◈ Industrial / IoT
Hydroponic AI Control System
01 / The Problem
Hydroponic farming requires precise, continuous management of nutrient solution composition, pH, electrical conductivity (EC), dissolved oxygen, and environmental parameters. Manual monitoring introduces lag, inconsistency, and human error that directly impacts crop yield and quality.
02 / The Solution
A closed-loop intelligent control system using sensor fusion across 12 parameters per zone, fed into an ML decision model that adjusts dosing pumps, ventilation, lighting schedules, and nutrient flow in real time. A Django-based dashboard provides operator visibility and override capability.
04 / Architecture
Sensor Array (pH, EC, DO, temp, humidity, CO2)
↓
MQTT Broker (edge)
↓
Data Aggregation & Preprocessing
↓
ML Control Model
(trained on 18 months of operational data)
↓
┌────────────────────────────────┐
│ Actuator Control Signals │
│ Dosing pumps / Valves / HVAC │
└────────────────────────────────┘
↓
Event Log → PostgreSQL
↓
Django Dashboard (operator UI)
05 / Results
25% improvement in yield consistency across crop cycles. 18% reduction in nutrient waste. Automated response time reduced from human-minutes to system-seconds.