Enhance project configuration and reporting utilities; update .gitignore for PNG exceptions, add CI change thresholds, and improve historical data handling in KPI calculations.

r_app/.gitignore

@@ -8,8 +8,14 @@ renv
 *.tmp
 *.swp
 *.save
+
+# Ignore ALL PNG files by default (generated outputs, analysis plots, etc.)
 *.png
 
+# EXCEPTIONS: Explicitly track intentional PNG assets
+# Uncomment or add lines below for PNG files that should be committed to git
+!r_app/CI_graph_example.png
+
 # Ignore files related to Rproj
 .Rproj.user/
 .Rhistory

r_app/80_utils_agronomic_support.R

@@ -24,8 +24,6 @@ library(tidyr)
 library(readxl)
 library(writexl)
 library(spdep)
-library(caret)
-library(CAST)
 
 # ============================================================================
 # SHARED HELPER FUNCTIONS (NOW IN 80_UTILS_COMMON.R)
@@ -588,7 +586,7 @@ create_summary_tables <- function(all_kpis) {
 #' @param current_year Current year
 #'
 #' @return Data frame with one row per field, all KPI columns
-create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_week, current_year) {
+create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_week, current_year, current_stats = NULL) {
   
   # Start with field identifiers AND field_idx for joining
   result <- field_boundaries_sf %>%
@@ -602,6 +600,20 @@ create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_wee
     ) %>%
     select(field_idx, Field_id, Field_name, Week, Year)
   
+  # ============================================
+  # GROUP 0: MEAN CI (from field statistics)
+  # ============================================
+  if (!is.null(current_stats)) {
+    result <- result %>%
+      left_join(
+        current_stats %>%
+          select(Field_id, Mean_CI),
+        by = "Field_id"
+      )
+  } else {
+    result$Mean_CI <- NA_real_
+  }
+  
   # ============================================
   # GROUP 1: FIELD UNIFORMITY (KPI 1)
   # ============================================
@@ -609,7 +621,8 @@ create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_wee
     left_join(
       all_kpis$uniformity %>% 
         select(field_idx, CV = cv_value, 
-               Uniformity_Category = uniformity_category),
+               Uniformity_Category = uniformity_category,
+               Uniformity_Interpretation = interpretation),
       by = "field_idx"
     )
   
@@ -915,9 +928,95 @@ calculate_all_field_analysis_agronomic_support <- function(
                                            data_dir = data_dir, project_dir = project_dir)
   
   message("Calculating KPI 4: Growth Decline...")
-  growth_decline_kpi <- calculate_growth_decline_kpi(
+  
-  ci_pixels_by_field  
+  # Load historical field statistics to build weekly mean CI time series per field
-  )
+  # (growth_decline_kpi expects temporal series, not spatial pixel arrays)
+  weekly_mean_ci_by_field <- list()
+  
+  # Build list of weekly mean CI values for each field (4-week lookback)
+  for (field_idx in seq_len(nrow(field_boundaries_sf))) {
+    weekly_ci_values <- c()
+  }
+  
+  # Try to load historical data for trend calculation
+  if (!is.null(output_dir) && !is.null(project_dir)) {
+    tryCatch({
+      historical_data <- load_historical_field_data(
+        project_dir = project_dir,
+        current_week = current_week,
+        current_year = current_year,
+        reports_dir = output_dir,
+        num_weeks = 4,
+        auto_generate = FALSE,
+        field_boundaries_sf = field_boundaries_sf
+      )
+      
+      if (!is.null(historical_data) && length(historical_data) > 0) {
+        message("  Building weekly mean CI time series from historical data...")
+        
+        # Initialize list with empty vectors for each field
+        for (field_idx in seq_len(nrow(field_boundaries_sf))) {
+          weekly_mean_ci_by_field[[field_idx]] <- c()
+        }
+        
+        # Extract Mean_CI from each historical week (reverse order to go chronologically)
+        for (hist_idx in rev(seq_along(historical_data))) {
+          hist_week <- historical_data[[hist_idx]]
+          hist_data <- hist_week$data
+          
+          # Extract Mean_CI column if available
+          if ("Mean_CI" %in% names(hist_data)) {
+            # Match fields between historical data and field_boundaries
+            for (field_idx in seq_len(nrow(field_boundaries_sf))) {
+              field_name <- field_boundaries_sf$field[field_idx]
+              
+              # Find matching row in historical data by field name/ID
+              field_row <- which(
+                (hist_data$Field_id == field_name | hist_data$Field_name == field_name) &
+                !is.na(hist_data$Mean_CI)
+              )
+              
+              if (length(field_row) > 0) {
+                mean_ci_val <- as.numeric(hist_data$Mean_CI[field_row[1]])
+                if (!is.na(mean_ci_val)) {
+                  weekly_mean_ci_by_field[[field_idx]] <- c(weekly_mean_ci_by_field[[field_idx]], mean_ci_val)
+                }
+              }
+            }
+          }
+        }
+        
+        message(paste("  ✓ Loaded weekly Mean_CI for", sum(sapply(weekly_mean_ci_by_field, length) > 0), "fields"))
+      }
+    }, error = function(e) {
+      message(paste("  Note: Could not load historical field data for trend analysis:", e$message))
+    })
+  }
+  
+  # If no historical data available, create empty vectors (will result in "Insufficient data")
+  if (length(weekly_mean_ci_by_field) == 0 || all(sapply(weekly_mean_ci_by_field, length) == 0)) {
+    message("  Warning: No historical weekly CI data available - using current week only")
+    for (field_idx in seq_len(nrow(field_boundaries_sf))) {
+      # Use current week mean CI as single-point series (insufficient for trend)
+      if (!is.null(current_stats) && nrow(current_stats) > 0) {
+        field_name <- field_boundaries_sf$field[field_idx]
+        matching_row <- which(
+          (current_stats$Field_id == field_name | current_stats$Field_name == field_name) &
+          !is.na(current_stats$Mean_CI)
+        )
+        if (length(matching_row) > 0) {
+          weekly_mean_ci_by_field[[field_idx]] <- c(as.numeric(current_stats$Mean_CI[matching_row[1]]))
+        } else {
+          weekly_mean_ci_by_field[[field_idx]] <- NA_real_
+        }
+      } else {
+        weekly_mean_ci_by_field[[field_idx]] <- NA_real_
+      }
+    }
+  }
+  
+  # Calculate growth decline using weekly time series (not spatial pixel arrays)
+  growth_decline_kpi <- calculate_growth_decline_kpi(weekly_mean_ci_by_field)
   
   message("Calculating KPI 5: Field Patchiness...")
   # Calculate patchiness using both Gini coefficient and Moran's I spatial clustering
@@ -943,6 +1042,16 @@ calculate_all_field_analysis_agronomic_support <- function(
     # Use the common wrapper function (same as cane supply)
     gap_scores_result <- calculate_gap_scores(per_field_files, field_boundaries_sf)
     
+    # Guard against NULL or empty result from calculate_gap_scores
+    if (is.null(gap_scores_result) || nrow(gap_scores_result) == 0) {
+      message("  Warning: calculate_gap_scores returned NULL/empty - creating fallback")
+      gap_scores_result <- data.frame(
+        Field_id = field_boundaries_sf$field,
+        gap_score = NA_real_,
+        stringsAsFactors = FALSE
+      )
+    }
+    
     # Convert to the format expected by orchestrator
     gap_filling_kpi <- gap_scores_result %>%
       mutate(field_idx = match(Field_id, field_boundaries_sf$field)) %>%
@@ -999,7 +1108,8 @@ calculate_all_field_analysis_agronomic_support <- function(
    field_boundaries_sf = field_boundaries_sf,
    all_kpis = all_kpis,
    current_week = current_week,
-   current_year = current_year
+   current_year = current_year,
+   current_stats = current_stats
   )
 
   # Create summary tables

r_app/80_utils_cane_supply.R

@@ -27,6 +27,15 @@ library(tidyr)
 library(readxl)
 library(writexl)
 
+# ============================================================================
+# ALERT THRESHOLDS & CONFIGURATION CONSTANTS
+# ============================================================================
+
+# CI change thresholds for alert categorization
+# These values are project-standard and should be consistent across all workflows
+CI_CHANGE_DECLINE_THRESHOLD <- -0.5    # Weekly CI change threshold for decline alerts
+CI_CHANGE_INCREASE_THRESHOLD <- 0.5    # Weekly CI change threshold for increase alerts
+
 # ============================================================================
 # ANGATA-SPECIFIC HELPER FUNCTIONS (Placeholder Section)
 # ============================================================================
@@ -139,7 +148,7 @@ categorize_cv_trend_long_term <- function(cv_slope) {
 #' Determine status alert based on harvest probability and crop health
 #' Priority order:
 #' 1. harvest_ready (imminent + mature ≥12 months)
-#' 2. decline_stress (drop ≥2 points but still >1.5)
+#' 2. decline_stress (drop ≥CI_CHANGE_DECLINE_THRESHOLD but still >1.5)
 #' 3. harvested_bare (Mean CI < 1.5)
 #' @param imminent_prob Numeric harvest probability
 #' @param age_week Numeric age in weeks
@@ -152,8 +161,8 @@ calculate_status_alert <- function(imminent_prob, age_week, weekly_ci_change, me
     return("harvest_ready")
   }
   
-  # Priority 2: Strong decline
+  # Priority 2: Strong decline (using configurable threshold)
-  if (!is.na(weekly_ci_change) && weekly_ci_change <= -2.0 && !is.na(mean_ci) && mean_ci > 1.5) {
+  if (!is.na(weekly_ci_change) && weekly_ci_change <= CI_CHANGE_DECLINE_THRESHOLD && !is.na(mean_ci) && mean_ci > 1.5) {
     return("decline_stress")
   }
   
@@ -582,7 +591,7 @@ calculate_field_analysis_cane_supply <- function(setup,
   
   # ========== PHASE 6: LOAD HARVEST PROBABILITIES ==========
   message("\n4. Loading harvest probabilities from script 31...")
-  harvest_prob_dir <- file.path(data_dir, "..", "reports", "kpis", "field_stats")
+  harvest_prob_dir <- setup$kpi_field_stats_dir
   harvest_prob_file <- file.path(harvest_prob_dir, 
                                   sprintf("%s_harvest_imminent_week_%02d_%d.csv", project_dir, current_week, current_year))
   message(paste("  Looking for:", harvest_prob_file))
@@ -634,13 +643,23 @@ calculate_field_analysis_cane_supply <- function(setup,
   #   print(head(field_analysis_df[, available_cols], 10))
   # }
   
-  # # ========== PHASE 10: CALCULATE FARM-LEVEL KPIS ==========
+  # ========== PHASE 10: CALCULATE FARM-LEVEL KPIS ==========
   # farm_kpi_results <- calculate_farm_level_kpis(
   #   field_analysis_df,
   #   current_week,
   #   current_year,
   #   end_date
   # )
+  
+  # For now, farm-level KPIs are not implemented in CANE_SUPPLY workflow
+  farm_kpi_results <- NULL
+  
+  # ========== RETURN RESULTS ==========
+  return(list(
+    field_analysis_df = field_analysis_df,
+    farm_kpi_results = farm_kpi_results,
+    export_paths = export_paths
+  ))
 }
 
 # ============================================================================

r_app/80_utils_common.R

@@ -17,6 +17,25 @@
 #   centralized in the orchestrator script.
 # ============================================================================
 
+# ============================================================================
+# LOAD PROJECT CONFIGURATION (Guard against re-sourcing)
+# ============================================================================
+# Ensure parameters_project.R has been sourced to provide global configuration
+# (PROJECT, data_dir, field_boundaries_path, etc.). Use a sentinel to avoid double-sourcing.
+if (!exists("PROJECT", envir = .GlobalEnv)) {
+  tryCatch({
+    source(here::here("r_app", "parameters_project.R"))
+  }, error = function(e) {
+    # Fallback: try relative path if here() doesn't work
+    tryCatch({
+      source("parameters_project.R")
+    }, error = function(e2) {
+      warning(paste("Could not source parameters_project.R:", e2$message, 
+                    "- using defaults or expecting caller to set PROJECT/data_dir"))
+    })
+  })
+}
+
 # ============================================================================
 # CONSTANTS (from 80_calculate_kpis.R)
 # ============================================================================
@@ -495,8 +514,15 @@ calculate_gap_scores <- function(per_field_files, field_boundaries_sf) {
       dplyr::summarise(gap_score = mean(gap_score, na.rm = TRUE), .groups = "drop")
     
     message(paste("  ✓ Calculated gap scores for", nrow(gap_scores_df), "fields"))
-    message(paste("    Gap score range:", round(min(gap_scores_df$gap_score, na.rm=TRUE), 2), "-", 
+    
-                  round(max(gap_scores_df$gap_score, na.rm=TRUE), 2), "%"))
+    # Guard against all-NA values which would produce Inf/-Inf warnings
+    if (any(is.finite(gap_scores_df$gap_score))) {
+      min_score <- round(min(gap_scores_df$gap_score, na.rm = TRUE), 2)
+      max_score <- round(max(gap_scores_df$gap_score, na.rm = TRUE), 2)
+      message(paste("    Gap score range:", min_score, "-", max_score, "%"))
+    } else {
+      message("    Gap score range: All values are NA (no valid gap scores)")
+    }
   } else {
     message("  WARNING: No gap scores calculated from per-field mosaics")
     gap_scores_df <- NULL
@@ -645,6 +671,8 @@ load_harvest_data <- function(data_dir) {
       if (all(required_cols %in% names(harvesting_data))) {
         # Convert to data frame and ensure column types
         harvesting_data <- as.data.frame(harvesting_data)
+        # CRITICAL: Coerce field to character to preserve leading zeros (e.g., "01", "02")
+        harvesting_data$field <- as.character(harvesting_data$field)
         harvesting_data$year <- as.numeric(harvesting_data$year)
         harvesting_data$tonnage_ha <- as.numeric(harvesting_data$tonnage_ha)
         
@@ -664,7 +692,12 @@ load_harvest_data <- function(data_dir) {
   # Fallback: create empty data frame if loading failed
   if (is.null(harvesting_data)) {
     message("  WARNING: No harvest data available. TCH yield prediction will use graceful fallback (NA values)")
-    harvesting_data <- data.frame(field = character(), year = numeric(), tonnage_ha = numeric())
+    harvesting_data <- data.frame(
+      field = character(),   # Explicitly character to preserve leading zeros when data is added
+      year = numeric(),
+      tonnage_ha = numeric(),
+      stringsAsFactors = FALSE
+    )
   }
   
   return(harvesting_data)

r_app/90_CI_report_with_kpis_agronomic_support.Rmd

@@ -376,13 +376,13 @@ prev_week_1_date <- report_date_obj - 7
 prev_week_2_date <- report_date_obj - 14
 prev_week_3_date <- report_date_obj - 21
 
-week_minus_1 <- lubridate::isoweek(prev_week_1_date)
+week_minus_1 <- sprintf("%02d", lubridate::isoweek(prev_week_1_date))
 week_minus_1_year <- lubridate::isoyear(prev_week_1_date)
 
-week_minus_2 <- lubridate::isoweek(prev_week_2_date)
+week_minus_2 <- sprintf("%02d", lubridate::isoweek(prev_week_2_date))
 week_minus_2_year <- lubridate::isoyear(prev_week_2_date)
 
-week_minus_3 <- lubridate::isoweek(prev_week_3_date)
+week_minus_3 <- sprintf("%02d", lubridate::isoweek(prev_week_3_date))
 week_minus_3_year <- lubridate::isoyear(prev_week_3_date)
 
 # Format current week with leading zeros
@@ -468,10 +468,14 @@ if (!is.null(CI_quadrant) && nrow(CI_quadrant) > 0) {
 <span style="font-size:100pt; line-height:1.0; font-weight:700;">Satellite Based Field Reporting</span>
 :::
 
+
+
 ::: {custom-style="Cover_subtitle" style="text-align:center; margin-top:18px;"}
 <span style="font-size:20pt; font-weight:600;">Chlorophyll Index (CI) Monitoring Report — `r toupper(params$data_dir)` Farm (Week `r { rd <- params$report_date; rd <- if (inherits(rd, "Date")) rd else suppressWarnings(as.Date(rd)); if (is.na(rd)) rd <- Sys.Date(); if (!is.null(params$week)) params$week else format(rd, '%V') }`, `r { rd <- params$report_date; rd <- if (inherits(rd, "Date")) rd else suppressWarnings(as.Date(rd)); if (is.na(rd)) rd <- Sys.Date(); format(rd, '%Y') }`)</span>
 :::
 
+\newpage
+
 ## Report Summary
 
 **Farm Location:** `r toupper(project_dir)` Estate  
@@ -907,8 +911,8 @@ tryCatch({
   
   # 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_1 <- aggregate_mosaics_safe(as.numeric(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")
+  farm_mosaic_minus_3 <- aggregate_mosaics_safe(as.numeric(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
@@ -1547,7 +1551,6 @@ if (!exists("field_details_table") || is.null(field_details_table) || nrow(field
       select(
         Field = Field_id,
         `Field Size (acres)` = field_size_acres,
-        `Growth Uniformity` = Uniformity_Interpretation,
         `Mean CI` = Mean_CI,
         `Weekly CI Change` = Weekly_CI_Change,
         `Yield Forecast (t/ha)` = TCH_Forecasted,
@@ -1561,7 +1564,6 @@ if (!exists("field_details_table") || is.null(field_details_table) || nrow(field
       select(
         Field = Field_id,
         `Field Size (acres)` = field_size_acres,
-        `Growth Uniformity` = Uniformity_Interpretation,
         `Mean CI` = Mean_CI,
         `Yield Forecast (t/ha)` = TCH_Forecasted,
         `Gap Score` = Gap_Score,
@@ -1616,8 +1618,9 @@ The Chlorophyll Index (CI) is a vegetation index that measures the relative amou
 
 CI values typically range from 0 (bare soil or severely stressed vegetation) to 7+ (very healthy, dense vegetation). For sugarcane, values between 3-7 generally indicate good crop health, depending on the growth stage.
 
+<div align="center">
 ![Chlorophyll Index Example](CI_graph_example.png)
-
+</div>
 
 ### What You'll Find in This Report:
 

r_app/91_CI_report_with_kpis_cane_supply.Rmd

@@ -487,7 +487,7 @@ if (exists("summary_data") && !is.null(summary_data) && "field_analysis" %in% na
 
 ## 1.1 Overview of cane supply area, showing zones with number of acres being harvest ready  
 
-```{r overview_map, fig.width=9, fig.height=7, fig.align="center", echo=FALSE, message=FALSE, warning=FALSE}
+```{r overview_map, fig.width=8, fig.height=7, fig.align="center", echo=FALSE, message=FALSE, warning=FALSE}
 # Create a hexbin overview map with ggplot
 tryCatch({
   # Use per-field field_analysis data from RDS (already loaded in load_kpi_data chunk)
@@ -643,7 +643,7 @@ tryCatch({
 })
 ```
 
-\newpage
+
 ## 1.2 Key Performance Indicators 
 
 ```{r combined_kpi_table, echo=FALSE, results='asis'}
@@ -931,9 +931,9 @@ CI values typically range from 0 (bare soil or severely stressed vegetation) to
 
 
 
-```{r ci_fig, echo=FALSE, fig.align='right', out.width='40%', fig.cap="Chlorophyll Index Example"}
+<div align="center">
-knitr::include_graphics("CI_graph_example.png")
+![Chlorophyll Index Example](CI_graph_example.png)
-```
+</div>
 
 
 ### Data File Structure and Columns

r_app/parameters_project.R

@@ -623,13 +623,19 @@ detect_tile_structure_from_merged_final <- function(merged_final_tif_dir, daily_
 
 #' Detect mosaic mode from project structure
 #'
+#' Determine mosaic architecture (legacy detection function)
+#'
+#' NOTE: This is a legacy function kept for backward compatibility. 
+#' The project has moved to per-field (single-file) architecture.
+#' `weekly_tile_max` is no longer created in all_dirs, so this will always return "single-file"
+#'
 #' Determines if project uses "tiled" (legacy) or "single-file" (per-field) mosaics
 #'
 #' @param project_dir Character. Project name
-#' @return Character. "tiled" or "single-file"
+#' @return Character. "tiled" or "single-file" (now always "single-file")
 detect_mosaic_mode <- function(project_dir) {
   # Per-field architecture is standard - always return "single-file"
-  # unless weekly_tile_max directory exists with content
+  # Legacy support: check if weekly_tile_max exists (it won't in standard setup)
   mosaic_tiled_dir <- file.path("laravel_app", "storage", "app", project_dir, "weekly_tile_max")
   
   if (dir.exists(mosaic_tiled_dir) && length(list.files(mosaic_tiled_dir)) > 0) {
@@ -662,11 +668,15 @@ get_project_storage_path <- function(project_dir, subdir = NULL) {
   return(path)
 }
 
-#' Get mosaic directory
+#' Get mosaic directory (legacy function)
+#'
+#' NOTE: This is a legacy helper kept for backward compatibility.
+#' In the standard per-field workflow, this returns weekly_mosaic directory.
+#' The "tiled" mode is no longer created (weekly_tile_max_dir was removed from all_dirs).
 #'
 #' @param project_dir Character. Project name
-#' @param mosaic_mode Character. "tiled" or "single-file"
+#' @param mosaic_mode Character. "tiled" or "single-file" (auto-detects if "auto")
-#' @return Character. Full path to mosaic directory
+#' @return Character. Full path to mosaic directory (typically weekly_mosaic)
 get_mosaic_dir <- function(project_dir, mosaic_mode = "auto") {
   if (mosaic_mode == "auto") {
     mosaic_mode <- detect_mosaic_mode(project_dir)
@@ -718,11 +728,14 @@ check_harvest_output_exists <- function(project_dir, week_num, year_num) {
   file.exists(path)
 }
 
-#' Get mosaic verification directory
+#' Get mosaic verification directory (legacy function)
+#'
+#' NOTE: This is a legacy helper kept for backward compatibility.
+#' Standard workflow uses weekly_mosaic; tiled mode is no longer created.
 #'
 #' @param project_dir Character. Project name
 #' @param mosaic_mode Character. "tiled" or "single-file"
-#' @return Character. Full path to mosaic directory
+#' @return Character. Full path to mosaic directory for verification
 get_mosaic_verification_dir <- function(project_dir, mosaic_mode) {
   base <- file.path("laravel_app", "storage", "app", project_dir)
   

r_app/report_utils.R

@@ -802,30 +802,9 @@ get_week_path <- function(mosaic_path, input_date, week_offset) {
     target_week <- sprintf("%02d", lubridate::isoweek(target_date))  # Left-pad week number with a zero if needed
     target_year <- lubridate::isoyear(target_date)
     
-    # Primary approach: Try single-file mosaic path first
+    # Load single-file mosaic for the given week
     path_to_week <- here::here(mosaic_path, paste0("week_", target_week, "_", target_year, ".tif"))
     
-    # Smart fallback: If single-file doesn't exist AND path contains "weekly_mosaic", check for tiles
-    if (!file.exists(path_to_week) && grepl("weekly_mosaic", mosaic_path)) {
-      # Try to locate tile-based mosaics in weekly_tile_max instead
-      tile_mosaic_path <- sub("weekly_mosaic", "weekly_tile_max", mosaic_path)
-      
-      # Look for any tile files matching the week pattern (e.g., week_XX_YYYY_00.tif, week_XX_YYYY_01.tif, etc.)
-      if (dir.exists(tile_mosaic_path)) {
-        tile_files <- list.files(tile_mosaic_path, 
-                                  pattern = paste0("^week_", target_week, "_", target_year, "_(\\d{2})\\.tif$"),
-                                  full.names = TRUE)
-        
-        if (length(tile_files) > 0) {
-          # Found tiles - return the first tile as primary, note that multiple tiles exist
-          safe_log(paste("Single-file mosaic not found for week", target_week, target_year, 
-                        "but found", length(tile_files), "tile files in weekly_tile_max. Using tile approach."), "INFO")
-          # Return first tile - caller should aggregate if needed
-          path_to_week <- tile_files[1]  # Return first tile; downstream can handle multiple tiles
-        }
-      }
-    }
-    
     # Log the path calculation
     safe_log(paste("Calculated path for week", target_week, "of year", target_year, ":", path_to_week), "INFO")
     
@@ -1169,10 +1148,10 @@ generate_field_kpi_summary <- function(field_name, field_details_table, CI_quadr
     }
     
     kpi_text <- paste0(
-      "Size: ", round(field_summary$field_size, 1), " ha | Growth Uniformity: ", field_summary$uniformity_levels, 
+      "Size: ", round(field_summary$field_size * 0.404686, 1), " ha | Mean CI: ", round(field_summary$avg_mean_ci, 2),
+      " | Growth Uniformity: ", field_summary$uniformity_levels, 
       " | ", yield_text, " | Gap Score: ", round(field_summary$max_gap_score, 1), 
-      " | Decline Risk: ", field_summary$highest_decline_risk, " | Weed Risk: ", field_summary$highest_weed_risk, 
+      " | Decline Risk: ", field_summary$highest_decline_risk, " | Weed Risk: ", field_summary$highest_weed_risk
-      " | Mean CI: ", round(field_summary$avg_mean_ci, 2)
     )
     
     # Wrap in smaller text HTML tags for Word output