Enhance report utility functions and add validation scripts

- Updated `create_CI_map` and `create_CI_diff_map` functions to enforce a 1:1 aspect ratio for consistent map sizing.
- Modified `ci_plot` function to adjust widths of arranged maps for better layout.
- Changed raster merging method in `aggregate_per_field_mosaics_to_farm_level` from `mosaic` to `merge` for improved handling of field data.
- Introduced `test_kpi_validation.R` script to validate the structure of KPI RDS files, ensuring expected KPIs are present.
- Added `test_overview_maps_aggregation.R` script to test the aggregation pipeline for overview maps, including loading field mosaics, creating a farm-level mosaic, and generating visualizations.

README.md

@@ -18,5 +18,7 @@ SmartCane processes 4-band satellite imagery (RGB + NIR) into actionable crop he
 ---
 
 **Last Updated**: February 2026
+
 **Maintained By**: Resilience BV
+
 **Repository**: https://github.com/TimonWeitkamp/smartcane_experimental_area

r_app/10_create_per_field_tiffs_utils.R

@@ -152,10 +152,10 @@ crop_tiff_to_fields <- function(tif_path, tif_date, fields, output_base_dir) {
 #' @param field_tiles_dir Character. Target directory for per-field TIFFs
 #'                                   (e.g., "field_tiles/").
 #' @param fields sf object. GeoDataFrame of field boundaries with 'field_name' column.
-#' @param field_tiles_ci_dir Character. Optional. Directory where migrated CI-calculated
+#' @param field_tiles_ci_dir Character. Optional. DEPRECATED - no longer used.
-#'                                       TIFFs are stored. If provided, skips dates
+#'                                       Kept for backward compatibility but ignored.
-#'                                       already processed and moved to field_tiles_CI/.
+#'                                       Script 10 now only checks its own output
-#'                                       Default: NULL (process all dates).
+#'                                       directory (field_tiles/).
 #' @param end_date Date. Optional. End date for processing window (YYYY-MM-DD).
 #'                       Default: NULL (process all available dates).
 #' @param offset Integer. Optional. Number of days to look back from end_date.
@@ -245,31 +245,10 @@ process_new_merged_tif <- function(merged_tif_dir, field_tiles_dir, fields, fiel
   for (tif_path in tiff_files) {
     tif_date <- gsub("\\.tif$", "", basename(tif_path))
 
-    # CHECK 1: Skip if this date was already migrated to field_tiles_CI/
+    # SKIP CHECK: Only check Script 10's own output directory (field_tiles/)
-    # (This means Script 20 already processed it and extracted RDS)
+    # Do NOT check field_tiles_CI/ — that's Script 20's responsibility.
-    if (!is.null(field_tiles_ci_dir) && dir.exists(field_tiles_ci_dir)) {
+    # Script 10 and Script 20 are independent; Script 10 should not depend on
-      # Check if ANY field has this date in field_tiles_CI/
+    # Script 20's success/failure. If field_tiles/ is missing, reprocess it.
-      date_migrated <- FALSE
-
-      # Sample check: look for date in field_tiles_CI/*/DATE.tif
-      sample_field_dirs <- list.dirs(field_tiles_ci_dir, full.names = TRUE, recursive = FALSE)
-      for (field_dir in sample_field_dirs) {
-        potential_file <- file.path(field_dir, paste0(tif_date, ".tif"))
-        if (file.exists(potential_file)) {
-          date_migrated <- TRUE
-          break
-        }
-      }
-
-      if (date_migrated) {
-        safe_log(paste("Skipping:", tif_date, "(already migrated and processed by Script 20)"), "INFO")
-        total_skipped <- total_skipped + 1
-        next
-      }
-    }
-
-    # CHECK 2: Skip if this date already exists in field_tiles/
-    # (means this date has already been processed through Script 10)
     if (dir.exists(field_tiles_dir)) {
       date_exists_in_field_tiles <- FALSE
       

r_app/20_ci_extraction_per_field.R

@@ -150,7 +150,9 @@ main <- function() {
       ci_raster <- calc_ci_from_raster(raster_4band)
       
       # Create 5-band (R, G, B, NIR, CI)
+      # Explicitly set band names after combining to ensure proper naming
       five_band <- c(raster_4band, ci_raster)
+      names(five_band) <- c("Red", "Green", "Blue", "NIR", "CI")
       
       # Now process all fields from this single merged TIFF
       fields_processed_this_date <- 0

r_app/20_ci_extraction_utils.R

@@ -1013,6 +1013,10 @@ calc_ci_from_raster <- function(raster_obj) {
   # NDVI ranges -1 to 1 and is different from Chlorophyll Index
   ci <- nir / green - 1
   
+  # CRITICAL: Explicitly name the CI band before returning
+  # This ensures proper band naming when combined with other rasters
+  names(ci) <- "CI"
+  
   return(ci)
 }
 

r_app/80_utils_agronomic_support.R

@@ -543,7 +543,7 @@ create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
       by = c("field_idx")
     ) %>%
     left_join(
-      all_kpis$tch_forecasted %>% select(field_idx, tch_forecasted),
+      all_kpis$tch_forecasted %>% select(field_idx, tch_forecasted, mean_ci),
       by = c("field_idx")
     ) %>%
     left_join(
@@ -562,6 +562,7 @@ create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
       `Decline Risk` = decline_severity,
       `Weed Risk` = weed_pressure_risk,
       `CI Change %` = mean_ci_pct_change,
+      `Mean CI` = mean_ci,
       `CV Value` = cv_value
     ) %>%
     # Add placeholder columns expected by reporting script (will be populated from other sources)
@@ -570,7 +571,7 @@ create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
       `Gap Score` = NA_real_
     ) %>%
     select(field_idx, Field, `Field Size (ha)`, `Growth Uniformity`, `Yield Forecast (t/ha)`, 
-           `Gap Score`, `Decline Risk`, `Weed Risk`, `CI Change %`, `CV Value`)
+           `Gap Score`, `Decline Risk`, `Weed Risk`, `CI Change %`, `Mean CI`, `CV Value`)
   
   return(result)
 }

r_app/90_CI_report_with_kpis_agronomic_support.Rmd

@@ -53,6 +53,7 @@ suppressPackageStartupMessages({
   
   # Visualization
   library(tmap)       # For interactive maps (field boundary visualization)
+  library(ggspatial)  # For basemap tiles and spatial annotations (OSM basemap with ggplot2)
   
   # Reporting
   library(knitr)      # For R Markdown document generation (code execution and output)
@@ -378,43 +379,101 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
 ## Executive Summary - Key Performance Indicators
 
 ```{r combined_kpi_table, echo=FALSE, results='asis'}
-# Safely display KPI tables
+# Display KPI tables - standardized format with Level, Count, Percent columns
 if (exists("summary_tables") && !is.null(summary_tables) && length(summary_tables) > 0) {
   
-  # Try to combine KPI tables, with fallback if structure is unexpected
   tryCatch({
-    # Build a list of valid dataframes from summary_tables
+    # KPI metadata for display
-    valid_tables <- list()
+    kpi_display_order <- list(
-    
+      uniformity = list(display = "Field Uniformity", level_col = "Status", count_col = "Field Count"),
-    for (kpi_name in names(summary_tables)) {
+      area_change = list(display = "Area Change", level_col = "Status", count_col = "Field Count"),
-      kpi_df <- summary_tables[[kpi_name]]
+      tch_forecast = list(display = "TCH Forecasted", level_col = NULL, count_col = "Fields"),
-      
+      growth_decline = list(display = "Growth Decline", level_col = "Trend", count_col = "Field Count"),
-      # Skip NULL, empty, or non-dataframe items
+      weed_pressure = list(display = "Weed Presence", level_col = "Risk Level", count_col = "Field Count"),
-      if (!is.null(kpi_df) && is.data.frame(kpi_df) && nrow(kpi_df) > 0) {
+      gap_filling = list(display = "Gap Filling", level_col = NULL, count_col = NULL)
-        # Add KPI name as a column if not already present
+    )
-        if (!"KPI" %in% names(kpi_df)) {
+
-          display_name <- gsub("_", " ", tools::toTitleCase(gsub("_summary|_data", "", kpi_name)))
+    standardize_kpi <- function(df, level_col, count_col) {
-          kpi_df$KPI <- display_name
+      if (is.null(level_col) || !(level_col %in% names(df)) || is.null(count_col) || !(count_col %in% names(df))) {
+        return(NULL)
+      }
+      total <- sum(df[[count_col]], na.rm = TRUE)
+      total <- ifelse(total == 0, NA_real_, total)
+
+      df %>%
+        dplyr::transmute(
+          Level = as.character(.data[[level_col]]),
+          Count = as.integer(round(as.numeric(.data[[count_col]]))),
+          Percent = dplyr::if_else(
+            is.na(total),
+            NA_real_,
+            round(Count / total * 100, 1)
+          )
+        )
+    }
+
+    combined_df_list <- list()
+    order_keys <- names(kpi_display_order)
+
+    for (kpi_key in order_keys) {
+      config <- kpi_display_order[[kpi_key]]
+      if (!kpi_key %in% names(summary_tables)) next
+      kpi_df <- summary_tables[[kpi_key]]
+      if (is.null(kpi_df) || !is.data.frame(kpi_df) || nrow(kpi_df) == 0) next
+
+      kpi_rows <- standardize_kpi(kpi_df, config$level_col, config$count_col)
+      if (is.null(kpi_rows) && kpi_key %in% c("tch_forecast", "gap_filling")) {
+        numeric_cols <- names(kpi_df)[vapply(kpi_df, is.numeric, logical(1))]
+        if (length(numeric_cols) > 0) {
+          kpi_rows <- tibble::tibble(
+            Level = numeric_cols,
+            Count = round(as.numeric(kpi_df[1, numeric_cols]), 2),
+            Percent = NA_real_
+          )
         }
-        valid_tables[[kpi_name]] <- kpi_df
+      }
+
+      if (!is.null(kpi_rows)) {
+        kpi_rows$KPI <- config$display
+        combined_df_list[[length(combined_df_list) + 1]] <- kpi_rows
       }
     }
-    
+
-    # Combine all valid tables
+    combined_df <- dplyr::bind_rows(combined_df_list)
-    if (length(valid_tables) > 0) {
+
-      # Use careful bind_rows that handles mismatched columns
+    if (nrow(combined_df) > 0) {
-      combined_df <- dplyr::bind_rows(valid_tables, .id = NULL)
+      combined_df <- combined_df %>%
-      
+        dplyr::group_by(KPI) %>%
-      # Display as flextable
+        dplyr::mutate(
-      ft <- flextable(combined_df) %>% autofit()
+          KPI_display = if_else(dplyr::row_number() == 1, KPI, "")
-      ft
+        ) %>%
+        dplyr::ungroup() %>%
+        dplyr::select(KPI = KPI_display, Level, Count, Percent)
+
+      ft <- flextable(combined_df) %>%
+        merge_v(j = "KPI") %>%
+        autofit()
+
+      kpi_group_sizes <- combined_df %>%
+        dplyr::group_by(KPI) %>%
+        dplyr::tally() %>%
+        dplyr::pull(n)
+      cum_rows <- cumsum(kpi_group_sizes)
+      for (i in seq_along(cum_rows)) {
+        if (i < length(cum_rows)) {
+          ft <- ft %>%
+            hline(i = cum_rows[i], border = officer::fp_border(width = 2))
+        }
+      }
+
+      print(ft)
     } else {
-      cat("No valid KPI summary tables found.\n")
+      cat("No valid KPI summary tables found for display.\n")
     }
     
   }, error = function(e) {
-    safe_log(paste("Error combining KPI tables:", e$message), "WARNING")
+    safe_log(paste("Error displaying KPI tables:", e$message), "WARNING")
-    cat("KPI summary tables could not be combined for display. Individual KPI sections will be shown below.\n")
+    cat("KPI summary tables could not be displayed. Individual KPI sections will be shown below.\n")
   })
   
 } else {
@@ -620,6 +679,372 @@ if (!exists("field_details_table") || is.null(field_details_table)) {
 }
 ```
 
+## Farm-Level Overview Maps
+
+```{r aggregate_farm_level_rasters, message=FALSE, warning=FALSE, include=FALSE}
+# Aggregate per-field weekly mosaics into single farm-level rasters for visualization
+# This creates on-the-fly mosaics for current week and historical weeks without saving intermediate files
+
+tryCatch({
+  safe_log("Starting farm-level raster aggregation for overview maps")
+  
+  # Helper function to safely aggregate mosaics for a specific week
+  aggregate_mosaics_safe <- function(week_num, year_num, label) {
+    tryCatch({
+      safe_log(paste("Aggregating mosaics for", label, "(week", week_num, ",", year_num, ")"))
+      
+      # Call the utility function from report_utils.R
+      # This function reads all per-field mosaics and merges them into a single raster
+      farm_mosaic <- aggregate_per_field_mosaics_to_farm_level(
+        weekly_mosaic_dir = weekly_CI_mosaic,
+        target_week = week_num,
+        target_year = year_num
+      )
+      
+      if (!is.null(farm_mosaic)) {
+        safe_log(paste("✓ Successfully aggregated farm mosaic for", label, ""))
+        return(farm_mosaic)
+      } else {
+        safe_log(paste("Warning: Farm mosaic is NULL for", label), "WARNING")
+        return(NULL)
+      }
+    }, error = function(e) {
+      safe_log(paste("Error aggregating mosaics for", label, ":", e$message), "WARNING")
+      return(NULL)
+    })
+  }
+  
+  # Aggregate mosaics for three weeks: current, week-1, week-3
+  farm_mosaic_current <- aggregate_mosaics_safe(current_week, current_iso_year, "current week")
+  farm_mosaic_minus_1 <- aggregate_mosaics_safe(week_minus_1, week_minus_1_year, "week-1")
+  farm_mosaic_minus_3 <- aggregate_mosaics_safe(week_minus_3, week_minus_3_year, "week-3")
+  
+  # Extract CI band (5th band, or named "CI") from each aggregated mosaic
+  farm_ci_current <- NULL
+  farm_ci_minus_1 <- NULL
+  farm_ci_minus_3 <- NULL
+  
+  if (!is.null(farm_mosaic_current)) {
+    if ("CI" %in% names(farm_mosaic_current)) {
+      farm_ci_current <- farm_mosaic_current[["CI"]]
+    } else if (terra::nlyr(farm_mosaic_current) >= 5) {
+      farm_ci_current <- farm_mosaic_current[[5]]  # CI is typically band 5 (R,G,B,NIR,CI)
+    }
+    if (!is.null(farm_ci_current)) {
+      safe_log("✓ Extracted CI band from current week mosaic")
+    }
+  }
+  
+  if (!is.null(farm_mosaic_minus_1)) {
+    if ("CI" %in% names(farm_mosaic_minus_1)) {
+      farm_ci_minus_1 <- farm_mosaic_minus_1[["CI"]]
+    } else if (terra::nlyr(farm_mosaic_minus_1) >= 5) {
+      farm_ci_minus_1 <- farm_mosaic_minus_1[[5]]
+    }
+    if (!is.null(farm_ci_minus_1)) {
+      safe_log("✓ Extracted CI band from week-1 mosaic")
+    }
+  }
+  
+  if (!is.null(farm_mosaic_minus_3)) {
+    if ("CI" %in% names(farm_mosaic_minus_3)) {
+      farm_ci_minus_3 <- farm_mosaic_minus_3[["CI"]]
+    } else if (terra::nlyr(farm_mosaic_minus_3) >= 5) {
+      farm_ci_minus_3 <- farm_mosaic_minus_3[[5]]
+    }
+    if (!is.null(farm_ci_minus_3)) {
+      safe_log("✓ Extracted CI band from week-3 mosaic")
+    }
+  }
+  
+  # Calculate difference rasters
+  farm_ci_diff_week <- NULL
+  if (!is.null(farm_ci_current) && !is.null(farm_ci_minus_1)) {
+    farm_ci_diff_week <- farm_ci_current - farm_ci_minus_1
+    safe_log("✓ Calculated week-over-week difference raster")
+  } else {
+    safe_log("Warning: Cannot calculate week-over-week difference - missing data", "WARNING")
+  }
+
+  # Reproject rasters and boundaries for ggplot basemap (OSM uses EPSG:4326)
+  farm_ci_current_ll <- farm_ci_current
+  farm_ci_diff_week_ll <- farm_ci_diff_week
+  AllPivots0_ll <- AllPivots0
+  target_crs <- "EPSG:4326"
+
+  downsample_raster <- function(r, max_cells = 2000000) {
+    if (is.null(r)) {
+      return(NULL)
+    }
+    n_cells <- terra::ncell(r)
+    if (!is.na(n_cells) && n_cells > max_cells) {
+      fact <- ceiling(sqrt(n_cells / max_cells))
+      safe_log(paste("Downsampling raster by factor", fact), "INFO")
+      return(terra::aggregate(r, fact = fact, fun = mean, na.rm = TRUE))
+    }
+    r
+  }
+
+  if (!is.null(farm_ci_current) && !terra::is.lonlat(farm_ci_current)) {
+    farm_ci_current_ll <- terra::project(farm_ci_current, target_crs, method = "bilinear")
+  }
+  if (!is.null(farm_ci_diff_week) && !terra::is.lonlat(farm_ci_diff_week)) {
+    farm_ci_diff_week_ll <- terra::project(farm_ci_diff_week, target_crs, method = "bilinear")
+  }
+  if (!is.null(AllPivots0)) {
+    AllPivots0_ll <- sf::st_transform(AllPivots0, 4326)
+  }
+
+  farm_ci_current_ll <- downsample_raster(farm_ci_current_ll)
+  farm_ci_diff_week_ll <- downsample_raster(farm_ci_diff_week_ll)
+
+  # Ensure boundaries align with raster extent to avoid plotting issues
+  sf::sf_use_s2(FALSE)
+  if (!is.null(AllPivots0_ll) && !is.null(farm_ci_current_ll)) {
+    AllPivots0_ll <- sf::st_make_valid(AllPivots0_ll)
+    crop_bbox_current <- sf::st_as_sfc(sf::st_bbox(terra::ext(farm_ci_current_ll), crs = 4326))
+    AllPivots0_ll <- sf::st_intersection(AllPivots0_ll, crop_bbox_current)
+    AllPivots0_ll <- sf::st_collection_extract(AllPivots0_ll, "POLYGON")
+  }
+
+  # Prepare boundary and label data frames for ggplot (avoid geom_sf coord resets)
+  bounds_df <- NULL
+  labels_df <- NULL
+  if (!is.null(AllPivots0_ll)) {
+    bounds_coords <- sf::st_coordinates(AllPivots0_ll)
+    bounds_df <- as.data.frame(bounds_coords)
+    bounds_df$group <- interaction(bounds_df$L1, bounds_df$L2, drop = TRUE)
+    label_pts <- sf::st_point_on_surface(AllPivots0_ll)
+    labels_df <- cbind(as.data.frame(sf::st_coordinates(label_pts)), sub_field = label_pts$sub_field)
+  }
+  
+  # Log completion
+  safe_log("Farm-level raster aggregation complete")
+  
+}, error = function(e) {
+  safe_log(paste("Error in farm-level raster aggregation:", e$message), "ERROR")
+})
+```
+
+### Chlorophyll Index (CI) Overview Map - Current Week
+
+```{r render_farm_ci_map, echo=FALSE, fig.height=5.5, fig.width=6.5, dpi=150, dev='png', message=FALSE, warning=FALSE}
+# Create farm-level chlorophyll index map with OpenStreetMap basemap
+tryCatch({
+  if (!is.null(farm_ci_current_ll)) {
+    safe_log("Rendering farm-level CI overview map")
+    
+    # Convert raster to data.frame for ggplot
+    ci_df <- as.data.frame(farm_ci_current_ll, xy = TRUE, na.rm = FALSE)
+    colnames(ci_df) <- c("x", "y", "ci_value")
+    
+    # Choose color palette based on colorblind_friendly parameter
+    if (colorblind_friendly) {
+      fill_scale <- ggplot2::scale_fill_viridis_c(
+        name = "Chlorophyll Index (CI)",
+        limits = c(1, 8),
+        direction = -1,  # Reversed: green=high, yellow/red=low
+        na.value = "transparent",
+        oob = scales::squish
+      )
+    } else {
+      # Use Red-Yellow-Green diverging palette (reversed for intuitive interpretation)
+      fill_scale <- ggplot2::scale_fill_distiller(
+        palette = "RdYlGn",
+        name = "Chlorophyll Index (CI)",
+        limits = c(1, 8),
+        direction = 1,  # Standard direction for RdYlGn
+        na.value = "transparent"
+      )
+    }
+    
+    # Build the map
+    ci_ext <- terra::ext(farm_ci_current_ll)
+    ci_bbox <- sf::st_bbox(c(xmin = ci_ext$xmin, xmax = ci_ext$xmax, ymin = ci_ext$ymin, ymax = ci_ext$ymax), crs = 4326)
+
+    map <- ggplot2::ggplot() +
+      ggspatial::annotation_map_tile(
+        type = "osm",
+        zoom = 14,
+        alpha = 0.4
+      ) +
+      # Add raster layer with CI values
+      ggplot2::geom_raster(
+        data = ci_df,
+        ggplot2::aes(x = x, y = y, fill = ci_value)
+      ) +
+      fill_scale +
+      ggplot2::coord_sf(
+        crs = 4326,
+        xlim = c(ci_ext$xmin, ci_ext$xmax),
+        ylim = c(ci_ext$ymin, ci_ext$ymax),
+        expand = FALSE
+      )
+
+    if (!is.null(bounds_df)) {
+      map <- map + ggplot2::geom_path(
+        data = bounds_df,
+        ggplot2::aes(x = X, y = Y, group = group),
+        color = "black",
+        linewidth = 0.4
+      )
+    }
+
+    if (!is.null(labels_df)) {
+      map <- map + ggplot2::geom_text(
+        data = labels_df,
+        ggplot2::aes(x = X, y = Y, label = sub_field),
+        size = 3,
+        color = "black"
+      )
+    }
+
+    map <- map +
+      # Add scale bar and theme
+      ggspatial::annotation_scale(
+        location = "br",
+        width_hint = 0.25
+      ) +
+      ggplot2::theme_void() +
+      ggplot2::theme(
+        legend.position = "bottom",
+        legend.direction = "horizontal",
+        legend.title = ggplot2::element_text(size = 10),
+        legend.text = ggplot2::element_text(size = 9),
+        plot.title = ggplot2::element_text(hjust = 0.5, size = 12, face = "bold"),
+        panel.background = ggplot2::element_rect(fill = "white", color = NA)
+      ) +
+      ggplot2::labs(
+        title = paste("Current Week CI Overview - Week", current_week, "of", current_iso_year)
+      )
+    
+    # Print the map
+    print(map)
+    safe_log("✓ Farm-level CI map rendered successfully")
+    
+  } else {
+    safe_log("Farm-level CI raster not available - skipping overview map", "WARNING")
+    plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
+    text(1, 1, "Farm-level CI overview map not available", cex = 1.5, col = "gray")
+  }
+}, error = function(e) {
+  safe_log(paste("Error rendering farm CI overview map:", e$message), "ERROR")
+  plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
+  text(1, 1, "Error creating CI overview map", cex = 1.5, col = "red")
+})
+```
+
+### Weekly Chlorophyll Index Difference Map
+
+```{r render_farm_ci_diff_map, echo=FALSE, fig.height=5.5, fig.width=6.5, dpi=150, dev='png', message=FALSE, warning=FALSE}
+# Create farm-level CI difference map (week-over-week change)
+tryCatch({
+  if (!is.null(farm_ci_diff_week_ll)) {
+    safe_log("Rendering farm-level CI difference map (week-over-week)")
+    
+    # Convert difference raster to data.frame for ggplot
+    diff_df <- as.data.frame(farm_ci_diff_week_ll, xy = TRUE, na.rm = FALSE)
+    colnames(diff_df) <- c("x", "y", "ci_diff")
+    
+    # Determine color palette based on colorblind_friendly parameter
+    if (colorblind_friendly) {
+      # Use plasma for colorblind-friendly diverging visualization
+      fill_scale <- ggplot2::scale_fill_viridis_c(
+        name = "CI Change (Week-over-Week)",
+        option = "plasma",
+        limits = c(-3, 3),
+        na.value = "transparent",
+        oob = scales::squish
+      )
+    } else {
+      # Use Red-Blue diverging palette (red=decline, blue=increase)
+      fill_scale <- ggplot2::scale_fill_distiller(
+        palette = "RdBu",
+        name = "CI Change (Week-over-Week)",
+        limits = c(-3, 3),
+        direction = 1,
+        na.value = "transparent"
+      )
+    }
+    
+    # Build the map
+    diff_ext <- terra::ext(farm_ci_diff_week_ll)
+    diff_bbox <- sf::st_bbox(c(xmin = diff_ext$xmin, xmax = diff_ext$xmax, ymin = diff_ext$ymin, ymax = diff_ext$ymax), crs = 4326)
+
+    map <- ggplot2::ggplot() +
+      ggspatial::annotation_map_tile(
+        type = "osm",
+        zoom = 12,
+        alpha = 0.4
+      ) +
+      # Add raster layer with difference values
+      ggplot2::geom_raster(
+        data = diff_df,
+        ggplot2::aes(x = x, y = y, fill = ci_diff)
+      ) +
+      fill_scale +
+      ggplot2::coord_sf(
+        crs = 4326,
+        xlim = c(diff_ext$xmin, diff_ext$xmax),
+        ylim = c(diff_ext$ymin, diff_ext$ymax),
+        expand = FALSE
+      )
+
+    if (!is.null(bounds_df)) {
+      map <- map + ggplot2::geom_path(
+        data = bounds_df,
+        ggplot2::aes(x = X, y = Y, group = group),
+        color = "black",
+        linewidth = 0.4
+      )
+    }
+
+    if (!is.null(labels_df)) {
+      map <- map + ggplot2::geom_text(
+        data = labels_df,
+        ggplot2::aes(x = X, y = Y, label = sub_field),
+        size = 3,
+        color = "black"
+      )
+    }
+
+    map <- map +
+      # Add scale bar and theme
+      ggspatial::annotation_scale(
+        location = "br",
+        width_hint = 0.25
+      ) +
+      ggplot2::theme_void() +
+      ggplot2::theme(
+        legend.position = "bottom",
+        legend.direction = "horizontal",
+        legend.title = ggplot2::element_text(size = 10),
+        legend.text = ggplot2::element_text(size = 9),
+        plot.title = ggplot2::element_text(hjust = 0.5, size = 12, face = "bold"),
+        panel.background = ggplot2::element_rect(fill = "white", color = NA)
+      ) +
+      ggplot2::labs(
+        title = paste("Weekly CI Change - Week", current_week, "vs Week", week_minus_1)
+      )
+    
+    # Print the map
+    print(map)
+    safe_log("✓ Farm-level CI difference map rendered successfully")
+    
+  } else {
+    safe_log("Farm-level difference raster not available - skipping difference map", "WARNING")
+    plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
+    text(1, 1, "Week-over-week difference map not available", cex = 1.5, col = "gray")
+  }
+}, error = function(e) {
+  safe_log(paste("Error rendering farm CI difference map:", e$message), "ERROR")
+  plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
+  text(1, 1, "Error creating CI difference map", cex = 1.5, col = "red")
+})
+```
+
+\newpage
+
 # Section 2: Field-by-Field Analysis
 
 ## Overview of Field-Level Insights
@@ -635,7 +1060,7 @@ This section provides detailed, field-specific analyses including chlorophyll in
 
 \newpage
 
-```{r generate_field_visualizations, eval=TRUE, fig.height=3.8, fig.width=10, dpi=300, dev='png', message=TRUE, echo=FALSE, warning=TRUE, include=TRUE, results='asis'}
+```{r generate_field_visualizations, eval=TRUE, fig.height=3.8, fig.width=6.5, dpi=150, dev='png', message=TRUE, echo=FALSE, warning=TRUE, include=TRUE, results='asis'}
 # Generate detailed visualizations for each field using purrr::walk
 tryCatch({
   # Prepare merged field list and week/year info
@@ -799,7 +1224,7 @@ tryCatch({
 })
 ```
 
-```{r generate_subarea_visualizations, echo=FALSE, fig.height=3.8, fig.width=10, message=FALSE, warning=FALSE, results='asis', eval=FALSE}
+```{r generate_subarea_visualizations, echo=FALSE, fig.height=3.8, fig.width=6.5, dpi=150, message=FALSE, warning=FALSE, results='asis', eval=FALSE}
 # Alternative visualization grouped by sub-area (disabled by default)
 tryCatch({
   # Group pivots by sub-area
@@ -907,12 +1332,12 @@ if (!exists("field_details_table") || is.null(field_details_table)) {
 
 # Display the cleaned field table with flextable
 # Set column widths to fit page (approximately 6.5 inches for 1-inch margins)
-col_widths <- c(1.2, 0.9, 1.0, 1.0, 0.8, 0.9, 0.8, 0.7, 0.6)  # inches for each column
+# Scale widths proportionally: original total = 8.0 inches, scale to 6.2 inches
+col_widths <- c(0.97, 0.73, 0.80, 0.80, 0.65, 0.73, 0.65, 0.56, 0.48)  # inches for each column
 
 ft <- flextable(field_details_clean) %>%
   set_caption("Detailed Field Performance Summary") %>%
-  width(width = col_widths) %>%
+  width(width = col_widths)
-  autofit(add_w = 0, add_h = 0)
 
 ft
 ```

r_app/MANUAL_PIPELINE_RUNNER.R

@@ -438,8 +438,8 @@
 #   rmarkdown::render(
 rmarkdown::render(
   "r_app/90_CI_report_with_kpis_agronomic_support.Rmd",
-  params = list(data_dir = "aura", report_date = as.Date("2026-01-01")),
+  params = list(data_dir = "aura", report_date = as.Date("2022-12-08")),
-  output_file = "SmartCane_Report_agronomic_support_aura_2026-01-01.docx",
+  output_file = "SmartCane_Report_agronomic_support_aura_2022-12-08.docx",
   output_dir = "laravel_app/storage/app/aura/reports"
 )
 #

r_app/report_utils.R

@@ -78,7 +78,11 @@ create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend =
   # Add layout configuration to prevent legend rescaling
   map <- map + tm_layout(legend.position = c("left", "bottom"),
                          legend.outside = FALSE,
-                         inner.margins = 0.05)
+                         inner.margins = 0.05,
+                         asp = 1)  # Force 1:1 aspect ratio for consistent sizing
+  
+  # Add bounds/view settings for fixed aspect ratio
+  map <- map + tm_view(asp = 1)
   
   # Add borders if requested
   if (borders) {
@@ -146,7 +150,11 @@ create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_lege
   # Add layout configuration to prevent legend rescaling
   map <- map + tm_layout(legend.position = c("right", "bottom"),
                          legend.outside = FALSE,
-                         inner.margins = 0.05)
+                         inner.margins = 0.05,
+                         asp = 1)  # Force 1:1 aspect ratio for consistent sizing
+  
+  # Add bounds/view settings for fixed aspect ratio
+  map <- map + tm_view(asp = 1)
   
   # Add borders if requested
   if (borders) {
@@ -260,17 +268,19 @@ ci_plot <- function(pivotName,
                           week = week, age = age, borders = borders, colorblind = colorblind_friendly)
     
     # Create historical maps only if data is available
-    maps_to_arrange <- list(CImap)
+    # Build list with all available maps - order matches original: [m2, m1, current, diff_1w, diff_3w]
-    widths_to_use <- c(1)
+    # Widths match original hardcoded: c(0.23, 0.18, 0.18, 0.18, 0.23)
+    maps_to_arrange <- list()
+    widths_to_use <- c()
     field_heading_note <- ""
     
-    # Try to create 2-week ago map
+    # Try to create 2-week ago map (legend on left)
     if (!is.null(singlePivot_m2)) {
       CImap_m2 <- create_CI_map(singlePivot_m2, AllPivots2, joined_spans2, 
                                 show_legend = TRUE, legend_is_portrait = TRUE, 
                                 week = week_minus_2, age = age - 2, borders = borders, colorblind = colorblind_friendly)
-      maps_to_arrange <- c(list(CImap_m2), maps_to_arrange)
+      maps_to_arrange <- c(maps_to_arrange, list(CImap_m2))
-      widths_to_use <- c(0.4, widths_to_use)
+      widths_to_use <- c(widths_to_use, 0.24)
     }
     
     # Try to create 1-week ago map
@@ -279,25 +289,29 @@ ci_plot <- function(pivotName,
                                 show_legend = FALSE, legend_is_portrait = FALSE, 
                                 week = week_minus_1, age = age - 1, borders = borders, colorblind = colorblind_friendly)
       maps_to_arrange <- c(maps_to_arrange, list(CImap_m1))
-      widths_to_use <- c(widths_to_use, 0.3)
+      widths_to_use <- c(widths_to_use, 0.17)
     }
     
+    # Always add current week map (center position)
+    maps_to_arrange <- c(maps_to_arrange, list(CImap))
+    widths_to_use <- c(widths_to_use, 0.17)
+    
     # Try to create 1-week difference map
     if (!is.null(abs_CI_last_week)) {
       CI_max_abs_last_week <- create_CI_diff_map(abs_CI_last_week, AllPivots2, joined_spans2,  
                                                 show_legend = FALSE, legend_is_portrait = FALSE, 
                                                 week_1 = week, week_2 = week_minus_1, age = age, borders = borders, colorblind = colorblind_friendly)
       maps_to_arrange <- c(maps_to_arrange, list(CI_max_abs_last_week))
-      widths_to_use <- c(widths_to_use, 0.3)
+      widths_to_use <- c(widths_to_use, 0.17)
     }
     
-    # Try to create 3-week difference map
+    # Try to create 3-week difference map (legend on right)
     if (!is.null(abs_CI_three_week)) {
       CI_max_abs_three_week <- create_CI_diff_map(abs_CI_three_week, AllPivots2, joined_spans2, 
                                                  show_legend = TRUE, legend_is_portrait = TRUE, 
                                                  week_1 = week, week_2 = week_minus_3, age = age, borders = borders, colorblind = colorblind_friendly)
       maps_to_arrange <- c(maps_to_arrange, list(CI_max_abs_three_week))
-      widths_to_use <- c(widths_to_use, 0.4)
+      widths_to_use <- c(widths_to_use, 0.24)
     }
     
     # Normalize widths to sum to 1
@@ -925,19 +939,19 @@ aggregate_per_field_mosaics_to_farm_level <- function(
     
     safe_log(paste("Successfully loaded mosaics for", length(raster_list), "fields"), "INFO")
     
-    # Create a SpatRasterCollection and mosaic using correct terra syntax
+    # Create a SpatRasterCollection and merge using correct terra syntax
     tryCatch({
       rsrc <- terra::sprc(raster_list)
       safe_log(paste("Created SpatRasterCollection with", length(raster_list), "rasters"), "DEBUG")
       
-      # Mosaic the rasters - this merges them into a single continuous raster
+      # Merge the rasters into a single continuous raster (no overlap expected between fields)
-      farm_mosaic <- terra::mosaic(rsrc)
+      farm_mosaic <- terra::merge(rsrc)
       
       safe_log(paste("Aggregated", length(raster_list), "per-field mosaics into farm-level mosaic"), "INFO")
       
       # Verify mosaic was created successfully
       if (is.null(farm_mosaic)) {
-        stop("mosaic() returned NULL")
+        stop("merge() returned NULL")
       }
       
       return(farm_mosaic)

r_app/test_kpi_validation.R

@@ -0,0 +1,155 @@
+#!/usr/bin/env Rscript
+# Diagnostic script to validate KPI RDS file structure
+# Usage: Rscript test_kpi_validation.R [project] [date]
+
+# Set up arguments
+args <- commandArgs(trailingOnly = TRUE)
+
+if (length(args) < 2) {
+  cat("Usage: Rscript test_kpi_validation.R [project] [date]\n")
+  cat("Example: Rscript test_kpi_validation.R aura 2022-11-14\n")
+  quit(status = 1)
+}
+
+project_dir <- args[1]
+report_date <- as.Date(args[2])
+
+cat("\n=== KPI RDS Validation ===\n")
+cat("Project:", project_dir, "\n")
+cat("Date:", report_date, "\n")
+
+# Load utilities
+source("r_app/parameters_project.R")
+source("r_app/00_common_utils.R")
+
+# Set up paths
+paths <- setup_project_directories(project_dir)
+kpi_data_dir <- paths$kpi_reports_dir
+
+# Calculate week
+current_week <- as.numeric(format(as.Date(report_date), "%V"))
+current_year <- as.numeric(format(as.Date(report_date), "%G"))
+
+kpi_rds_filename <- paste0(project_dir, "_kpi_summary_tables_week", 
+                           sprintf("%02d_%d", current_week, current_year), ".rds")
+kpi_rds_path <- file.path(kpi_data_dir, kpi_rds_filename)
+
+cat("\nKPI directory:", kpi_data_dir, "\n")
+cat("KPI filename:", kpi_rds_filename, "\n")
+cat("Full path:", kpi_rds_path, "\n\n")
+
+# Check if directory exists
+if (!dir.exists(kpi_data_dir)) {
+  cat("ERROR: KPI directory does not exist!\n")
+  quit(status = 1)
+}
+
+# List available files
+cat("Files in KPI directory:\n")
+files <- list.files(kpi_data_dir, pattern = "\\.rds$")
+if (length(files) == 0) {
+  cat("  (none)\n")
+} else {
+  for (f in files) {
+    cat("  -", f, "\n")
+  }
+}
+
+# Check if our specific file exists
+if (!file.exists(kpi_rds_path)) {
+  cat("\nWARNING: Expected KPI file not found!\n")
+  cat("Expected:", kpi_rds_filename, "\n")
+  quit(status = 1)
+}
+
+cat("\n✓ KPI file found. Loading...\n\n")
+
+# Load the RDS
+loaded_data <- readRDS(kpi_rds_path)
+
+# Inspect structure
+cat("=== RDS Structure ===\n")
+cat("Class:", class(loaded_data), "\n")
+cat("Length:", length(loaded_data), "\n")
+cat("Names:", paste(names(loaded_data), collapse = ", "), "\n\n")
+
+# Check if new or legacy structure
+if (is.list(loaded_data) && "summary_tables" %in% names(loaded_data)) {
+  cat("✓ New structure detected (has $summary_tables)\n\n")
+  summary_tables <- loaded_data$summary_tables
+  
+  if ("field_details" %in% names(loaded_data)) {
+    cat("✓ Also has $field_details\n\n")
+  }
+} else {
+  cat("✓ Legacy structure (direct list of KPI tables)\n\n")
+  summary_tables <- loaded_data
+}
+
+# Now inspect the summary_tables
+cat("=== Available KPI Tables ===\n")
+cat("Keys:", paste(names(summary_tables), collapse = ", "), "\n\n")
+
+# Expected KPIs
+expected_kpis <- c(
+  "uniformity",
+  "area_change",
+  "tch_forecasted",
+  "growth_decline",
+  "weed_pressure",
+  "gap_filling"
+)
+
+cat("=== Expected vs Actual ===\n")
+for (kpi in expected_kpis) {
+  # Try both formats
+  found <- FALSE
+  actual_key <- NA
+  
+  if (kpi %in% names(summary_tables)) {
+    found <- TRUE
+    actual_key <- kpi
+  } else if (paste0(kpi, "_summary") %in% names(summary_tables)) {
+    found <- TRUE
+    actual_key <- paste0(kpi, "_summary")
+  }
+  
+  status <- if (found) "✓ FOUND" else "✗ MISSING"
+  cat(sprintf("%-20s %s", kpi, status))
+  if (found) {
+    cat(" (key: ", actual_key, ")")
+  }
+  cat("\n")
+}
+
+cat("\n=== Detailed KPI Contents ===\n")
+for (kpi_key in names(summary_tables)) {
+  kpi_df <- summary_tables[[kpi_key]]
+  
+  cat("\n", kpi_key, ":\n", sep="")
+  cat("  Class:", class(kpi_df), "\n")
+  cat("  Dimensions:", nrow(kpi_df), "rows ×", ncol(kpi_df), "cols\n")
+  cat("  Columns:", paste(names(kpi_df), collapse = ", "), "\n")
+  
+  if (nrow(kpi_df) > 0) {
+    cat("  First few rows:\n")
+    print(head(kpi_df, 3))
+  } else {
+    cat("  (empty dataframe)\n")
+  }
+}
+
+cat("\n=== Validation Summary ===\n")
+missing_count <- sum(!expected_kpis %in% c(names(summary_tables), paste0(expected_kpis, "_summary")))
+if (missing_count == 0) {
+  cat("✓ All expected KPIs are present!\n")
+} else {
+  cat("✗ Missing", missing_count, "KPI(s):\n")
+  for (kpi in expected_kpis) {
+    if (!kpi %in% names(summary_tables) && !paste0(kpi, "_summary") %in% names(summary_tables)) {
+      cat("  -", kpi, "\n")
+    }
+  }
+}
+
+cat("\n")

r_app/test_overview_maps_aggregation.R

@@ -0,0 +1,371 @@
+#!/usr/bin/env Rscript
+
+# ==============================================================================
+# TEST SCRIPT: Farm-Level Mosaic Aggregation for Overview Maps
+# ==============================================================================
+# Purpose: Test each step of the aggregation pipeline independently
+# ==============================================================================
+
+# Parse arguments
+args <- commandArgs(trailingOnly = TRUE)
+project_dir <- if (length(args) > 0) args[1] else "aura"
+report_date_str <- if (length(args) > 1) args[2] else "2022-12-08"
+
+cat("\n========== Testing Overview Maps Aggregation ==========\n")
+cat(paste("Project:", project_dir, "\n"))
+cat(paste("Report Date:", report_date_str, "\n\n"))
+cat(paste("Project:", project_dir, "\n"))
+cat(paste("Report Date:", report_date_str, "\n"))
+cat(paste(strrep("═", 80), "\n\n"))
+
+# Load libraries
+suppressPackageStartupMessages({
+  library(here)
+  library(sf)
+  library(terra)
+  library(tidyverse)
+  library(lubridate)
+  library(ggspatial)
+})
+
+# Load project config
+tryCatch({
+  source(here::here("r_app", "parameters_project.R"))
+  source(here::here("r_app", "00_common_utils.R"))
+}, error = function(e) {
+  stop("Error loading project utilities: ", e$message)
+})
+
+# Set up paths
+paths <- setup_project_directories(project_dir)
+weekly_CI_mosaic <- paths$weekly_mosaic_dir
+
+# Calculate week/year from report_date
+report_date_obj <- as.Date(report_date_str)
+current_week <- lubridate::isoweek(report_date_obj)
+current_iso_year <- lubridate::isoyear(report_date_obj)
+
+cat(paste(strrep("=", 80), "\n"))
+cat(paste("STEP 1: Check Directory Structure\n"))
+cat(paste(strrep("=", 80), "\n"))
+
+cat(paste("\nweekly_CI_mosaic path:", weekly_CI_mosaic, "\n"))
+cat(paste("Directory exists:", dir.exists(weekly_CI_mosaic), "\n"))
+
+if (!dir.exists(weekly_CI_mosaic)) {
+  cat("ERROR: weekly_mosaic directory not found!\n")
+  quit(status = 1)
+}
+
+# List contents
+all_items <- list.files(weekly_CI_mosaic, full.names = FALSE)
+cat(paste("\nTotal items in weekly_mosaic/:", length(all_items), "\n"))
+cat("First 10 items:\n")
+for (i in 1:min(10, length(all_items))) {
+  cat(paste("  ", all_items[i], "\n"))
+}
+
+# Find field directories
+field_dirs <- all_items[
+  !grepl("\\.tif$", all_items, ignore.case = TRUE) & 
+  dir.exists(file.path(weekly_CI_mosaic, all_items))
+]
+
+cat(paste("\nField directories found:", length(field_dirs), "\n"))
+if (length(field_dirs) > 0) {
+  cat("First 10 field directories:\n")
+  for (i in 1:min(10, length(field_dirs))) {
+    cat(paste("  ", field_dirs[i], "\n"))
+  }
+}
+
+cat(paste(strrep("=", 80), "\n"))
+cat(paste("STEP 2: Check Weekly Mosaic Files for Target Week\n"))
+cat(paste(strrep("=", 80), "\n"))
+
+cat(paste("\nTarget week:", sprintf("%02d", current_week), "\n"))
+cat(paste("Target year:", current_iso_year, "\n\n"))
+
+# Check which fields have mosaic files for this week
+found_files <- 0
+missing_files <- 0
+
+for (field_dir in field_dirs[1:min(10, length(field_dirs))]) {
+  expected_file <- paste0("week_", sprintf("%02d", current_week), "_", current_iso_year, ".tif")
+  full_path <- file.path(weekly_CI_mosaic, field_dir, expected_file)
+  
+  if (file.exists(full_path)) {
+    cat(paste("  ✓ FOUND:", field_dir, "/", expected_file, "\n"))
+    found_files <- found_files + 1
+  } else {
+    cat(paste("  ✗ MISSING:", field_dir, "/", expected_file, "\n"))
+    missing_files <- missing_files + 1
+    
+    # List what actually exists in this field's directory
+    field_path <- file.path(weekly_CI_mosaic, field_dir)
+    field_contents <- list.files(field_path, full.names = FALSE)
+    if (length(field_contents) > 0) {
+      cat(paste("     Available:", paste(field_contents[1:min(3, length(field_contents))], collapse = ", "), "\n"))
+    }
+  }
+}
+
+cat(paste("\nFound: ", found_files, " files | Missing: ", missing_files, "\n"))
+
+if (found_files == 0) {
+  cat("\nERROR: No weekly mosaic files found for this week/year combination!\n")
+  cat("Check if Script 40 (mosaic_creation) has been run for this week.\n")
+  quit(status = 1)
+}
+
+cat("\n================================================================================\n")
+cat("STEP 3: Load Individual Field Mosaics\n")
+cat("================================================================================\n")
+
+# Load all available mosaics
+raster_list <- list()
+loaded_count <- 0
+
+for (field_dir in field_dirs) {
+  full_path <- file.path(weekly_CI_mosaic, field_dir, 
+                         paste0("week_", sprintf("%02d", current_week), "_", current_iso_year, ".tif"))
+  
+  if (file.exists(full_path)) {
+    tryCatch({
+      r <- terra::rast(full_path)
+      raster_list[[field_dir]] <- r
+      loaded_count <- loaded_count + 1
+      
+      if (loaded_count <= 5) {
+        cat(paste("  ✓", field_dir, "- Raster loaded\n"))
+        cat(paste("    Dimensions:", dim(r)[1], "×", dim(r)[2], "\n"))
+        cat(paste("    Bands:", terra::nlyr(r), "\n"))
+        cat(paste("    Band names:", paste(names(r), collapse = ", "), "\n"))
+        cat(paste("    CRS:", terra::crs(r), "\n\n"))
+      }
+    }, error = function(e) {
+      cat(paste("  ✗", field_dir, "- ERROR loading:", e$message, "\n"))
+    })
+  }
+}
+
+cat(paste("\nSuccessfully loaded:", loaded_count, "field mosaics\n"))
+
+if (loaded_count == 0) {
+  cat("\nERROR: Could not load any field mosaics!\n")
+  quit(status = 1)
+}
+
+cat("\n================================================================================\n")
+cat("STEP 4: Test Mosaic Aggregation\n")
+cat("================================================================================\n")
+
+cat(paste("\nAttempting to mosaic", length(raster_list), "rasters...\n"))
+
+tryCatch({
+  # Create SpatRasterCollection
+  cat("  Creating SpatRasterCollection...\n")
+  rsrc <- terra::sprc(raster_list)
+  cat(paste("  ✓ SpatRasterCollection created with", length(raster_list), "rasters\n\n"))
+  
+  # Mosaic
+  cat("  Mosaicing rasters...\n")
+  farm_mosaic <- terra::merge(rsrc)
+  cat("  ✓ Mosaic successful!\n\n")
+  
+  cat(paste("Farm mosaic dimensions:", dim(farm_mosaic)[1], "×", dim(farm_mosaic)[2], "\n"))
+  cat(paste("Bands:", terra::nlyr(farm_mosaic), "\n"))
+  cat(paste("Band names:", paste(names(farm_mosaic), collapse = ", "), "\n"))
+  cat(paste("CRS:", terra::crs(farm_mosaic), "\n"))
+  
+}, error = function(e) {
+  cat(paste("✗ ERROR during mosaicing:", e$message, "\n"))
+  quit(status = 1)
+})
+
+cat("\n================================================================================\n")
+cat("STEP 5: Extract CI Band\n")
+cat("================================================================================\n")
+
+tryCatch({
+  if ("CI" %in% names(farm_mosaic)) {
+    cat("  CI band found by name\n")
+    farm_ci <- farm_mosaic[["CI"]]
+  } else if (terra::nlyr(farm_mosaic) >= 5) {
+    cat("  CI band not named, using band 5\n")
+    farm_ci <- farm_mosaic[[5]]
+  } else {
+    stop("Could not find CI band (expected band 5 or named 'CI')")
+  }
+  
+  cat(paste("  ✓ CI band extracted\n"))
+  cat(paste("    Dimensions:", dim(farm_ci)[1], "×", dim(farm_ci)[2], "\n"))
+  cat(paste("    Data range:", round(terra::minmax(farm_ci)[1], 2), "to", round(terra::minmax(farm_ci)[2], 2), "\n"))
+  cat(paste("    NA values:", sum(is.na(terra::values(farm_ci))), "\n\n"))
+  
+}, error = function(e) {
+  cat(paste("✗ ERROR extracting CI band:", e$message, "\n"))
+  quit(status = 1)
+})
+
+cat(paste(strrep("=", 80), "\n"))
+cat(paste("STEP 6: Load Field Boundaries for Visualization\n"))
+cat(paste(strrep("=", 80), "\n"))
+
+tryCatch({
+  boundaries_result <- load_field_boundaries(paths$data_dir)
+  
+  if (is.list(boundaries_result) && "field_boundaries_sf" %in% names(boundaries_result)) {
+    field_boundaries_sf <- boundaries_result$field_boundaries_sf
+  } else {
+    field_boundaries_sf <- boundaries_result
+  }
+  
+  if (nrow(field_boundaries_sf) == 0) {
+    stop("No field boundaries loaded")
+  }
+  
+  AllPivots0 <- field_boundaries_sf %>%
+    dplyr::filter(!is.na(field), !is.na(sub_field))
+  
+  cat(paste("  ✓ Field boundaries loaded\n"))
+  cat(paste("    Fields:", nrow(AllPivots0), "\n"))
+  cat(paste("    CRS:", sf::st_crs(AllPivots0)$epsg, "\n\n"))
+  
+}, error = function(e) {
+  cat(paste("✗ ERROR loading field boundaries:", e$message, "\n"))
+  AllPivots0 <- NULL
+})
+
+cat("\n================================================================================\n")
+cat("STEP 7: Test ggplot Visualization\n")
+cat("================================================================================\n")
+
+tryCatch({
+  cat("  Reprojecting raster and boundaries to EPSG:4326 for OSM basemap...\n")
+  target_crs <- "EPSG:4326"
+  farm_ci_ll <- farm_ci
+  AllPivots0_ll <- AllPivots0
+
+  if (!terra::is.lonlat(farm_ci)) {
+    farm_ci_ll <- terra::project(farm_ci, target_crs, method = "bilinear")
+  }
+  if (!is.null(AllPivots0)) {
+    AllPivots0_ll <- sf::st_transform(AllPivots0, 4326)
+  }
+
+  # Ensure boundaries align with raster extent to avoid plotting issues
+  sf::sf_use_s2(FALSE)
+  if (!is.null(AllPivots0_ll)) {
+    AllPivots0_ll <- sf::st_make_valid(AllPivots0_ll)
+    crop_bbox_current <- sf::st_as_sfc(sf::st_bbox(terra::ext(farm_ci_ll), crs = 4326))
+    AllPivots0_ll <- sf::st_intersection(AllPivots0_ll, crop_bbox_current)
+    AllPivots0_ll <- sf::st_collection_extract(AllPivots0_ll, "POLYGON")
+  }
+
+  bounds_df <- NULL
+  labels_df <- NULL
+  if (!is.null(AllPivots0_ll)) {
+    bounds_coords <- sf::st_coordinates(AllPivots0_ll)
+    bounds_df <- as.data.frame(bounds_coords)
+    bounds_df$group <- interaction(bounds_df$L1, bounds_df$L2, drop = TRUE)
+    label_pts <- sf::st_point_on_surface(AllPivots0_ll)
+    labels_df <- cbind(as.data.frame(sf::st_coordinates(label_pts)), sub_field = label_pts$sub_field)
+  }
+
+  cat("  Converting raster to data.frame...\n")
+  ci_df <- as.data.frame(farm_ci_ll, xy = TRUE, na.rm = FALSE)
+  colnames(ci_df) <- c("x", "y", "ci_value")
+  
+  cat(paste("  Data.frame dimensions:", nrow(ci_df), "rows ×", ncol(ci_df), "columns\n"))
+  cat(paste("  Non-NA pixels:", sum(!is.na(ci_df$ci_value)), "\n\n"))
+  
+  cat("  Building ggplot map with OSM basemap...\n")
+  
+  ci_ext <- terra::ext(farm_ci_ll)
+  map <- ggplot2::ggplot() +
+    ggspatial::annotation_map_tile(
+      type = "osm",
+      zoom = 14,
+      alpha = 0.4
+    ) +
+    ggplot2::geom_raster(
+      data = ci_df,
+      ggplot2::aes(x = x, y = y, fill = ci_value)
+    ) +
+    ggplot2::scale_fill_viridis_c(
+      name = "Chlorophyll Index (CI)",
+      limits = c(1, 8),
+      direction = -1,
+      na.value = "transparent",
+      oob = scales::squish
+    ) +
+    ggplot2::coord_sf(
+      crs = 4326,
+      xlim = c(ci_ext$xmin, ci_ext$xmax),
+      ylim = c(ci_ext$ymin, ci_ext$ymax),
+      expand = FALSE
+    )
+  
+  if (!is.null(bounds_df)) {
+    map4 <- map + ggplot2::geom_path(
+      data = bounds_df,
+      ggplot2::aes(x = X, y = Y, group = group),
+      color = "black",
+      linewidth = 0.4
+    )
+  }
+
+  if (!is.null(labels_df)) {
+    map5 <- map4 + ggplot2::geom_text(
+      data = labels_df,
+      ggplot2::aes(x = X, y = Y, label = sub_field),
+      size = 3,
+      color = "black"
+    )
+  }
+  
+  map6 <- map5 +
+    ggspatial::annotation_scale(
+      location = "br",
+      width_hint = 0.25
+    ) +
+    ggplot2::theme_void() +
+    ggplot2::theme(
+      legend.position = "bottom",
+      legend.direction = "horizontal",
+      plot.title = ggplot2::element_text(hjust = 0.5, size = 12, face = "bold")
+    ) +
+    ggplot2::labs(
+      title = paste("Test: Farm-Level CI Overview - Week", sprintf("%02d", current_week), "of", current_iso_year)
+    )
+  
+  cat("  ✓ Map object created successfully!\n\n")
+  
+  # Try to save the map
+  output_path <- paste0("test_overview_map_", project_dir, "_w", sprintf("%02d", current_week), "_", current_iso_year, ".png")
+  cat(paste("  Saving test map to:", output_path, "\n"))
+  
+  tryCatch({
+    ggplot2::ggsave(output_path, map, width = 12, height = 10, dpi = 150)
+    cat(paste("  ✓ Map saved successfully!\n"))
+  }, error = function(e) {
+    cat(paste("  ✗ Could not save map:", e$message, "\n"))
+  })
+  
+}, error = function(e) {
+  cat(paste("✗ ERROR in ggplot visualization:", e$message, "\n"))
+  cat(paste("   Full error:", deparse(e), "\n"))
+  quit(status = 1)
+})
+
+cat("\n================================================================================\n")
+cat("SUCCESS: All steps completed!\n")
+cat("================================================================================\n")
+cat("Summary:\n")
+cat(paste("  - Loaded", loaded_count, "field mosaics\n"))
+cat(paste("  - Created farm-level mosaic\n"))
+cat(paste("  - Extracted CI band\n"))
+cat(paste("  - Created ggplot visualization with OSM basemap\n"))
+cat("\nThe aggregation pipeline is working correctly.\n")
+cat("If the report still shows no maps, check the report date/week combination.\n")