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backend/database/ERD_Dashboard_Chicken_Counting.md
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# Entity Relationship Diagram (ERD) & Unified Master Architecture
This document defines the integrated **Audrix Smart Poultry Platform ERD (`ERD_audrix`)** and how the **Chicken Counting & Weight Estimation Vision System** connects directly into the master farm database.
---
## 🗺️ Unified Master Mermaid ERD (`ERD_audrix` Integration)
```mermaid
erDiagram
%% ==========================================
%% 1. CORE ENTERPRISE HIERARCHY (ERD_audrix)
%% ==========================================
USER_ACCESS ||--|{ SITES : "manages"
SITES ||--|{ KANDANG : "contains"
KANDANG ||--|{ CYCLES : "runs"
CYCLES ||--|{ FLOCK : "houses"
%% ==========================================
%% 2. OUR DOMAIN: EDGE TOPOLOGY & VISION
%% ==========================================
KANDANG ||--|{ FLOOR : "has_levels"
FLOOR ||--|{ CAMERA : "equipped_with"
FLOOR ||--o{ BATCH_RUNS : "executes"
CAMERA ||--o{ BATCH_RUNS : "records"
BATCH_RUNS ||--o{ DETECTIONS : "captures_tracks"
%% ==========================================
%% 3. VISION ROLLUPS -> MASTER AUDRIX TABLES
%% ==========================================
CYCLES ||--o{ CHICKEN_COUNTING : "records_daily_count"
CYCLES ||--o{ CHICKEN_WEIGHT : "records_daily_weight"
BATCH_RUNS ||--o{ CHICKEN_COUNTING : "aggregates_into_cc"
BATCH_RUNS ||--o{ CHICKEN_WEIGHT : "aggregates_into_cw"
%% ==========================================
%% 4. NON-VISION EXTERNAL STREAMS (DO NOT TOUCH)
%% ==========================================
FLOCK ||--o{ IOT_PANEL : "monitored_by"
CYCLES ||--o{ FEED_SACKS : "tracks_inventory"
CYCLES ||--o{ MANUAL_INPUT : "logs_ground_truth"
CYCLES ||--o{ AI_INSIGHT : "generates_alerts"
%% ==========================================
%% 5. MASTER KPI AGGREGATION (ERD_audrix)
%% ==========================================
CYCLES ||--o{ KPI : "computes_daily_kpi"
CHICKEN_COUNTING ||--o{ KPI : "feeds_headcount"
CHICKEN_WEIGHT ||--o{ KPI : "feeds_vision_weight"
FEED_SACKS ||--o{ KPI : "feeds_feed_usage"
MANUAL_INPUT ||--o{ KPI : "feeds_manual_weights"
%% ==========================================
%% ENTITY DEFINITIONS
%% ==========================================
USER_ACCESS {
int user_id PK "NOT NULL"
varchar user_name "30 NOT NULL"
varchar password_hash "255 NOT NULL"
varchar status "30 NOT NULL"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
}
SITES {
int site_id PK "NOT NULL"
varchar site_name "30 NOT NULL"
int user_id FK "References USER_ACCESS"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
}
KANDANG {
int kandang_id PK "NOT NULL"
varchar kandang_name "30 NOT NULL"
int site_id FK "References SITES"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
}
CYCLES {
int cycle_id PK "NOT NULL (Flock Batch Run)"
int kandang_id FK "References KANDANG"
int total_days "NOT NULL"
date start_date "NOT NULL (Day 0)"
date end_date "NOT NULL"
int doc_in_weight "NOT NULL (Initial DOC weight for this cycle)"
int doc_in_count "NOT NULL (Stocking inventory)"
varchar status "30 NOT NULL"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
}
FLOCK {
int flock_id PK "NOT NULL"
varchar flock_name "30 NOT NULL"
int cycle_id FK "References CYCLES"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
}
FLOOR {
string floor_id PK "e.g. K1-L1, K1-L2"
int kandang_id FK "References KANDANG"
string level "Floor level designation (L1, L2, L3)"
json roi_points "Counting gate coordinates"
float px_per_cm "Spatial calibration constant"
}
CAMERA {
string camera_id PK "e.g. CC1, CC2, CC3, CC4"
string floor_id FK "References FLOOR"
int camera_num "1 to 4"
float px_per_cm "Camera pixel to cm calibration"
string stream_url "Source video / RTSP stream"
}
BATCH_RUNS {
integer id PK "AUTOINCREMENT (SQLite edge runs)"
string date "YYYY-MM-DD"
string location FK "References FLOOR(floor_id)"
string camera_id FK "References CAMERA(camera_id)"
integer total_entered "Headcount count validated"
integer frames_processed
real elapsed_seconds
string stopped_reason
string source_video
string generated_at
integer cycle_day "0 = DOC count only, 1+ = Weight active"
real area_cm2_median "Median body area (cm^2)"
real minor_axis_cm_median "Median width (cm)"
real major_axis_cm_median "Median length (cm)"
real predicted_weight_g "Estimated weight (g)"
real gompertz_baseline_g "Expected curve weight (g)"
real vision_gain_g "Vision allometric gain (g)"
integer n_weight_samples "Valid sample count"
real rejection_rate "MAD outlier filter rejection rate"
}
DETECTIONS {
string date "YYYY-MM-DD"
string camera_id FK
integer track_id "BoT-SORT track identifier"
integer class_id "0 = chicken"
float confidence "YOLO confidence score"
integer frame_index "Count trigger frame"
float bbox_area_cm2 "Area in cm^2"
float width_cm "Width in cm"
float height_cm "Height in cm"
float minor_axis_cm "min(width, height) cm"
float major_axis_cm "max(width, height) cm"
float aspect_ratio_cm "width / height"
float perimeter_cm "Perimeter cm"
float mad_zscore "MAD Z-score outlier metric"
}
CHICKEN_COUNTING {
int cc_id PK "NOT NULL"
int cycle_id FK "References CYCLES"
date date "NOT NULL"
int total_count "NOT NULL (Aggregated total from edge BATCH_RUNS)"
int mortality_count "NOT NULL (Carcasses from vision segmentation)"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
}
CHICKEN_WEIGHT {
int cw_id PK "NOT NULL"
int cycle_id FK "References CYCLES"
date date "NOT NULL"
int age "NOT NULL (Cycle Day: 1+)"
int doc_weight "NOT NULL (Cycle-specific initial DOC weight)"
float average_weight "NOT NULL (predicted_weight_g from edge)"
int chicken_count "NOT NULL (n_weight_samples)"
float uniformity "NOT NULL (Flock weight uniformity %)"
float average_daily_gain "NOT NULL (ADG in grams/day)"
float area_cm2_median "Median body area in cm^2"
float minor_axis_cm_median "Median minor axis / width in cm"
float major_axis_cm_median "Median major axis / length in cm"
float gompertz_baseline_g "Theoretical Gompertz curve baseline"
float vision_gain_g "Allometric vision gain residual"
float rejection_rate "MAD outlier filter rejection percentage"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
}
IOT_PANEL {
int panel_id PK "NOT NULL"
date date "NOT NULL"
float wind_speed "NOT NULL"
float humidity "NOT NULL"
float water_total "NOT NULL"
float average_temperature "NOT NULL"
float experience_temperature "NOT NULL"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
int flock_id FK "References FLOCK"
}
FEED_SACKS {
int fs_id PK "NOT NULL (fc_id)"
date date "NOT NULL"
int in_today "NOT NULL"
int out_today "NOT NULL"
int in_total "NOT NULL"
int out_total "NOT NULL"
int feed_use_today "NOT NULL"
int feed_use_total "NOT NULL"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
int cycle_id FK "References CYCLES"
}
MANUAL_INPUT {
int manual_id PK "NOT NULL"
date date "NOT NULL"
int age_manual "NOT NULL"
int mortality_manual "NOT NULL"
int mortality_manual_total "NOT NULL"
int feed_in_manual "NOT NULL"
int feed_use_manual "NOT NULL"
int feed_in_manual_total "NOT NULL"
int feed_use_manual_total "NOT NULL"
int harverst_manual "NOT NULL"
int harverst_manual_total "NOT NULL"
float harvest_weight_manual "NOT NULL"
float harvest_weight_manual_total "NOT NULL"
float manual_weight "NOT NULL"
float average_harvest_day_manual "NOT NULL"
float average_harvest_weight_manual "NOT NULL"
float fcr_manual "NOT NULL"
float eef_manual "NOT NULL"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
int cycle_id FK "References CYCLES"
}
AI_INSIGHT {
int id PK "NOT NULL"
date date "NOT NULL"
text insight_text "NOT NULL"
string alert "NOT NULL"
varchar section "100 NOT NULL"
varchar session "100 NOT NULL"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
int cycle_id FK "References CYCLES"
}
KPI {
int kpi_id PK "NOT NULL"
date date "NOT NULL"
int age "NOT NULL"
int mortality "NOT NULL"
int mortality_total "NOT NULL"
int feed "NOT NULL"
int feed_total "NOT NULL"
int harverst "NOT NULL"
int harverst_total "NOT NULL"
float harvest_weight "NOT NULL"
float harvest_weight_total "NOT NULL"
int chicken_life "NOT NULL"
float chicken_life_percentage "NOT NULL"
float iot_weight "NOT NULL"
float average_harvest_day "NOT NULL"
float average_harvest_weight "NOT NULL"
float fcr "NOT NULL (Feed Conversion Ratio)"
float eef "NOT NULL (European Efficiency Factor)"
timestamp created_at "NOT NULL"
timestamp updated_at "NOT NULL"
int cycle_id FK "References CYCLES"
int cc_id FK "References CHICKEN_COUNTING"
int cw_id FK "References CHICKEN_WEIGHT"
int fs_id FK "References FEED_SACKS"
int manual_id FK "References MANUAL_INPUT"
}
```
---
## 🏗️ Architectural Tier Descriptions & Integration Mapping
### Tier 1: Farm Hierarchy (`USER_ACCESS` $\to$ `SITES` $\to$ `KANDANG` $\to$ `CYCLES` $\to$ `FLOCK`)
- Belongs to master `ERD_audrix`.
- Defines farm ownership, coops, operational commercial cycles (`CYCLES`), and biological flocks (`FLOCK`).
---
### Tier 2: Edge Vision Subsystem (`FLOOR` $\to$ `CAMERA` $\to$ `BATCH_RUNS` $\to$ `DETECTIONS`)
- **Physical Hierarchy:** Each `KANDANG` has multiple floor levels (`FLOOR`), and each floor operates 4 top-down optical counting cameras (`CAMERA`: CC1, CC2, CC3, CC4) calibrated by `px_per_cm`.
- **Execution & Curation:** Daily batch runs process video feeds on the edge and populate SQLite table `BATCH_RUNS` and Parquet observation datasets (`DETECTIONS`).
- **Cycle Day 0 Rule:** Day 0 is strictly for initial stocking headcount (`total_entered`), while Day 1+ calculates both headcount and vision-derived geometric body weights.
---
### Tier 3: Vision Aggregations to Audrix Tables (`CHICKEN_COUNTING` & `CHICKEN_WEIGHT`)
Our edge pipeline aggregates floor camera runs and pushes clean daily rollups directly into the two vision tables of `ERD_audrix`:
1. **`CHICKEN_COUNTING` (`cc_id`)**:
- `total_count` $\leftarrow \sum \text{total\_entered}$ across all floor cameras for that date.
- `mortality_count` $\leftarrow \text{total dead carcasses detected by mortality segmentation}$.
2. **`CHICKEN_WEIGHT` (`cw_id`)**:
- `average_weight` $\leftarrow \text{fused predicted weight in grams}$.
- `chicken_count` $\leftarrow \sum \text{n\_weight\_samples}$ (valid non-outlier tracks).
- `uniformity` $\leftarrow \text{flock size uniformity percentage}$.
- `average_daily_gain` $\leftarrow \text{daily weight gain in grams/day}$.
- `area_cm2_median`, `minor_axis_cm_median`, `major_axis_cm_median` $\leftarrow \text{calibrated physical dimensions}$.
- `gompertz_baseline_g`, `vision_gain_g` $\leftarrow \text{audited growth model residual breakdown}$.
---
### Tier 4: Master Analytics & KPI Synthesis (`KPI`)
- `KPI` is the central analytics ledger that combines:
- Vision counts from `CHICKEN_COUNTING` (`cc_id`).
- Vision body weights from `CHICKEN_WEIGHT` (`cw_id`).
- Daily feed intake from `FEED_SACKS` (`fs_id`).
- Human ground truth logs from `MANUAL_INPUT` (`manual_id`).
- Computes macro biological & economic efficiency indicators:
$$\text{FCR} = \frac{\text{Total Feed Consumed (kg)}}{\text{Total Harvest / Live Weight (kg)}}$$
$$\text{EEF} = \frac{\text{Livability \%} \times \text{Average Weight (kg)}}{\text{Age (Days)} \times \text{FCR}} \times 100$$