IoT·shipped

Smart Irrigation IoT

Built a soil/climate irrigation setup: NodeMCU sensors publish to Firebase, a Flutter dashboard visualizes the stream, and an SVM model drives pump actuation with hard edge cutoffs if the cloud path stalls.

Date2024-08

Reading TimeN/A

Statusshipped

StackNodeMCU, Flutter, Dart+2

NodeMCU multi-sensor publish

Edge node

[High Confidence]

SVM on moisture / temp / humidity

Decision model

[High Confidence]

What it does

Timer-based irrigation ignores humidity and temperature. This project streams soil moisture, temperature, and humidity from a NodeMCU into Firebase Realtime Database, shows the feed in a Flutter app, and uses a small SVM to decide when to actuate a pump — with firmware hard cutoffs if cloud commands stop arriving.

Constraints

Tiny edge RAM/CPU
Unreliable Wi-Fi in the field
Actuation still needs a local fail-safe
Operators want live graphs, not only relay state

Decisions & tradeoffs

Firebase as fan-out bus — many consumers without taxing the MCU
SVM on three sensor features — cheap inference, easy retrain vs a neural net
Client caching for dashboard continuity during brief drops
Firmware emergency cutoffs independent of the cloud path

flowchart LR
  Sensors[Soil Moisture / Temp / Humidity] --> EdgeNode[NodeMCU]
  EdgeNode -->|Telemetry| CloudBus[(Firebase Realtime Database)]
  CloudBus --> PredictiveModel[SVM Decision]
  CloudBus --> Dashboard[Flutter Ops App]
  PredictiveModel -->|Relay Command| Actuator[Water Actuator]
  EdgeNode -->|Hard Cutoff Guard| Actuator

Tradeoffs: a cloud hop adds latency vs peer sockets and unlocks multi-consumer analytics. SVM fits low-dimensional sensor vectors; it will not magically generalize to unrelated climates without retraining.

What it demonstrates

End-to-end IoT path from firmware to mobile UI
Keeping safety on the edge when cloud control is optional
Matching model complexity to feature dimensionality