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Overview

The Aqua-IoT dashboard provides comprehensive data visualization capabilities for analyzing sensor readings over time. By leveraging Django’s ORM and template system, the platform delivers real-time insights into environmental conditions across your monitoring systems.

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Data Retrieval Architecture

All visualization data is retrieved through Django’s ORM, ensuring efficient database queries: 
# Data retrieval pattern used across all views
# Example from sensores/views.py:60
def temperaturaAquario(request):
  temperatura = TemperaturaAquario.objects.all()    
  temperaturas = {'temperaturas': temperatura}  
  return render(request, "temp-aquario.html", temperaturas)
Each view function queries all records for its sensor type using objects.all(), providing complete historical data for visualization.

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Multi-Sensor Dashboard

The main dashboard aggregates data from all six sensor types into a unified visualization interface: 
# Multi-sensor data aggregation from sensores/views.py:13
def home(request):
  if request.user.is_authenticated:
    # Temperatura Plantas
    temperaturap = TemperaturaPlantas.objects.all()  
    # Nivel Agua 
    nivel = NivelAgua.objects.all()   
    # Umidade
    umidade = Umidade.objects.all() 
    # TDS
    tds = Tds.objects.all()  
    # Temperatura Aquario 
    temperaturaa = TemperaturaAquario.objects.all() 
    # LDR
    ldr = Ldr.objects.all()  
    # Dados  
    context = {
              'temperaturas': temperaturap,
              'nivels': nivel,
              'umidades': umidade,
              'tdss': tds,
              'temperaturaas': temperaturaa,
              'ldrs': ldr,
       }
    return render(request, "index.html", context)

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Data Context Structure

The dashboard passes six data collections to the template:

temperaturas

Plant temperature sensor readings (TemperaturaPlantas)

temperaturaas

Aquarium temperature sensor readings (TemperaturaAquario)

umidades

Humidity sensor readings (Umidade)

nivels

Water level sensor readings (NivelAgua)

ldrs

Light sensor readings with luminosity data (Ldr)

tdss

Water quality sensor readings (Tds)

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Sensor-Specific Visualizations

Each sensor type has a dedicated view for detailed data analysis:

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Temperature Visualization (Plants)

# Plant temperature view from sensores/views.py:77
def temperaturaPlantas(request):
  temperatura = TemperaturaPlantas.objects.all()    
  temperaturas = {'temperaturas': temperatura}  
  return render(request, "temp-plantas.html", temperaturas)
Data Fields Available:
  • temperatura: Current temperature reading
  • unidade_medida: Measurement unit (degrees)
  • nome: Sensor name
  • tipo: Sensor type
  • grupo: Sensor grouping
  • data_criacao: Timestamp

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Temperature Visualization (Aquarium)

# Aquarium temperature view from sensores/views.py:59
def temperaturaAquario(request):
  temperatura = TemperaturaAquario.objects.all()    
  temperaturas = {'temperaturas': temperatura}  
  return render(request, "temp-aquario.html", temperaturas)
Use Cases:
  • Track water temperature trends for aquatic health
  • Identify temperature fluctuations that may stress fish
  • Monitor heating/cooling system performance

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Humidity Visualization

# Humidity view from sensores/views.py:65
def umidade(request):
  umidade = Umidade.objects.all()    
  umidades = {'umidades': umidade}  
  return render(request, "umidade.html", umidades)
Data Fields Available:
  • umidade: Humidity percentage reading
  • unidade_medida: Measurement unit (percentage)
  • Common sensor attributes
Analysis Capabilities:
  • Monitor humidity levels for optimal plant growth
  • Detect dry or overly humid conditions
  • Track seasonal humidity variations

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Water Level Visualization

# Water level view from sensores/views.py:71
def nivelAgua(request):
  nivel = NivelAgua.objects.all()    
  nivels = {'nivels': nivel}  
  return render(request, "nivel.html", nivels)
Data Fields Available:
  • nivel: Current water level in centimeters
  • nivel_minimo: Minimum threshold level (200cm default)
  • unidade_medida: Measurement unit (centimeters)
Advanced Features:
  • Compare current level against minimum threshold
  • Visualize depletion rates over time
  • Alert on low water conditions
Water level readings below nivel_minimo should trigger immediate attention to prevent system failures.

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Light Intensity Visualization

# Light sensor view from sensores/views.py:83
def ldr(request):
  ldr = Ldr.objects.all()    
  ldrs = {'ldrs': ldr}  
  return render(request, "ldr.html", ldrs)
Data Fields Available:
  • luminosidade: Current light intensity in lumens
  • media_luminosidade: Average light intensity (30 lumens default)
  • unidade_medida: Measurement unit (lumens)
Visualization Insights:
  • Track daily light cycles and photoperiods
  • Compare readings against average baseline
  • Optimize artificial lighting schedules

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Water Quality Visualization (TDS)

# TDS sensor view from sensores/views.py:89
def tds(request):
  tds = Tds.objects.all()    
  tdss = {'tdss': tds}  
  return render(request, "tds.html", tdss)
Data Fields Available:
  • tds: Current TDS reading in ppm
  • media_tds: Average TDS reading (30 ppm default)
  • unidade_medida: Measurement unit (ppm)
Analysis Applications:
  • Monitor nutrient solution concentration
  • Track water quality degradation over time
  • Compare against optimal TDS ranges for specific crops

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Data Model Structure

All sensor data inherits from the base Sensor model: 
# Base sensor model from sensores/models.py:5
class Sensor(models.Model):
  nome = models.CharField(max_length=50)
  tipo = models.CharField(max_length=50)
  grupo = models.CharField(max_length=50)  
  data_criacao = models.DateTimeField(auto_now_add=True)
  class Meta:
    abstract = True

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Timestamp-Based Analysis

Every sensor reading includes a data_criacao timestamp, enabling:

Time-Series Analysis

Plot sensor readings over time to identify trends and patterns

Historical Comparison

Compare current readings with historical data

Rate of Change

Calculate how quickly conditions are changing

Event Correlation

Correlate sensor events across different monitoring points

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Visualization Workflow

1

Access Dashboard

Navigate to the main dashboard (/) to see all sensor data at a glance
2

Select Sensor Type

Choose a specific sensor category for detailed visualization
3

Analyze Data

Review current readings, compare with averages/thresholds, and identify trends
4

Cross-Reference Sensors

Return to main dashboard to compare readings across different sensor types
5

Take Action

Use insights to adjust environmental controls or sensor configurations

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Advanced Analysis Features

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Threshold Monitoring

Sensors with threshold fields enable automated monitoring:
The nivel_minimo field in NivelAgua allows visualization of whether current levels are above or below critical thresholds. Default minimum is 200cm.
The media_luminosidade field in Ldr sensors provides a baseline for comparing current readings. Default average is 30 lumens.
The media_tds field in TDS sensors helps visualize whether water quality is within acceptable parameters. Default average is 30 ppm.

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Sensor Grouping

The grupo field enables organized visualization by location or system:
  • Group sensors by physical location (“greenhouse-1”, “tank-a”)
  • Organize by crop type or growing zone
  • Separate production and experimental areas

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Data Export Capabilities

All sensor data is accessible through both web views and REST API: 
# API router configuration from sensores/urls.py:5
router = routers.DefaultRouter()
router.register('temperatura-aquario', views.TemperaturaAquarioViewset, basename='temperatura-aquario')
router.register('umidade', views.UmidadeViewset, basename='umidade')
router.register('nivel', views.NivelAguaViewset, basename='nivel')
router.register('temperatura-plantas', views.TemperaturaPlantasViewset, basename='temperatura-plantas')
router.register('ldr', views.LdrViewset, basename='ldr')
router.register('tds', views.TdsViewset, basename='tds')
Access sensor data programmatically through the REST API at /api/ endpoints for custom visualization tools or data export.

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Visualization Best Practices

Regular Review

Check visualizations daily to catch anomalies early

Trend Analysis

Focus on trends over time rather than individual readings

Cross-Sensor Correlation

Compare multiple sensor types to understand system interactions

Baseline Establishment

Use average fields to establish normal operating ranges

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Next Steps

API Reference

Learn how to access sensor data programmatically for custom visualizations

Dashboard Overview

Return to dashboard overview for comprehensive feature guide

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