UPDATE

dashboard_data/cards/sensor_radar_chart.py

@@ -41,16 +41,6 @@ def _normalize_to_ideal_score(value: float | None, minimum: float, ideal: float,
 
 
 def _resolve_profile(context: dict[str, Any]) -> tuple[dict[str, dict[str, float]], str]:
-    location = context.get("location")
-    if location is not None:
-        location_profile = getattr(location, "ideal_sensor_profile", None) or {}
-        if location_profile:
-            merged = {
-                metric: {**DEFAULT_IDEAL_SENSOR_PROFILE.get(metric, {}), **location_profile.get(metric, {})}
-                for metric in set(DEFAULT_IDEAL_SENSOR_PROFILE) | set(location_profile)
-            }
-            return merged, "location"
-
     plants = context.get("plants", [])
     for plant in plants:
         plant_profile = getattr(plant, "health_profile", None) or {}

dashboard_data/cards/soil_moisture_heatmap.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from math import sqrt
 from typing import Any
 
-from sensor_data.models import SensorData, SensorDataHistory
+from farm_data.models import SensorData
 
 
 QUALITY_REAL = "REAL"
@@ -61,9 +61,9 @@ def _latest_sensor_measurement(sensor: Any, histories: list[Any]) -> dict[str, A
     series = _sensor_time_series(sensor, histories)
     latest = series[-1] if series else {"timestamp": None, "value": None, "quality_flag": QUALITY_MISSING}
     return {
-        "sensor_id": str(sensor.uuid_sensor),
-        "latitude": float(sensor.location.latitude),
-        "longitude": float(sensor.location.longitude),
+        "sensor_id": str(sensor.farm_uuid),
+        "latitude": float(sensor.center_location.latitude),
+        "longitude": float(sensor.center_location.longitude),
         "depth": None,
         "timestamp": latest["timestamp"],
         "soil_moisture_value": latest["value"],
@@ -99,7 +99,7 @@ def _grid_axis(min_value: float, max_value: float) -> list[float]:
 
 def _load_sensor_network(current_sensor: Any) -> list[Any]:
     plant_ids = list(current_sensor.plants.values_list("id", flat=True))
-    queryset = SensorData.objects.select_related("location").prefetch_related("plants")
+    queryset = SensorData.objects.select_related("center_location").prefetch_related("plants")
     if plant_ids:
         queryset = queryset.filter(plants__id__in=plant_ids).distinct()
     return list(queryset)
@@ -118,14 +118,8 @@ def build_soil_moisture_heatmap(sensor_id: str, context: dict | None = None, ai_
         }
 
     sensors = _load_sensor_network(current_sensor)
-    sensor_ids = [sensor.uuid_sensor for sensor in sensors]
-    history_rows = SensorDataHistory.objects.filter(uuid_sensor__in=sensor_ids).order_by("-recorded_at")[:200]
-    history_map: dict[Any, list[Any]] = {}
-    for row in history_rows:
-        history_map.setdefault(row.uuid_sensor, []).append(row)
-
     sensor_points = [
-        _latest_sensor_measurement(sensor, history_map.get(sensor.uuid_sensor, []))
+        _latest_sensor_measurement(sensor, [])
         for sensor in sensors
     ]
     valid_sensor_points = [point for point in sensor_points if point["soil_moisture_value"] is not None]