Hackathon Project Blueprint — AI & IoT Training Camp

1 — Project Theme & Problem Statement

Describe the real-world problem your project solves. Be specific — e.g., "Monitoring soil moisture in a school garden and alerting the gardener when irrigation is needed."

2 — Mandatory Requirements Checklist

Your project MUST include all three of the following. Check each box and specify details:

At least 1 AI Model (Image Classifier / Audio Classifier / Pose Classifier)
Model type: __________________ Classes: __________________
At least 1 Physical Sensor (DHT11, Soil Moisture, MQ-2, Flame, LDR, etc.)
Sensor(s): __________________ Pin(s): __________________
At least 1 Actuator (Servo Motor, Buzzer, Relay/Pump, LED)
Actuator(s): __________________ Pin(s): __________________

3 — System Architecture Diagram

Draw your Input → Processing → Output pipeline below. Label each component with specific hardware and pin numbers.

Input (Sensor / Camera)

→

Processing (AI + Logic)

→

Output (Actuator / Display)

Sketch the full wiring diagram here (optional — use back of page if needed):

Draw your wiring diagram here
(sensor positions, pin connections, power supply)

4 — Hardware Inventory

List every hardware component your project needs. Verify availability with the instructor.

Component	Quantity	Pin / Connection	Available?

5 — AI Model Training Plan

Model Type	Class Names	Samples / Class	Data Collection Method

Minimum samples: Image ≥ 150/class · Audio ≥ 40/class · Pose ≥ 100/class. More = better accuracy.

6 — Team Role Assignment

Role	Name(s)	Responsibilities
Team Leader		Overall coordination, timeline tracking, presentation delivery
AI / Software		Data collection, model training, PictoBlox block coding
Hardware		Circuit wiring, sensor calibration, physical prototype build
Tester / QA		End-to-end testing, edge case debugging, documentation
Presenter		Pitch preparation, demo narration, Q&A handling

7 — Day 5 Sprint Plan

Sprint	Time	Deliverable
Sprint 1	Session 1 (90 min)	Training data collected + All hardware wired & tested
Sprint 2	Session 2 (90 min)	AI model trained (>85% accuracy) + Physical prototype built
Sprint 3	Session 3 (90 min)	Full integration working (sensor → AI → actuator) MVP done
Sprint 4	Session 4 (90 min)	Edge cases handled + Pitch rehearsed (2× minimum)

8 — Decision Logic (If-Else Pseudocode)

Write your main decision logic in plain language:

FOREVER:
  Read sensor → _______________
  Analyze image/audio/pose from webcam
  IF _______________  THEN
    _______________
  ELSE IF _______________  THEN
    _______________
  ELSE
    _______________

9 — Instructor Approval

Instructor Feedback / Adjustments

Approved By / Signature

Date: _______________

Plan Locked: After instructor approval, no changes are allowed to the project scope. Hardware and AI model type are finalized. Only implementation details may be adjusted during Day 5 sprints.