Timon
711a005e52
- Updated settings.local.json to include new read permissions for r_app directory. - Adjusted the harvest readiness condition in 80_utils_cane_supply.R to lower the imminent probability threshold. - Removed unused get_status_trigger function from 80_utils_common.R to streamline code. - Added total area analyzed feature in 90_CI_report_with_kpis_agronomic_support.Rmd, including area calculations in summary tables. - Updated translations_90.json to include new keys for total area analyzed and area label. - Created create_field_checklist.R and create_field_checklist.py scripts to generate Excel checklists from GeoJSON data, sorting fields by area and splitting assignments among team members.
653 lines
26 KiB
R
653 lines
26 KiB
R
# 80_UTILS_CANE_SUPPLY.R
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# ============================================================================
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# CANE SUPPLY CLIENT-SPECIFIC UTILITIES (SCRIPT 80 - CLIENT TYPE: cane_supply)
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#
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# Contains ANGATA and other cane supply-specific KPI and reporting functions.
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#
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# Currently, CANE_SUPPLY clients use the common utilities from 80_utils_common.R:
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# - Weekly statistics (calculate_field_statistics, calculate_kpi_trends)
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# - Field analysis reporting (generate_field_analysis_summary)
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# - Excel export (export_field_analysis_excel)
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#
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# This file is structured to accommodate future ANGATA-specific functionality such as:
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# - Custom yield models
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# - Harvest readiness criteria
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# - Supply chain integration hooks
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# - ANGATA-specific alerting and messaging
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#
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# Orchestrator: (Placeholder - uses common functions)
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# Dependencies: 00_common_utils.R, 80_utils_common.R
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# Used by: 80_calculate_kpis.R (when client_type == "cane_supply")
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# ============================================================================
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library(terra)
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library(sf)
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library(dplyr)
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library(tidyr)
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library(readxl)
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library(writexl)
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# ============================================================================
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# ALERT THRESHOLDS & CONFIGURATION CONSTANTS
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# ============================================================================
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# CI change thresholds for alert categorization and status determination
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# These values are project-standard and should be consistent across all workflows
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CI_CHANGE_RAPID_GROWTH_THRESHOLD <- 0.5 # Weekly CI change for positive growth alert
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CI_CHANGE_POSITIVE_GROWTH_THRESHOLD <- 0.2 # Weekly CI change for acceptable growth
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CI_CHANGE_STABLE_THRESHOLD <- -0.2 # Weekly CI change for stable status (between -0.2 and +0.2)
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CI_CHANGE_STRESS_TREND_THRESHOLD <- -0.3 # 4-week trend threshold for stress detection
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CI_CHANGE_RAPID_DECLINE_THRESHOLD <- -0.5 # Weekly CI change for rapid decline alert
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# Deprecated aliases (for backward compatibility if needed):
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CI_CHANGE_DECLINE_THRESHOLD <- CI_CHANGE_RAPID_DECLINE_THRESHOLD # Weekly CI change threshold for decline alerts
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CI_CHANGE_INCREASE_THRESHOLD <- CI_CHANGE_RAPID_GROWTH_THRESHOLD # Weekly CI change threshold for increase alerts
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# ============================================================================
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# ANGATA-SPECIFIC HELPER FUNCTIONS (Placeholder Section)
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# ============================================================================
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#' Calculate acreage for each field from geometry
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#' @param field_boundaries_sf sf object with field geometries
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#' @param unit Character. Unit preference: "hectare" or "acre" (default from AREA_UNIT_PREFERENCE)
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#' @return data.frame with field and area columns (column name reflects unit: Area_ha or Area_ac)
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calculate_field_acreages <- function(field_boundaries_sf, unit = AREA_UNIT_PREFERENCE) {
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tryCatch({
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# Get field identifier (handles pivot.geojson structure)
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# Use intersect() to find the first matching column, avoiding pull() ambiguity
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field_names <- {
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available_cols <- intersect(
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colnames(field_boundaries_sf),
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c("field", "field_id", "Field_id", "name", "Name")
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)
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if (length(available_cols) > 0) {
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# Extract the first matching column
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field_boundaries_sf %>%
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sf::st_drop_geometry() %>%
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pull(available_cols[1])
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} else {
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# Fall back to sequential indices if no field name column exists
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seq_len(nrow(field_boundaries_sf))
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}
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}
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# Use unified area calculation function
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areas <- calculate_area_from_geometry(field_boundaries_sf, unit = unit)
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# Create output data frame with unit-aware column name
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unit_label <- get_area_unit_label(unit)
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col_name <- paste0("Area_", unit_label)
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result_df <- data.frame(
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field = field_names,
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area = areas,
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stringsAsFactors = FALSE
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)
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colnames(result_df) <- c("field", col_name)
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# Aggregate by field to handle multi-row fields (e.g., sub_fields)
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# Use bare lambda to preserve original column name (not list() which creates suffixes)
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result_df %>%
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group_by(field) %>%
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summarise(across(all_of(col_name), ~ sum(., na.rm = TRUE)), .groups = "drop")
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}, error = function(e) {
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message(paste("Warning: Could not calculate areas from geometries -", e$message))
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unit_label <- get_area_unit_label(unit)
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col_name <- paste0("Area_", unit_label)
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result_df <- data.frame(field = character(0))
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result_df[[col_name]] <- numeric(0)
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return(result_df)
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})
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}
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#' Calculate age in weeks from planting date
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#'
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#' @param planting_date Date of planting
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#' @param reference_date Date to calculate age relative to (typically end_date)
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#' @return Numeric age in weeks (rounded to nearest week)
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calculate_age_week <- function(planting_date, reference_date) {
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if (is.na(planting_date)) {
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return(NA_real_)
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}
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round(as.numeric(difftime(reference_date, planting_date, units = "weeks")), 0)
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}
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#' Categorize regression slope into field uniformity interpretation
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#'
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#' **Input**: Numeric slope from `calculate_regression_slope()` applied to CV values
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#' **Output**: 3-category labels for field uniformity trend
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#'
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#' **Categories**:
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#' - "More uniform" (slope < -0.01): Field becoming more homogeneous
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#' - "Stable uniformity" (-0.01 ≤ slope ≤ 0.01): Field uniformity stable
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#' - "Less uniform" (slope > 0.01): Field becoming more patchy/heterogeneous
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#'
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#' @param slope Numeric slope from `calculate_regression_slope(cv_values)`
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#' @return Character category or NA
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#'
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categorize_cv_trend_long_term <- function(slope) {
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if (is.na(slope)) {
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return(NA_character_)
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} else if (slope < -0.01) {
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return("More uniform")
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} else if (slope > 0.01) {
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return("Less uniform")
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} else {
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return("Stable uniformity")
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}
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}
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#' Determine status alert for CANE_SUPPLY client (harvest/milling workflow)
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#'
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#' Alerts focus on: harvest readiness, crop health monitoring, exception detection
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#' Uses WEEKLY trends (Four_week_trend) not daily noise for consistency
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#' Designed for harvest/milling clients who manage expectation, not daily operations
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#'
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#' Priority order:
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#' 1. harvest_ready → Schedule harvest operations
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#' 2. harvested_bare → Record completion / detect unexpected harvest
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#' 3. stress_detected → Monitor crop health (consistent decline)
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#' 4. germination_delayed → Early warning for young fields
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#' 5. growth_on_track → Positive signal (no action needed)
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#' 6. NA → Normal growth (no alert)
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#'
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#' @param imminent_prob Numeric harvest probability (0-1)
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#' @param age_week Numeric age in weeks since planting/harvest
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#' @param mean_ci Numeric mean Chlorophyll Index
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#' @param four_week_trend Numeric 4-week trend in CI (slope of growth)
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#' @param weekly_ci_change Numeric week-over-week CI change
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#' @param cv Numeric coefficient of variation (field uniformity)
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#' @return Character status alert code or NA
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calculate_status_alert <- function(imminent_prob, age_week, mean_ci,
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four_week_trend, weekly_ci_change, cv) {
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# Priority 1: HARVEST READY - highest business priority
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# Field is mature (≥12 months) AND harvest model predicts imminent harvest
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if (!is.na(imminent_prob) && imminent_prob > 0.3 && !is.na(age_week) && age_week >= 52) {
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return("harvest_ready")
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}
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# Priority 2: HARVESTED/BARE - indicator of completion (or unexpected harvest)
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# Mean CI dropped below vegetative threshold
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if (!is.na(mean_ci) && mean_ci < 1.5) {
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return("harvested_bare")
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}
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# Priority 3: STRESS DETECTED - consistent health decline (weekly trend)
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# Uses Four_week_trend (smooth trend) NOT daily fluctuation to avoid noise
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# Crop declining but not yet bare → opportunity to investigate
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# References: CI_CHANGE_STRESS_TREND_THRESHOLD for 4-week trend detection
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if (!is.na(four_week_trend) && four_week_trend < CI_CHANGE_STRESS_TREND_THRESHOLD &&
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!is.na(mean_ci) && mean_ci > 1.5) {
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return("stress_detected")
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}
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# Priority 4: GERMINATION DELAYED - early warning for young fields
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# Age 4-8 weeks is typical germination window; low CI = slow start
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if (!is.na(age_week) && age_week >= 4 && age_week <= 8 &&
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!is.na(mean_ci) && mean_ci < 1.5) {
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return("germination_delayed")
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}
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# Priority 5: GROWTH ON TRACK - positive operational status
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# Field is healthy, uniform, and growing steadily (no action needed)
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# Conditions: good uniformity (CV < 0.15) AND stable/positive weekly trend
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# References: CI_CHANGE_STABLE_THRESHOLD (±0.2 = stable, no change)
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if (!is.na(cv) && cv < 0.15 &&
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!is.na(four_week_trend) && four_week_trend >= CI_CHANGE_STABLE_THRESHOLD &&
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!is.na(weekly_ci_change) && weekly_ci_change >= CI_CHANGE_STABLE_THRESHOLD) {
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return("growth_on_track")
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}
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# Default: NORMAL GROWTH (no specific alert)
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# Field is growing but may have minor variability; continues normal monitoring
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NA_character_
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}
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#' Build complete per-field KPI dataframe - ALWAYS ALL FIELDS from GeoJSON
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#'
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#' CRITICAL: Always outputs all fields from field_boundaries_sf, regardless of data availability.
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#' Fields without satellite data this week have all KPI columns set to NA.
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#' This is dynamic - output row count equals nrow(field_boundaries_sf), not hardcoded.
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#'
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#' @param current_stats data.frame with current week statistics (may have fewer rows if data is incomplete)
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#' @param planting_dates data.frame with field_id and planting_date columns
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#' @param imminent_prob_data data.frame with Field_id and Imminent_prob_actual columns (or NULL)
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#' @param gap_scores_df data.frame with Field_id and gap_score columns (guaranteed to have all fields)
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#' @param field_boundaries_sf sf object with field geometries (authoritative source of all field IDs)
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#' @param end_date Date object for current report date
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#' @return data.frame with 22 columns and nrow(field_boundaries_sf) rows (all fields, NAs for missing data)
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calculate_all_field_kpis <- function(current_stats,
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planting_dates,
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imminent_prob_data,
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gap_scores_df,
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field_boundaries_sf,
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end_date) {
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message("\nBuilding final field analysis output...")
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# CRITICAL: Start with ALL fields from field_boundaries_sf (dynamic, based on GeoJSON)
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# This ensures output always includes all fields, even if they lack satellite data
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all_fields <- field_boundaries_sf %>%
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sf::st_drop_geometry() %>%
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mutate(Field_id = field) %>%
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select(Field_id) %>%
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distinct()
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# Pre-calculate areas using unified function
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acreage_lookup <- calculate_field_acreages(field_boundaries_sf, unit = AREA_UNIT_PREFERENCE)
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# Determine area column name from result
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unit_label <- get_area_unit_label(AREA_UNIT_PREFERENCE)
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area_col_name <- paste0("Area_", unit_label)
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# Left-join current_stats to all_fields to preserve all field IDs
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# Fields without data in current_stats will have NA values
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field_analysis_df <- all_fields %>%
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left_join(current_stats, by = "Field_id") %>%
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mutate(
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# Column 2: Farm_Section (user fills manually)
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Farm_Section = NA_character_,
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# Column 3: Field_name (from GeoJSON)
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Field_name = Field_id,
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# Column 4: Acreage (from geometry, unit-aware)
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Acreage = {
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acreages_vec <- acreage_lookup[[area_col_name]][match(Field_id, acreage_lookup$field)]
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if_else(is.na(acreages_vec), 0, acreages_vec)
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},
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# Column 8: Last_harvest_or_planting_date (from harvest.xlsx)
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Last_harvest_or_planting_date = {
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planting_dates$planting_date[match(Field_id, planting_dates$field_id)]
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},
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# Column 9: Age_week (calculated)
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Age_week = {
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sapply(seq_len(n()), function(idx) {
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calculate_age_week(Last_harvest_or_planting_date[idx], end_date)
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})
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},
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# Column 10: Phase (based on Age_week)
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Phase = sapply(Age_week, get_phase_by_age),
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# Column 12: Germination_progress (binned Pct_pixels_CI_gte_2)
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Germination_progress = sapply(Pct_pixels_CI_gte_2, bin_percentage),
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# Column 13: Imminent_prob (from script 31 or NA)
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Imminent_prob = {
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if (!is.null(imminent_prob_data)) {
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as.numeric(imminent_prob_data$Imminent_prob_actual[match(Field_id, imminent_prob_data$Field_id)])
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} else {
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rep(NA_real_, n())
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}
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},
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# Column 14: Status_Alert (multi-priority logic for harvest/milling workflow)
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Status_Alert = {
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sapply(seq_len(n()), function(idx) {
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calculate_status_alert(
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imminent_prob = Imminent_prob[idx],
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age_week = Age_week[idx],
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mean_ci = Mean_CI[idx],
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four_week_trend = Four_week_trend[idx],
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weekly_ci_change = Weekly_ci_change[idx],
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cv = CV[idx]
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)
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})
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},
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# Column 19b: CV_Trend_Long_Term_Category (categorical slope)
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CV_Trend_Long_Term_Category = sapply(CV_Trend_Long_Term, categorize_cv_trend_long_term),
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# Column 21: Cloud_pct_clear (binned into intervals)
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Cloud_pct_clear = sapply(Cloud_pct_clear, bin_percentage),
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# Column 22: Gap_score (1σ method); gap_scores_df now has all fields
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Gap_score = {
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if (!is.null(gap_scores_df) && nrow(gap_scores_df) > 0) {
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gap_scores_df$gap_score[match(Field_id, gap_scores_df$Field_id)]
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} else {
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rep(NA_real_, n())
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}
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}
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) %>%
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select(
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all_of(c("Field_id", "Farm_Section", "Field_name", "Acreage", "Status_Alert",
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"Last_harvest_or_planting_date", "Age_week", "Phase",
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"Germination_progress",
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"Mean_CI", "Weekly_ci_change", "Four_week_trend", "CI_range", "CI_Percentiles",
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"CV", "CV_Trend_Short_Term", "CV_Trend_Long_Term", "CV_Trend_Long_Term_Category",
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"Imminent_prob", "Cloud_pct_clear", "Cloud_category", "Gap_score"))
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)
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message(paste("✓ Built final output with", nrow(field_analysis_df), "fields (from GeoJSON) and 22 columns"))
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return(field_analysis_df)
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}
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#' Aggregate per-field data into farm-level KPI summary
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#'
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#' @param field_analysis_df data.frame with per-field KPI data
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#' @param current_week Numeric current week number
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#' @param current_year Numeric current year
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#' @param end_date Date object for current report date
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#' @return List with phase_distribution, status_distribution, cloud_distribution, overall_stats
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calculate_farm_level_kpis <- function(field_analysis_df, current_week, current_year, end_date) {
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cat("\n=== CALCULATING FARM-LEVEL KPI SUMMARY ===\n")
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# Filter to only fields with actual data
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field_data <- field_analysis_df %>%
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filter(!is.na(Mean_CI) & !is.na(Acreage)) %>%
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filter(Acreage > 0)
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if (nrow(field_data) == 0) {
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message("No valid field data for farm-level aggregation")
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return(NULL)
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}
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farm_summary <- list()
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# 1. PHASE DISTRIBUTION
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phase_dist <- field_data %>%
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group_by(Phase) %>%
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summarise(
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num_fields = n(),
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acreage = sum(Acreage, na.rm = TRUE),
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.groups = 'drop'
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) %>%
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rename(Category = Phase)
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farm_summary$phase_distribution <- phase_dist
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# 2. STATUS ALERT DISTRIBUTION
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status_dist <- field_data %>%
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group_by(Status_Alert) %>%
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summarise(
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num_fields = n(),
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acreage = sum(Acreage, na.rm = TRUE),
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.groups = 'drop'
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) %>%
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rename(Category = Status_Alert)
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farm_summary$status_distribution <- status_dist
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# 3. CLOUD COVERAGE DISTRIBUTION
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cloud_dist <- field_data %>%
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group_by(Cloud_category) %>%
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summarise(
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num_fields = n(),
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acreage = sum(Acreage, na.rm = TRUE),
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.groups = 'drop'
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) %>%
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rename(Category = Cloud_category)
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farm_summary$cloud_distribution <- cloud_dist
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# 4. OVERALL STATISTICS
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farm_summary$overall_stats <- data.frame(
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total_fields = nrow(field_data),
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total_acreage = sum(field_data$Acreage, na.rm = TRUE),
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mean_ci = round(mean(field_data$Mean_CI, na.rm = TRUE), 2),
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median_ci = round(median(field_data$Mean_CI, na.rm = TRUE), 2),
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mean_cv = round(mean(field_data$CV, na.rm = TRUE), 4),
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week = current_week,
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year = current_year,
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date = as.character(end_date)
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)
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# Print summaries
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cat("\n--- PHASE DISTRIBUTION ---\n")
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print(phase_dist)
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cat("\n--- STATUS TRIGGER DISTRIBUTION ---\n")
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print(status_dist)
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cat("\n--- CLOUD COVERAGE DISTRIBUTION ---\n")
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print(cloud_dist)
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cat("\n--- OVERALL FARM STATISTICS ---\n")
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print(farm_summary$overall_stats)
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return(farm_summary)
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}
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# ============================================================================
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# ORCHESTRATOR FOR CANE_SUPPLY WORKFLOWS
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# ============================================================================
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#' Main orchestrator for CANE_SUPPLY per-field KPI workflow
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#'
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#' This function coordinates all KPI calculations for the per-field analysis workflow.
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#' It loads historical data, calculates current/previous week statistics, computes
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#' all 22 KPI columns, and aggregates farm-level summaries.
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#'
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#' @param setup List with directory paths (kpi_reports_dir, data_dir, etc.)
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#' @param client_config List with workflow configuration (script_91_compatible, outputs)
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#' @param end_date Date object for current report date
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#' @param project_dir Character project identifier
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#' @param weekly_mosaic Character path to weekly mosaic directory
|
||
#' @param daily_vals_dir Character path to daily values directory
|
||
#' @param field_boundaries_sf sf object with field geometries
|
||
#' @param data_dir Character path to data directory
|
||
#' @return List with field_analysis_df, farm_kpi_results, export_paths
|
||
calculate_field_analysis_cane_supply <- function(setup,
|
||
client_config,
|
||
end_date,
|
||
project_dir,
|
||
weekly_mosaic,
|
||
daily_vals_dir,
|
||
field_boundaries_sf,
|
||
data_dir) {
|
||
|
||
message("\n", strrep("=", 70))
|
||
message("CANE_SUPPLY WORKFLOW: PER-FIELD ANALYSIS (Script 91 compatible)")
|
||
message(strrep("=", 70))
|
||
|
||
reports_dir <- file.path(setup$reports_dir, "kpis")
|
||
|
||
# ========== PHASE 1: WEEKLY ANALYSIS SETUP ==========
|
||
message("\n", strrep("-", 70))
|
||
message("PHASE 1: PER-FIELD WEEKLY ANALYSIS ")
|
||
message(strrep("-", 70))
|
||
|
||
weeks <- calculate_week_numbers(end_date)
|
||
current_week <- weeks$current_week
|
||
current_year <- weeks$current_year
|
||
previous_week <- weeks$previous_week
|
||
previous_year <- weeks$previous_year
|
||
|
||
message(paste("Week:", current_week, "/ Year (ISO 8601):", current_year))
|
||
|
||
# Find per-field weekly mosaics
|
||
message("Finding per-field weekly mosaics...")
|
||
|
||
if (!dir.exists(weekly_mosaic)) {
|
||
stop(paste("ERROR: weekly_mosaic directory not found:", weekly_mosaic,
|
||
"\nScript 40 (mosaic creation) must be run first."))
|
||
}
|
||
|
||
field_dirs <- list.dirs(weekly_mosaic, full.names = FALSE, recursive = FALSE)
|
||
field_dirs <- field_dirs[field_dirs != ""]
|
||
|
||
if (length(field_dirs) == 0) {
|
||
stop(paste("ERROR: No field subdirectories found in:", weekly_mosaic,
|
||
"\nScript 40 must create weekly_mosaic/{FIELD}/ structure."))
|
||
}
|
||
|
||
# Verify we have mosaics for this week
|
||
single_file_pattern <- sprintf("week_%02d_%d\\.tif", current_week, current_year)
|
||
per_field_files <- c()
|
||
for (field in field_dirs) {
|
||
field_mosaic_dir <- file.path(weekly_mosaic, field)
|
||
files <- list.files(field_mosaic_dir, pattern = single_file_pattern, full.names = TRUE)
|
||
if (length(files) > 0) {
|
||
per_field_files <- c(per_field_files, files)
|
||
}
|
||
}
|
||
|
||
if (length(per_field_files) == 0) {
|
||
stop(paste("ERROR: No mosaics found for week", current_week, "year", current_year,
|
||
"\nExpected pattern:", single_file_pattern,
|
||
"\nChecked:", weekly_mosaic))
|
||
}
|
||
|
||
message(paste(" ✓ Found", length(per_field_files), "per-field weekly mosaics"))
|
||
|
||
# ========== PHASE 2: LOAD HISTORICAL DATA ==========
|
||
message("\nLoading historical field data for trend calculations...")
|
||
num_weeks_to_load <- max(WEEKS_FOR_FOUR_WEEK_TREND, WEEKS_FOR_CV_TREND_LONG)
|
||
message(paste(" Attempting to load up to", num_weeks_to_load, "weeks of historical data..."))
|
||
|
||
allow_auto_gen <- !exists("_INSIDE_AUTO_GENERATE", envir = .GlobalEnv)
|
||
|
||
historical_data <- load_historical_field_data(
|
||
project_dir, current_week, current_year, reports_dir,
|
||
num_weeks = num_weeks_to_load,
|
||
auto_generate = allow_auto_gen,
|
||
field_boundaries_sf = field_boundaries_sf,
|
||
daily_vals_dir = daily_vals_dir
|
||
)
|
||
|
||
# ========== PHASE 3: LOAD PLANTING DATES ==========
|
||
message("\nLoading harvest data from harvest.xlsx for planting dates...")
|
||
# Use load_harvest_data() from 80_utils_common.R for consistency with 80_calculate_kpis.R
|
||
harvesting_data <- load_harvest_data(data_dir)
|
||
|
||
planting_dates <- extract_planting_dates(harvesting_data, field_boundaries_sf)
|
||
|
||
if (is.null(planting_dates) || nrow(planting_dates) == 0) {
|
||
message("WARNING: No planting dates available. Using NA for all fields.")
|
||
planting_dates <- data.frame(
|
||
field_id = field_boundaries_sf$field,
|
||
planting_date = rep(as.Date(NA), nrow(field_boundaries_sf)),
|
||
stringsAsFactors = FALSE
|
||
)
|
||
}
|
||
|
||
# ========== PHASE 4: CALCULATE WEEKLY STATISTICS ==========
|
||
message("\nUsing modular RDS-based approach for weekly statistics...")
|
||
|
||
# Current week
|
||
message("\n1. Loading/calculating CURRENT week statistics (week", current_week, ")...")
|
||
current_stats <- load_or_calculate_weekly_stats(
|
||
week_num = current_week,
|
||
year = current_year,
|
||
project_dir = project_dir,
|
||
field_boundaries_sf = field_boundaries_sf,
|
||
mosaic_dir = weekly_mosaic,
|
||
reports_dir = reports_dir,
|
||
report_date = end_date
|
||
)
|
||
message(paste(" ✓ Loaded/calculated stats for", nrow(current_stats), "fields in current week"))
|
||
|
||
# Previous week
|
||
message("\n2. Loading/calculating PREVIOUS week statistics (week", previous_week, ")...")
|
||
prev_report_date <- end_date - 7
|
||
|
||
prev_stats <- load_or_calculate_weekly_stats(
|
||
week_num = previous_week,
|
||
year = previous_year,
|
||
project_dir = project_dir,
|
||
field_boundaries_sf = field_boundaries_sf,
|
||
mosaic_dir = weekly_mosaic,
|
||
reports_dir = reports_dir,
|
||
report_date = prev_report_date
|
||
)
|
||
message(paste(" ✓ Loaded/calculated stats for", nrow(prev_stats), "fields in previous week"))
|
||
|
||
# ========== PHASE 5: CALCULATE TRENDS ==========
|
||
message("\n3. Calculating trend columns...")
|
||
current_stats <- calculate_kpi_trends(
|
||
current_stats, prev_stats,
|
||
project_dir = project_dir,
|
||
reports_dir = reports_dir,
|
||
current_week = current_week,
|
||
year = current_year
|
||
)
|
||
message(paste(" ✓ Added Weekly_ci_change, CV_Trend_Short_Term, Four_week_trend, CV_Trend_Long_Term, nmr_of_weeks_analysed"))
|
||
|
||
# ========== PHASE 6: LOAD HARVEST PROBABILITIES ==========
|
||
message("\n4. Loading harvest probabilities from script 31...")
|
||
# Use get_harvest_output_path() to safely build path (stored in kpi_reports_dir)
|
||
harvest_prob_file <- get_harvest_output_path(project_dir, current_week, current_year)
|
||
message(paste(" Looking for:", harvest_prob_file))
|
||
|
||
imminent_prob_data <- tryCatch({
|
||
if (file.exists(harvest_prob_file)) {
|
||
prob_df <- readr::read_csv(harvest_prob_file, show_col_types = FALSE,
|
||
col_types = readr::cols(.default = readr::col_character()))
|
||
message(paste(" ✓ Loaded harvest probabilities for", nrow(prob_df), "fields"))
|
||
prob_df %>%
|
||
select(field, imminent_prob, detected_prob) %>%
|
||
rename(Field_id = field, Imminent_prob_actual = imminent_prob, Detected_prob = detected_prob)
|
||
} else {
|
||
message(paste(" INFO: Harvest probabilities not available (script 31 not run)"))
|
||
NULL
|
||
}
|
||
}, error = function(e) {
|
||
message(paste(" WARNING: Could not load harvest probabilities:", e$message))
|
||
NULL
|
||
})
|
||
|
||
# ========== PHASE 7: CALCULATE GAP SCORES ==========
|
||
gap_scores_df <- calculate_gap_scores(per_field_files, field_boundaries_sf)
|
||
|
||
# ========== PHASE 8: BUILD FINAL PER-FIELD DATAFRAME ==========
|
||
field_analysis_df <- calculate_all_field_kpis(
|
||
current_stats = current_stats,
|
||
planting_dates = planting_dates,
|
||
imminent_prob_data = imminent_prob_data,
|
||
gap_scores_df = gap_scores_df,
|
||
field_boundaries_sf = field_boundaries_sf,
|
||
end_date = end_date
|
||
)
|
||
|
||
# ========== PHASE 9: EXPORT PER-FIELD RESULTS ==========
|
||
export_paths <- export_field_analysis_excel(
|
||
field_analysis_df,
|
||
NULL,
|
||
project_dir,
|
||
current_week,
|
||
current_year,
|
||
reports_dir
|
||
)
|
||
|
||
# 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
|
||
))
|
||
}
|
||
|
||
# ============================================================================
|
||
# FUTURE EXTENSION POINTS
|
||
# ============================================================================
|
||
|
||
# Placeholder for ANGATA-specific utilities that may be added in future:
|
||
# - Custom yield models based on ANGATA historical data
|
||
# - Field condition thresholds specific to ANGATA growing practices
|
||
# - Integration with ANGATA harvest scheduling system
|
||
# - WhatsApp messaging templates for ANGATA supply chain stakeholders
|
||
# - Cost/benefit analysis for ANGATA operational decisions
|
||
|
||
# These functions can be added here as ANGATA requirements evolve.
|