based on crop type
    corn_thresholds <- list(
      healthy = list(NDVI = c(0.6, 1.0), SAVI = c(0.4, 0.8)),
      stressed = list(NDVI = c(0.3, 0.6), SAVI = c(0.2, 0.4)),
      severely_stressed = list(NDVI = c(0.0, 0.3), SAVI = c(0.0, 0.2))
    )

    # Apply stress classification
    if (inherits(agricultural_indices, "SpatRaster") && "NDVI" %in% names(agricultural_indices)) {
      ndvi_values <- terra::values(agricultural_indices[["NDVI"]], mat = FALSE)

      # Classify stress levels
      stress_classification <- ifelse(ndvi_values >= 0.6, "Healthy",
                                     ifelse(ndvi_values >= 0.3, "Moderate Stress", "Severe Stress"))

      # Create stress map
      stress_raster <- agricultural_indices[["NDVI"]]
      stress_numeric <- ifelse(ndvi_values >= 0.6, 3,
                              ifelse(ndvi_values >= 0.3, 2, 1))
      terra::values(stress_raster) <- stress_numeric
      names(stress_raster) <- "Stress_Level"

      plot_raster_fast(
        stress_raster,
        title = "Crop Stress Classification (1=Severe, 2=Moderate, 3=Healthy)",
        color_scheme = "terrain"
      )

      # Calculate stress statistics
      stress_table <- table(stress_classification)
      stress_percent <- round(prop.table(stress_table) * 100, 1)

      print("Crop Stress Distribution:")
      for (level in names(stress_table)) {
        print(paste(level, ":", stress_table[[level]], "pixels (", stress_percent[[level]], "%)"))
      }
    }

### Early Warning Systems

Create early warning systems for crop management:

    # Define warning thresholds
    warning_thresholds <- list(
      drought_stress = 0.4,    # NDVI below this indicates drought stress
      disease_risk = 0.3,      # NDVI below this indicates disease risk
      optimal_growth = 0.7     # NDVI above this indicates optimal conditions
    )

    # Generate alerts
    if (exists("ndvi_values")) {
      alerts <- list()

      # Calculate percentages in each category
      drought_pixels <- sum(ndvi_values < warning_thresholds$drought_stress, na.rm = TRUE)
      disease_pixels <- sum(ndvi_values < warning_thresholds$disease_risk, na.rm = TRUE)
      optimal_pixels <- sum(ndvi_values > warning_thresholds$optimal_growth, na.rm = TRUE)
      total_pixels <- sum(!is.na(ndvi_values))

      alerts$drought_risk <- round((drought_pixels / total_pixels) * 100, 1)
      alerts$disease_risk <- round((disease_pixels / total_pixels) * 100, 1)
      alerts$optimal_conditions <- round((optimal_pixels / total_pixels) * 100, 1)

      print("Agricultural Alert System:")
      print(paste("Drought stress risk:", alerts$drought_risk, "% of field"))
      print(paste("Disease risk:", alerts$disease_risk, "% of field"))
      print(paste("Optimal conditions:", alerts$optimal_conditions, "% of field"))

      # Generate recommendations
      if (alerts$drought_risk > 20) {
        print("RECOMMENDATION: Consider irrigation scheduling")
      }
      if (alerts$disease_risk > 10) {
        print("RECOMMENDATION: Increase disease monitoring")
      }
      if (alerts$optimal_conditions > 70) {
        print("STATUS: Crop conditions are generally favorable")
      }
    }

## Seasonal Crop Monitoring

### Multi-Temporal Analysis

Track crop development throughout the growing season:

    # Simulate time series data for growing season
    growth_stages <- c("Planting", "V6", "V12", "R1", "R3", "R6")
    ndvi_progression <- c(0.2, 0.4, 0.7, 0.8, 0.75, 0.6)

    # Create time series visualization concept
    seasonal_data <- data.frame(
      Stage = growth_stages,
      NDVI = ndvi_progression,
      DOY = c(120, 150, 180, 200, 220, 260)  # Day of year
    )

    print("Seasonal NDVI Progression:")
    print(seasonal_data)

    # Create conceptual growth curve
    if (requireNamespace("ggplot2", quietly = TRUE)) {
      library(ggplot2)

      growth_plot <- ggplot(seasonal_data, aes(x = DOY, y = NDVI)) +
        geom_line(color = "darkgreen", size = 1.2) +
        geom_point(color = "red", size = 3) +
        geom_text(aes(label = Stage), vjust = -0.5) +
        labs(title = "Typical Corn NDVI Progression",
             x = "Day of Year",
             y = "NDVI Value") +
        theme_minimal() +
        ylim(0, 1)

      print(growth_plot)
    }

### Harvest Timing Optimization

Use vegetation indices to optimize harvest timing:

    # Define maturity indicators based on NDVI decline
    maturity_thresholds <- list(
      corn = list(
        early_maturity = 0.7,     # NDVI starts declining
        harvest_ready = 0.5,      # Optimal harvest window
        post_harvest = 0.3        # Past optimal timing
      ),
      soybeans = list(
        early_maturity = 0.6,
        harvest_ready = 0.4,
        post_harvest = 0.25
      )
    )

    # Calculate harvest readiness
    if (exists("ndvi_values")) {
      # Use corn thresholds for demonstration
      thresholds <- maturity_thresholds$corn

      harvest_assessment <- list(
        early_maturity = sum(ndvi_values <= thresholds$early_maturity &
                            ndvi_values > thresholds$harvest_ready, na.rm = TRUE),
        harvest_ready = sum(ndvi_values <= thresholds$harvest_ready &
                           ndvi_values > thresholds$post_harvest, na.rm = TRUE),
        post_optimal = sum(ndvi_values <= thresholds$post_harvest, na.rm = TRUE),
        still_growing = sum(ndvi_values > thresholds$early_maturity, na.rm = TRUE)
      )

      total_pixels <- sum(!is.na(ndvi_values))

      print("Harvest Timing Assessment:")
      for (stage in names(harvest_assessment)) {
        percentage <- round((harvest_assessment[[stage]] / total_pixels) * 100, 1)
        print(paste(stage, ":", harvest_assessment[[stage]], "pixels (", percentage, "%)"))
      }
    }

## Water Management for Agriculture

### Irrigation Needs Assessment

Use water indices to assess irrigation requirements:

    # Calculate water-related indices
    green_band <- red_band
    terra::values(green_band) <- runif(2500, 0.1, 0.2)
    names(green_band) <- "Green"

    # Calculate NDWI for water stress detection
    ndwi <- calculate_water_index(
      green = green_band,
      nir = nir_band,
      index_type = "NDWI",
      verbose = TRUE
    )

    # Irrigation needs based on NDWI
    ndwi_values <- terra::values(ndwi, mat = FALSE)
    valid_ndwi <- ndwi_values[!is.na(ndwi_values)]

    if (length(valid_ndwi) > 0) {
      irrigation_zones <- ifelse(valid_ndwi < -0.3, "High Need",
                               ifelse(valid_ndwi < -0.1, "Moderate Need", "Adequate"))

      irrigation_table <- table(irrigation_zones)
      irrigation_percent <- round(prop.table(irrigation_table) * 100, 1)

      print("Irrigation Needs Assessment:")
      for (zone in names(irrigation_table)) {
        print(paste(zone, ":", irrigation_table[[zone]], "pixels (", irrigation_percent[[zone]], "%)"))
      }

      # Visualize irrigation zones
      irrigation_raster <- ndwi
      terra::values(irrigation_raster) <- as.numeric(as.factor(irrigation_zones))
      names(irrigation_raster) <- "Irrigation_Needs"

      plot_raster_fast(
        irrigation_raster,
        title = "Irrigation Needs Assessment (1=Adequate, 2=High, 3=Moderate)",
        color_scheme = "water"
      )
    }

## Crop Rotation Analysis

### Multi-Year Crop Tracking

Analyze crop rotation patterns using multi-temporal CDL data:

    # Simulate multi-year CDL data
    create_rotation_analysis <- function() {
      # Create sample rotation data
      rotation_data <- data.frame(
        year = rep(2021:2023, each = 4),
        field = rep(c("Field_A", "Field_B", "Field_C", "Field_D"), 3),
        crop = c(
          "Corn", "Soybeans", "Corn", "Wheat",      # 2021
          "Soybeans", "Corn", "Soybeans", "Corn",   # 2022
          "Corn", "Soybeans", "Corn", "Soybeans"   # 2023
        ),
        yield = runif(12, 80, 200)
      )

      return(rotation_data)
    }

    rotation_analysis <- create_rotation_analysis()
    print("Crop Rotation Analysis:")
    print(rotation_analysis)

    # Analyze rotation patterns
    rotation_patterns <- list()
    fields <- unique(rotation_analysis$field)

    for (field in fields) {
      field_data <- rotation_analysis[rotation_analysis$field == field, ]
      rotation_patterns[[field]] <- paste(field_data$crop, collapse = " Ã¢â€ â€™ ")
    }

    print("Rotation Patterns:")
    for (field in names(rotation_patterns)) {
      print(paste(field, ":", rotation_patterns[[field]]))
    }

### Sustainable Agriculture Metrics

Calculate sustainability indicators:

    # Calculate diversity index for crop rotation
    calculate_crop_diversity <- function(rotation_data) {
      diversity_scores <- list()

      for (field in unique(rotation_data$field)) {
        field_crops <- rotation_data$crop[rotation_data$field == field]
        unique_crops <- length(unique(field_crops))
        total_years <- length(field_crops)

        # Simple diversity score (0-1, higher = more diverse)
        diversity_scores[[field]] <- unique_crops / total_years
      }

      return(diversity_scores)
    }

    diversity_scores <- calculate_crop_diversity(rotation_analysis)

    print("Crop Diversity Scores (Higher = More Diverse):")
    for (field in names(diversity_scores)) {
      print(paste(field, ":", round(diversity_scores[[field]], 2)))
    }

    # Sustainability recommendations
    avg_diversity <- mean(unlist(diversity_scores))
    print(paste("Average field diversity:", round(avg_diversity, 2)))

    if (avg_diversity < 0.5) {
      print("RECOMMENDATION: Consider more diverse crop rotations")
    } else if (avg_diversity > 0.8) {
      print("STATUS: Good crop diversity maintained")
    }

## Integration with Farm Management

### Data Export for Farm Software

Prepare analysis results for integration with farm management systems:

    # Create farm-ready data export
    create_farm_export <- function(analysis_results, field_boundaries) {
      # Compile key metrics for farm management
      farm_data <- list(
        summary_statistics = list(),
        recommendations = list(),
        alerts = list()
      )

      # Extract key metrics
      if (exists("veg_stats") && length(veg_stats) > 0) {
        farm_data$summary_statistics <- veg_stats
      }

      # Generate management recommendations
      if (exists("alerts")) {
        if (alerts$drought_risk > 20) {
          farm_data$recommendations <- c(farm_data$recommendations,
                                       "Increase irrigation frequency")
        }
        if (alerts$disease_risk > 15) {
          farm_data$recommendations <- c(farm_data$recommendations,
                                       "Monitor for disease symptoms")
        }
      }

      return(farm_data)
    }

    # Export field-level results
    if (exists("field_analysis") && inherits(field_analysis, "sf")) {
      field_summary <- sf::st_drop_geometry(field_analysis)

      # Add coordinates for GPS guidance
      field_centroids <- sf::st_centroid(field_analysis)
      field_coords <- sf::st_coordinates(field_centroids)
      field_summary$centroid_lon <- field_coords[, 1]
      field_summary$centroid_lat <- field_coords[, 2]

      print("Field Summary for Farm Management:")
      print(field_summary)
    }

### Economic Analysis Support

Calculate economic indicators from vegetation analysis:

    # Estimate economic value based on vegetation health
    calculate_economic_metrics <- function(ndvi_data, crop_prices) {
      if (!exists("ndvi_values")) return(NULL)

      # Simplified yield prediction based on NDVI
      # (In practice, would use crop-specific models)
      predicted_yield <- ifelse(ndvi_values > 0.7, "High",
                               ifelse(ndvi_values > 0.5, "Medium", "Low"))

      yield_table <- table(predicted_yield)

      # Economic projections (simplified)
      economic_zones <- list(
        high_yield = sum(predicted_yield == "High", na.rm = TRUE),
        medium_yield = sum(predicted_yield == "Medium", na.rm = TRUE),
        low_yield = sum(predicted_yield == "Low", na.rm = TRUE)
      )

      return(economic_zones)
    }

    # Calculate economic zones
    if (exists("ndvi_values")) {
      economic_analysis <- calculate_economic_metrics(ndvi_values,
                                                      list(corn = 5.50, soybeans = 13.00))

      if (!is.null(economic_analysis)) {
        total_pixels <- sum(unlist(economic_analysis))

        print("Economic Potential Analysis:")
        for (zone in names(economic_analysis)) {
          pixels <- economic_analysis[[zone]]
          percentage <- round((pixels / total_pixels) * 100, 1)
          print(paste(zone, ":", pixels, "pixels (", percentage, "%)"))
        }
      }
    }

## Quality Assurance and Validation

### Data Quality Checks

Implement quality assurance for agricultural monitoring:

    # Vegetation index quality assessment
    quality_check <- function(vegetation_indices) {
      qc_results <- list()

      if (inherits(vegetation_indices, "SpatRaster")) {
        for (i in 1:terra::nlyr(vegetation_indices)) {
          index_name <- names(vegetation_indices)[i]
          values <- terra::values(vegetation_indices[[i]], mat = FALSE)

          qc_results[[index_name]] <- list(
            total_pixels = length(values),
            valid_pixels = sum(!is.na(values)),
            coverage_percent = round((sum(!is.na(values)) / length(values)) * 100, 1),
            value_range = range(values, na.rm = TRUE),
            outliers = sum(values < -1 | values > 1, na.rm = TRUE)  # For normalized indices
          )
        }
      }

      return(qc_results)
    }

    # Perform quality check
    if (exists("agricultural_indices")) {
      qc_results <- quality_check(agricultural_indices)

      print("Data Quality Assessment:")
      for (index in names(qc_results)) {
        qc <- qc_results[[index]]
        print(paste(index, "- Coverage:", qc$coverage_percent, "%, Outliers:", qc$outliers))
      }
    }

### Field Validation Support

Support ground truth collection and validation:

    # Create sampling points for field validation
    create_validation_points <- function(field_boundary, n_points = 10) {
      # Generate random points within field boundary
      if (inherits(field_boundary, "sf")) {
        sample_points <- sf::st_sample(field_boundary, n_points)
        validation_sf <- sf::st_sf(
          point_id = paste0("VP_", 1:length(sample_points)),
          validation_type = "Ground_Truth",
          geometry = sample_points
        )
        return(validation_sf)
      }
      return(NULL)
    }

    # Create validation points for first field
    if (exists("fields_sf")) {
      validation_points <- create_validation_points(fields_sf[1, ], n_points = 5)

      if (!is.null(validation_points)) {
        print("Validation Points Created:")
        print(sf::st_drop_geometry(validation_points))

        # Extract vegetation index values at validation points
        if (exists("agricultural_indices")) {
          validation_extracted <- universal_spatial_join(
            source_data = validation_points,
            target_data = agricultural_indices,
            method = "extract",
            verbose = FALSE
          )

          print("Extracted Values at Validation Points:")
          validation_summary <- sf::st_drop_geometry(validation_extracted)
          print(head(validation_summary))
        }
      }
    }

## Advanced Agricultural Workflows

### Complete Farm Analysis Pipeline

Demonstrate a complete agricultural analysis workflow:

    # Complete agricultural analysis workflow
    run_agricultural_workflow <- function(spectral_data, cdl_data = NULL,
                                         region_boundary = NULL) {
      workflow_results <- list()

      # Step 1: Calculate vegetation indices
      message("Step 1: Calculating vegetation indices...")
      indices <- calculate_multiple_indices(
        spectral_data = spectral_data,
        indices = c("NDVI", "SAVI", "DVI"),
        output_stack = TRUE
      )
      workflow_results$vegetation_indices <- indices

      # Step 2: Crop classification (if CDL available)
      if (!is.null(cdl_data)) {
        message("Step 2: Analyzing crop distribution...")
        crop_mask <- create_crop_mask(cdl_data, "corn")
        workflow_results$crop_mask <- crop_mask
      }

      # Step 3: Stress assessment
      message("Step 3: Assessing crop stress...")
      if (inherits(indices, "SpatRaster") && "NDVI" %in% names(indices)) {
        ndvi_vals <- terra::values(indices[["NDVI"]], mat = FALSE)
        stress_percent <- sum(ndvi_vals < 0.5, na.rm = TRUE) / sum(!is.na(ndvi_vals)) * 100
        workflow_results$stress_assessment <- round(stress_percent, 1)
      }

      # Step 4: Generate recommendations
      message("Step 4: Generating management recommendations...")
      recommendations <- character()

      if (!is.null(workflow_results$stress_assessment)) {
        if (workflow_results$stress_assessment > 25) {
          recommendations <- c(recommendations, "High stress detected - investigate causes")
        }
        if (workflow_results$stress_assessment < 10) {
          recommendations <- c(recommendations, "Crop conditions appear favorable")
        }
      }

      workflow_results$recommendations <- recommendations

      return(workflow_results)
    }

    # Run the workflow
    workflow_output <- run_agricultural_workflow(
      spectral_data = spectral_stack
    )

    print("Complete Agricultural Workflow Results:")
    print(paste("Stress assessment:", workflow_output$stress_assessment, "% of area"))
    print("Recommendations:")
    for (rec in workflow_output$recommendations) {
      print(paste("-", rec))
    }

## Summary and Best Practices

### Key Agricultural Functions

geospatialsuite provides comprehensive agricultural analysis through:

1. CDL Integration: `get_comprehensive_cdl_codes()`, `analyze_cdl_crops_dynamic()`
2. Crop Monitoring: `analyze_crop_vegetation()`, `calculate_ndvi_enhanced()`
3. Stress Detection: Multi-index analysis with threshold classification
4. Water Management: `calculate_water_index()` for irrigation planning
5. Yield Assessment: Composite index calculations and trend analysis

### Best Practices for Agricultural Applications

    # 1. Always validate your data
    print("Data Validation Checklist:")
    print("Check coordinate reference systems")
    print("Verify date ranges match growing season")
    print("Validate vegetation index ranges")
    print("Confirm crop mask accuracy")

    # 2. Use appropriate indices for your crop type
    crop_index_recommendations <- list(
      corn = c("NDVI", "EVI2", "SAVI", "DVI"),
      soybeans = c("NDVI", "SAVI", "GNDVI", "DVI"),
      wheat = c("NDVI", "SAVI", "MSAVI"),
      general = c("NDVI", "SAVI", "DVI", "RVI")
    )

    print("Recommended indices by crop type:")
    for (crop in names(crop_index_recommendations)) {
      indices <- crop_index_recommendations[[crop]]
      print(paste(crop, ":", paste(indices, collapse = ", ")))
    }

    # 3. Monitor throughout growing season
    print("Seasonal Monitoring Schedule:")
    print("- Planting: Establish baseline measurements")
    print("- Early season: Monitor emergence and establishment")
    print("- Mid-season: Peak growth assessment and stress detection")
    print("- Late season: Maturity evaluation and harvest timing")
    print("- Post-harvest: Residue analysis and planning")

### Integration with Precision Agriculture

    # Precision agriculture workflow components
    precision_ag_components <- list(
      data_collection = c("Satellite imagery", "UAV surveys", "Ground sensors"),
      analysis_methods = c("Vegetation indices", "Stress detection", "Yield mapping"),
      decision_support = c("Variable rate applications", "Irrigation scheduling", "Harvest timing"),
      validation = c("Ground truth sampling", "Yield monitoring", "Economic analysis")
    )

    print("Precision Agriculture Integration:")
    for (component in names(precision_ag_components)) {
      print(paste(component, ":", paste(precision_ag_components[[component]], collapse = ", ")))
    }

## Understanding analyze_crop_vegetation() Outputs

### Output Structure Overview

The function returns three main components:

1. vegetation_indices: Raster layers with calculated indices
2. analysis_results: Detailed analysis (stress, growth, yield)
3. metadata: Processing information

### Interpreting Stress Analysis

Stress detection classifies pixels into three categories:

Stress Level | NDVI Range | Interpretation
--- | --- | ---
Healthy | 0.6 - 1.0 | Normal vegetation growth
Moderate Stress | 0.4 - 0.6 | Some stress present
Severe Stress | 0.0 - 0.4 | Significant stress

    result <- analyze_crop_vegetation(data, analysis_type = "stress")
    stress <- result$analysis_results$stress_analysis$NDVI

    # What percentage of my field needs attention?
    cat(sprintf("%.1f%% of field shows stress\n",
                stress$moderate_stress_percentage + stress$severe_stress_percentage))

### Understanding Composite Yield Index

The composite yield index is a 0-1 normalized score that combines multiple vegetation indices:

How it’s calculated: 1. Multiple indices (NDVI, EVI, GNDVI, DVI, RVI) are normalized to 0-1 2. Normalized values are averaged 3. Result indicates relative yield potential

Interpretation: - 0.0-0.3: Low yield potential - 0.3-0.6: Medium yield potential - 0.6-0.8: High yield potential - 0.8-1.0: Very high yield potential

Important caveats: - This is a relative measure, not absolute yield - Should be calibrated with actual yield data - Works best for within-field comparisons - Correlation with actual yield varies by crop and region

    result <- analyze_crop_vegetation(data, crop_type = "corn", analysis_type = "yield")
    yield <- result$analysis_results$yield_analysis

    cat(sprintf("Yield Potential: %s\n", yield$yield_potential_class))
    cat(sprintf("Composite Score: %.2f\n", yield$composite_yield_index))

    # See which indices contributed
    for (idx in names(yield$index_contributions)) {
      contrib <- yield$index_contributions[[idx]]
      cat(sprintf("  %s: %.3f\n", idx, contrib$mean_normalized))
    }

### Growth Stage Predictions

Growth stage is predicted from NDVI patterns:

Crop | Stage | NDVI Range
--- | --- | ---
Corn | Emergence | < 0.3
Corn | Vegetative | 0.3 - 0.6
Corn | Reproductive | 0.6 - 0.8
Corn | Maturity | > 0.8

    result <- analyze_crop_vegetation(data, crop_type = "soybeans", analysis_type = "growth")
    growth <- result$analysis_results$growth_analysis

    cat(sprintf("Predicted stage: %s\n", growth$predicted_growth_stage))
    cat(sprintf("Confidence: %.0f%%\n", growth$stage_confidence * 100))

## Summary

geospatialsuite’s agricultural applications provide:

### CDL Analysis Capabilities:

- Comprehensive crop codes for all USDA CDL categories
- Dynamic crop analysis with area calculations
- Flexible crop masking and classification
- Multi-year rotation analysis
- Crop diversity mapping

### Precision Agriculture Tools:

- Crop-specific vegetation analysis with stress detection
- Enhanced NDVI with agricultural processing
- Multi-scale field analysis (50m to 2km scales)
- Management zone delineation
- Yield prediction support

### Integrated Workflows:

- Environmental integration (soil, weather, topography)
- Water quality assessment in agricultural context
- Temporal growth monitoring
- Risk assessment for pests and diseases
- Comprehensive reporting and visualization

### Key Advantages:

1. Works with any agricultural region - not limited to specific areas
2. Handles all crop types - from row crops to specialty crops
3. Multi-scale analysis - field to regional levels
4. Temporal integration - supports time series analysis
5. Robust error handling - reliable for operational use
6. Comprehensive documentation - easy to implement

### Applications:

- Precision agriculture and variable rate applications
- Crop yield prediction and forecasting
- Agricultural sustainability assessment
- Environmental impact monitoring
- Policy and regulatory compliance
- Research and development support

The agricultural tools in geospatialsuite provide everything needed for professional agricultural analysis and precision farming applications!

## Acknowledgments

This work was developed by the geospatialsuite team with contributions from: Olatunde D. Akanbi, Vibha Mandayam, Yinghui Wu, Jeffrey Yarus, Erika I. Barcelos, and Roger H. French.