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SmartCane/r_app/experiments/crop_messaging/young_field_analysis.R

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#
# YOUNG_FIELD_ANALYSIS.R
# ======================
# Specialized analysis for young sugarcane fields (0-12 months)
# Focuses on:
# 1. Germination issues (gap detection, uneven emergence)
# 2. Weed pressure detection (both patchy and uniform weeds)
# 3. Age-specific intervention recommendations
#
# Usage: Rscript young_field_analysis.R [current_week] [previous_week] [project_dir]
#
# 1. Load required packages
# -----------------------
suppressPackageStartupMessages({
library(sf)
library(terra)
library(tidyverse)
library(lubridate)
library(here)
library(readxl) # For reading harvest_data.xlsx
library(spdep) # For spatial statistics
})
# 2. Young field analysis configuration
# -----------------------------------
# Age thresholds (months)
GERMINATION_PHASE_MAX <- 4 # 0-4 months: critical germination period
WEED_CRITICAL_PHASE_MAX <- 8 # 4-8 months: weeds most competitive
YOUNG_FIELD_MAX <- 12 # 0-12 months: all young field analysis
# Detection thresholds
WEED_CI_CHANGE_THRESHOLD <- 1.5 # Weekly CI increase indicating possible weeds
SEVERE_WEED_CI_CHANGE <- 2.0 # Severe weed pressure
GERMINATION_GAP_CV_THRESHOLD <- 0.30 # High CV in young fields = poor germination
LOW_CI_GERMINATION_THRESHOLD <- 0.5 # Very low CI = poor emergence
# 3. Enhanced spectral indices for young crop detection
# ---------------------------------------------------
#' Calculate enhanced vegetation indices from RGBNIR bands
#' @param red Red band raster
#' @param green Green band raster
#' @param blue Blue band raster
#' @param nir Near-infrared band raster
#' @return List of vegetation index rasters
calculate_enhanced_indices <- function(red, green, blue, nir) {
cat("Calculating enhanced vegetation indices...\n")
# 1. Standard NDVI (baseline)
ndvi <- (nir - red) / (nir + red)
names(ndvi) <- "NDVI"
# 2. Green NDVI (more sensitive to early vegetation)
gndvi <- (nir - green) / (nir + green)
names(gndvi) <- "GNDVI"
# 3. Excess Green Index (early vegetation detection)
exg <- 2*green - red - blue
names(exg) <- "ExG"
# 4. Visible Atmospherically Resistant Index
vari <- (green - red) / (green + red + blue)
names(vari) <- "VARI"
# 5. Green Ratio Vegetation Index
grvi <- green / red
names(grvi) <- "GRVI"
# 6. Chlorophyll Index (CI = NIR / Green - 1, NOT NIR/Red)
# *** CRITICAL: Correct formula uses GREEN band, not RED ***
ci <- nir / green - 1
names(ci) <- "CI"
return(list(
NDVI = ndvi,
GNDVI = gndvi,
ExG = exg,
VARI = vari,
GRVI = grvi,
CI = ci
))
}
#' Create visual maps of different indices for comparison
#' @param indices_list List of index rasters
#' @param field_boundaries SF object with field boundaries
#' @param output_dir Directory to save maps
#' @param week_num Week number for filename
create_index_comparison_maps <- function(indices_list, field_boundaries, output_dir, week_num) {
if (!dir.exists(output_dir)) {
dir.create(output_dir, recursive = TRUE)
}
cat("Creating visual comparison maps and TIF exports...\n")
# Create individual maps for each index
for (index_name in names(indices_list)) {
tryCatch({
index_raster <- indices_list[[index_name]]
# Create filenames
map_filename <- file.path(output_dir, paste0("week_", sprintf("%02d", week_num), "_", index_name, "_map.png"))
tif_filename <- file.path(output_dir, paste0("week_", sprintf("%02d", week_num), "_", index_name, ".tif"))
# Export TIF file for QGIS
terra::writeRaster(index_raster, tif_filename, overwrite = TRUE)
cat("- Exported TIF:", tif_filename, "\n")
# Create PNG map without field boundaries
png(map_filename, width = 1200, height = 800, res = 150)
# Plot raster only (no field boundaries)
plot(index_raster, main = paste("Week", week_num, "-", index_name))
dev.off()
cat("- Saved map:", map_filename, "\n")
}, error = function(e) {
warning(paste("Error creating outputs for", index_name, ":", e$message))
})
}
cat("✓ Index comparison maps and TIF files created\n")
}
#' Load field age data from existing harvest_data object (loaded in parameters_project.R)
#' @param harvest_data Data frame with harvest/field information
#' @param field_boundaries SF object with field boundaries
#' @return Field boundaries with age information added
#' # harvest_data = harvesting_data
#' # field_boundaries = field_boundaries_sf
load_field_ages_from_existing <- function(harvest_data, field_boundaries) {
cat("Processing field age data from existing harvest_data object...\n")
tryCatch({
# Display structure of harvest data
cat("- Harvest data columns:", paste(colnames(harvest_data), collapse = ", "), "\n")
cat("- Harvest data rows:", nrow(harvest_data), "\n")
# Display field boundaries structure
cat("- Field boundaries columns:", paste(colnames(field_boundaries), collapse = ", "), "\n")
cat("- Field boundaries rows:", nrow(field_boundaries), "\n")
# Join harvest data with field boundaries on field and sub_field
# Convert field and sub_field to character to ensure consistent matching
# Filter for current season only (where season_end is today's date)
current_date <- Sys.Date()
harvest_data_clean <- harvest_data %>%
mutate(
field = as.character(field),
sub_field = as.character(sub_field)
) %>%
# Filter for current season only
filter(season_end == current_date | (season_end >= current_date - 7 & season_end <= current_date)) %>%
select(field, sub_field, age, season_start, season_end, tonnage_ha)
cat("- Harvest data after filtering for current season:", nrow(harvest_data_clean), "rows\n")
if (nrow(harvest_data_clean) == 0) {
cat("⚠️ No current season data found in harvest data\n")
cat("- Looking for season_end near:", current_date, "\n")
# Show available season_end dates to help debug
available_dates <- harvest_data %>%
select(season_end) %>%
distinct() %>%
arrange(season_end)
cat("- Available season_end dates in harvest data:\n")
print(available_dates)
return(field_boundaries)
}
field_boundaries_clean <- field_boundaries %>%
mutate(
field = as.character(field),
sub_field = as.character(sub_field)
)
# Perform the join
field_boundaries_with_age <- field_boundaries_clean %>%
left_join(harvest_data_clean, by = c("field", "sub_field"))
# Check join success
matched_fields <- sum(!is.na(field_boundaries_with_age$age))
total_fields <- nrow(field_boundaries_with_age)
cat("✓ Successfully joined harvest data\n")
cat("- Fields with age data:", matched_fields, "out of", total_fields, "\n")
if (matched_fields > 0) {
age_summary <- field_boundaries_with_age %>%
filter(!is.na(age)) %>%
pull(age) %>%
summary()
cat("- Age range (weeks):", paste(names(age_summary), "=", round(age_summary, 1), collapse = ", "), "\n")
# Convert age from weeks to months for analysis
field_boundaries_with_age <- field_boundaries_with_age %>%
mutate(age_months = round(age / 4.33, 1)) # 4.33 weeks per month average
cat("- Age range (months):", paste(round(range(field_boundaries_with_age$age_months, na.rm = TRUE), 1), collapse = " to "), "\n")
}
return(field_boundaries_with_age)
}, error = function(e) {
warning(paste("Error processing harvest data:", e$message))
cat("Returning original field boundaries without age information\n")
return(field_boundaries)
})
}
#' Detect germination issues in young fields
#' @param indices_list List of vegetation index rasters
#' @param field_boundaries SF object with field boundaries (with age info)
#' @param young_fields_only Logical: analyze only young fields?
#' @return List with germination analysis results
detect_germination_issues <- function(indices_list, field_boundaries, young_fields_only = TRUE) {
cat("=== GERMINATION ANALYSIS ===\n")
germination_results <- list()
field_boundaries_vect <- terra::vect(field_boundaries)
for (i in seq_len(nrow(field_boundaries))) {
field_name <- field_boundaries$field[i]
sub_field_name <- field_boundaries$sub_field[i]
field_id <- paste0(field_name, "_", sub_field_name)
# Get field age if available
field_age_months <- if ("age_months" %in% colnames(field_boundaries) && !is.na(field_boundaries$age_months[i])) {
field_boundaries$age_months[i]
} else {
NA
}
cat("Analyzing germination for field:", field_id)
if (!is.na(field_age_months)) {
cat(" (Age:", field_age_months, "months)")
}
cat("\n")
# Extract field boundary
field_vect <- field_boundaries_vect[i]
# Analyze each index for germination patterns
field_analysis <- list()
for (index_name in names(indices_list)) {
index_raster <- indices_list[[index_name]]
# Extract values for this field
field_values <- terra::extract(index_raster, field_vect, fun = NULL)
valid_values <- unlist(field_values)
valid_values <- valid_values[!is.na(valid_values) & is.finite(valid_values)]
if (length(valid_values) > 10) {
# Calculate germination-relevant metrics
mean_val <- mean(valid_values)
cv_val <- sd(valid_values) / mean_val
min_val <- min(valid_values)
# Only check for germination issues in fields ≤ 4 months old
check_germination <- is.na(field_age_months) || field_age_months <= GERMINATION_PHASE_MAX
if (check_germination) {
# Detect potential germination issues
high_variation <- cv_val > GERMINATION_GAP_CV_THRESHOLD
low_values <- mean_val < LOW_CI_GERMINATION_THRESHOLD
very_low_areas <- sum(valid_values < (mean_val - 2*sd(valid_values))) / length(valid_values) * 100
} else {
# Field too old for germination analysis
high_variation <- FALSE
low_values <- FALSE
very_low_areas <- 0
}
field_analysis[[index_name]] <- list(
mean = mean_val,
cv = cv_val,
min = min_val,
high_variation = high_variation,
low_values = low_values,
very_low_areas_pct = very_low_areas,
n_pixels = length(valid_values),
age_months = field_age_months,
germination_analysis_applied = check_germination
)
}
}
germination_results[[field_id]] <- field_analysis
}
return(germination_results)
}
#' Detect weed pressure using change analysis
#' @param current_indices List of current week indices
#' @param previous_indices List of previous week indices
#' @param field_boundaries SF object with field boundaries
#' @return List with weed detection results
detect_weed_pressure <- function(current_indices, previous_indices, field_boundaries) {
cat("=== WEED PRESSURE DETECTION ===\n")
weed_results <- list()
field_boundaries_vect <- terra::vect(field_boundaries)
for (i in seq_len(nrow(field_boundaries))) {
field_name <- field_boundaries$field[i]
sub_field_name <- field_boundaries$sub_field[i]
field_id <- paste0(field_name, "_", sub_field_name)
cat("Analyzing weed pressure for field:", field_id, "\n")
field_vect <- field_boundaries_vect[i]
field_weed_analysis <- list()
# Analyze change in each index
for (index_name in names(current_indices)) {
if (index_name %in% names(previous_indices)) {
current_raster <- current_indices[[index_name]]
previous_raster <- previous_indices[[index_name]]
# Extract values for both weeks
current_values <- unlist(terra::extract(current_raster, field_vect, fun = NULL))
previous_values <- unlist(terra::extract(previous_raster, field_vect, fun = NULL))
# Clean values
valid_idx <- !is.na(current_values) & !is.na(previous_values) &
is.finite(current_values) & is.finite(previous_values)
current_clean <- current_values[valid_idx]
previous_clean <- previous_values[valid_idx]
if (length(current_clean) > 10) {
# Calculate change metrics
change_values <- current_clean - previous_clean
mean_change <- mean(change_values)
change_cv <- sd(change_values) / abs(mean(current_clean))
# Weed detection criteria
# 1. Significant positive change (weeds growing faster than cane)
rapid_increase <- mean_change >= WEED_CI_CHANGE_THRESHOLD
severe_increase <- mean_change >= SEVERE_WEED_CI_CHANGE
# 2. Percentage of field with rapid increase
rapid_increase_pct <- sum(change_values >= WEED_CI_CHANGE_THRESHOLD) / length(change_values) * 100
# 3. Patchy vs uniform weed patterns
patchy_weeds <- change_cv > 0.5 && rapid_increase_pct > 10 && rapid_increase_pct < 80
uniform_weeds <- change_cv < 0.3 && rapid_increase_pct > 60 # Whole field weeds
# 4. Current vegetation level (high CI + rapid change = likely weeds)
current_mean <- mean(current_clean)
high_current_ci <- current_mean > 2.0 # Adjust threshold as needed
field_weed_analysis[[index_name]] <- list(
mean_change = mean_change,
change_cv = change_cv,
current_mean = current_mean,
rapid_increase = rapid_increase,
severe_increase = severe_increase,
rapid_increase_pct = rapid_increase_pct,
patchy_weeds = patchy_weeds,
uniform_weeds = uniform_weeds,
high_current_ci = high_current_ci,
n_pixels = length(current_clean)
)
}
}
}
weed_results[[field_id]] <- field_weed_analysis
}
return(weed_results)
}
#' Generate intervention recommendations based on detected issues
#' @param field_id Field identifier
#' @param germination_analysis Germination analysis results
#' @param weed_analysis Weed detection results
#' @param field_age_months Field age in months (if available)
#' @return List with recommendations
generate_young_field_recommendations <- function(field_id, germination_analysis, weed_analysis, field_age_months = NA) {
recommendations <- list()
# Priority level: 1=urgent, 2=high, 3=moderate, 4=monitoring
priority <- 4
messages <- c()
interventions <- c()
# Check for germination issues (if CI analysis available and field is young enough)
if ("CI" %in% names(germination_analysis)) {
ci_analysis <- germination_analysis[["CI"]]
# Only apply germination analysis for fields ≤ 4 months old
if (!is.null(ci_analysis$germination_analysis_applied) && ci_analysis$germination_analysis_applied) {
if (ci_analysis$high_variation && ci_analysis$low_values) {
priority <- min(priority, 1)
messages <- c(messages, "🚨 URGENT: Poor germination detected - high variation with low CI values")
interventions <- c(interventions, "Immediate replanting in gap areas", "Check seed quality and planting conditions")
} else if (ci_analysis$high_variation) {
priority <- min(priority, 2)
messages <- c(messages, "⚠️ Alert: Uneven emergence detected - moderate intervention needed")
interventions <- c(interventions, "Monitor gap areas for potential replanting")
}
if (ci_analysis$very_low_areas_pct > 15) {
priority <- min(priority, 2)
messages <- c(messages, paste0("⚠️ Alert: ", round(ci_analysis$very_low_areas_pct, 1), "% of field has very low vegetation"))
interventions <- c(interventions, "Investigate cause of low-performing areas")
}
} else {
# Field too old for germination analysis - check for other issues
if (ci_analysis$cv > 0.5) {
priority <- min(priority, 3)
messages <- c(messages, "📊 Info: Uneven crop growth detected (field too old for replanting)")
interventions <- c(interventions, "Monitor for potential management issues")
}
}
}
# Check for weed pressure (if CI analysis available)
if ("CI" %in% names(weed_analysis)) {
ci_weed <- weed_analysis[["CI"]]
if (ci_weed$severe_increase) {
priority <- min(priority, 1)
messages <- c(messages, paste0("🚨 CRITICAL: Severe weed pressure detected - CI increased by ", round(ci_weed$mean_change, 2)))
interventions <- c(interventions, "Immediate weed control required", "Consider herbicide application")
} else if (ci_weed$rapid_increase) {
priority <- min(priority, 2)
messages <- c(messages, paste0("⚠️ Alert: Weed pressure detected - CI increased by ", round(ci_weed$mean_change, 2)))
interventions <- c(interventions, "Schedule weed control within 1-2 weeks")
}
if (ci_weed$uniform_weeds) {
priority <- min(priority, 2)
messages <- c(messages, paste0("🔶 Pattern: Uniform weed emergence across ", round(ci_weed$rapid_increase_pct, 1), "% of field"))
interventions <- c(interventions, "Broad-scale weed management needed")
} else if (ci_weed$patchy_weeds) {
priority <- min(priority, 3)
messages <- c(messages, paste0("🔶 Pattern: Patchy weed emergence in ", round(ci_weed$rapid_increase_pct, 1), "% of field"))
interventions <- c(interventions, "Targeted spot treatment recommended")
}
}
# Age-specific recommendations
if (!is.na(field_age_months)) {
if (field_age_months <= GERMINATION_PHASE_MAX) {
interventions <- c(interventions, "Critical germination phase - maintain optimal moisture", "Monitor for pest damage")
} else if (field_age_months <= WEED_CRITICAL_PHASE_MAX) {
interventions <- c(interventions, "Peak weed competition period - prioritize weed control")
}
}
# Default monitoring if no issues
if (length(messages) == 0) {
messages <- c("✅ No significant issues detected")
interventions <- c("Continue routine monitoring")
}
return(list(
field_id = field_id,
priority = priority,
messages = messages,
interventions = interventions,
requires_action = priority <= 3
))
}
# 4. Main analysis function
# -----------------------
main_young_field_analysis <- function() {
# Get command line arguments
args <- commandArgs(trailingOnly = TRUE)
current_week <- if (length(args) >= 1) as.numeric(args[1]) else 30
previous_week <- if (length(args) >= 2) as.numeric(args[2]) else 29
project_dir <- if (length(args) >= 3) args[3] else "simba"
cat("=== YOUNG FIELD ANALYSIS SYSTEM ===\n")
cat("Project:", project_dir, "\n")
cat("Analyzing weeks:", previous_week, "→", current_week, "\n\n")
# Load project configuration
assign("project_dir", project_dir, envir = .GlobalEnv)
tryCatch({
source("parameters_project.R")
cat("✓ Project configuration loaded\n")
}, error = function(e) {
tryCatch({
source(here::here("r_app", "parameters_project.R"))
cat("✓ Project configuration loaded from r_app directory\n")
}, error = function(e) {
stop("Failed to load project configuration")
})
})
# Verify required variables
if (!exists("weekly_CI_mosaic") || !exists("field_boundaries_sf")) {
stop("Required project variables not found")
}
# Check if harvest data is available from parameters_project.R
if (exists("harvesting_data") && !is.null(harvesting_data)) {
cat("✓ Harvest data loaded from parameters_project.R\n")
field_boundaries_with_age <- load_field_ages_from_existing(harvesting_data, field_boundaries_sf)
} else {
cat("⚠️ No harvest data found in parameters_project.R\n")
field_boundaries_with_age <- field_boundaries_sf
}
# Construct mosaic file paths
year <- 2025
current_week_file <- sprintf("week_%02d_%d.tif", current_week, year)
previous_week_file <- sprintf("week_%02d_%d.tif", previous_week, year)
current_mosaic_path <- file.path(weekly_CI_mosaic, current_week_file)
previous_mosaic_path <- file.path(weekly_CI_mosaic, previous_week_file)
cat("Current week mosaic:", current_mosaic_path, "\n")
cat("Previous week mosaic:", previous_mosaic_path, "\n")
# Check if files exist
if (!file.exists(current_mosaic_path)) {
stop("Current week mosaic not found: ", current_mosaic_path)
}
if (!file.exists(previous_mosaic_path)) {
stop("Previous week mosaic not found: ", previous_mosaic_path)
}
# Load mosaics and extract bands
cat("\nLoading mosaics and extracting RGBNIR bands...\n")
current_mosaic <- terra::rast(current_mosaic_path)
previous_mosaic <- terra::rast(previous_mosaic_path)
cat("Current mosaic bands:", nlyr(current_mosaic), "\n")
cat("Previous mosaic bands:", nlyr(previous_mosaic), "\n")
# Extract RGBNIR bands (assuming standard order: R=1, G=2, B=3, NIR=4)
current_red <- current_mosaic[[1]]
current_green <- current_mosaic[[2]]
current_blue <- current_mosaic[[3]]
current_nir <- current_mosaic[[4]]
previous_red <- previous_mosaic[[1]]
previous_green <- previous_mosaic[[2]]
previous_blue <- previous_mosaic[[3]]
previous_nir <- previous_mosaic[[4]]
# Calculate enhanced indices for both weeks
cat("\nCalculating vegetation indices...\n")
current_indices <- calculate_enhanced_indices(current_red, current_green, current_blue, current_nir)
previous_indices <- calculate_enhanced_indices(previous_red, previous_green, previous_blue, previous_nir)
# Create output directory for maps
output_dir <- file.path(dirname(weekly_CI_mosaic), "young_field_analysis")
# Create visual comparison maps
cat("\nCreating visual maps...\n")
# Check if maps already exist before creating them
current_week_maps_exist <- all(sapply(names(current_indices), function(index_name) {
map_file <- file.path(output_dir, paste0("week_", sprintf("%02d", current_week), "_", index_name, "_map.png"))
tif_file <- file.path(output_dir, paste0("week_", sprintf("%02d", current_week), "_", index_name, ".tif"))
file.exists(map_file) && file.exists(tif_file)
}))
previous_week_maps_exist <- all(sapply(names(previous_indices), function(index_name) {
map_file <- file.path(output_dir, paste0("week_", sprintf("%02d", previous_week), "_", index_name, "_map.png"))
tif_file <- file.path(output_dir, paste0("week_", sprintf("%02d", previous_week), "_", index_name, ".tif"))
file.exists(map_file) && file.exists(tif_file)
}))
if (!current_week_maps_exist) {
cat("Creating current week maps (week", current_week, ")...\n")
create_index_comparison_maps(current_indices, field_boundaries_sf, output_dir, current_week)
} else {
cat("Current week maps already exist, skipping creation...\n")
}
if (!previous_week_maps_exist) {
cat("Creating previous week maps (week", previous_week, ")...\n")
create_index_comparison_maps(previous_indices, field_boundaries_sf, output_dir, previous_week)
} else {
cat("Previous week maps already exist, skipping creation...\n")
}
# Perform germination analysis
cat("\nPerforming germination analysis...\n")
germination_results <- detect_germination_issues(current_indices, field_boundaries_with_age)
# Perform weed detection analysis
cat("\nPerforming weed pressure analysis...\n")
weed_results <- detect_weed_pressure(current_indices, previous_indices, field_boundaries_with_age)
# Generate recommendations for each field
cat("\n=== FIELD RECOMMENDATIONS ===\n")
field_recommendations <- list()
action_needed_fields <- 0
for (field_id in names(germination_results)) {
germination_analysis <- germination_results[[field_id]]
weed_analysis <- if (field_id %in% names(weed_results)) weed_results[[field_id]] else list()
# Get field age if available
field_row <- field_boundaries_with_age[field_boundaries_with_age$field == strsplit(field_id, "_")[[1]][1] &
field_boundaries_with_age$sub_field == strsplit(field_id, "_")[[1]][2], ]
field_age_months <- if (nrow(field_row) > 0 && !is.na(field_row$age_months[1])) field_row$age_months[1] else NA
recommendations <- generate_young_field_recommendations(
field_id,
germination_analysis,
weed_analysis,
field_age_months
)
field_recommendations[[field_id]] <- recommendations
# Print recommendations
cat("\nFIELD:", field_id, "\n")
if (!is.na(field_age_months)) {
cat("Age:", field_age_months, "months\n")
}
cat("Priority Level:", recommendations$priority, "\n")
for (message in recommendations$messages) {
cat("-", message, "\n")
}
if (length(recommendations$interventions) > 0) {
cat("Recommended Actions:\n")
for (intervention in recommendations$interventions) {
cat(" •", intervention, "\n")
}
}
if (recommendations$requires_action) {
action_needed_fields <- action_needed_fields + 1
}
# Show index comparison for this field
if ("CI" %in% names(germination_analysis)) {
ci_stats <- germination_analysis[["CI"]]
cat("CI Stats: Mean =", round(ci_stats$mean, 3),
", CV =", round(ci_stats$cv, 3),
", Low areas =", round(ci_stats$very_low_areas_pct, 1), "%\n")
}
if ("CI" %in% names(weed_analysis)) {
weed_stats <- weed_analysis[["CI"]]
cat("Change Stats: CI Δ =", round(weed_stats$mean_change, 3),
", Rapid increase =", round(weed_stats$rapid_increase_pct, 1), "%\n")
}
}
# Summary
cat("\n=== ANALYSIS SUMMARY ===\n")
cat("Total fields analyzed:", length(field_recommendations), "\n")
cat("Fields requiring action:", action_needed_fields, "\n")
cat("Maps saved to:", output_dir, "\n")
cat("\nIndex comparison maps created for visual inspection:\n")
cat("- NDVI: Standard vegetation index\n")
cat("- GNDVI: Green NDVI (sensitive to early vegetation)\n")
cat("- ExG: Excess Green (early detection)\n")
cat("- VARI: Atmospherically resistant\n")
cat("- GRVI: Green ratio\n")
cat("- CI: Chlorophyll Index (current standard)\n")
cat("\n✓ Young field analysis complete\n")
return(list(
germination_results = germination_results,
weed_results = weed_results,
recommendations = field_recommendations,
indices_calculated = names(current_indices),
maps_created = file.path(output_dir)
))
}
# Run analysis if script called directly
if (sys.nframe() == 0) {
main_young_field_analysis()
}
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