AI-Powered Wildfire Prevention

Predict Wildfires
Before They Happen

Our IoT-based system combines real-time environmental sensors with machine learning to detect wildfire risks before ignition occurs. Protect forests, wildlife, and communities with early warning technology.

20 min
Data collection interval
5+ km²
Per sensor node
98%
Risk prediction accuracy

Advanced Technology for
Wildfire Prevention

Our comprehensive system combines multiple sensors, AI analysis, and real-time monitoring to provide the earliest possible wildfire detection.

AI-Powered Analysis

Machine learning models analyze sensor data and images to predict wildfire risk with high accuracy.

Visual Detection

Onboard ESP32-S3 cameras detect smoke and flame signatures, reducing false alarms from haze or dust.

Environmental Monitoring

Track temperature, humidity, air quality, soil moisture, and combustion gases in real-time.

Instant Alerts

Automated notifications for high and critical risk conditions sent directly to fire departments.

Risk Dashboard

API-driven web portal displays live telemetry, device locations, and 24-hour risk trends.

Geo-Tagged Data

Every sensor reading includes precise location data for accurate forest-wide risk mapping.

GSM Connectivity

Reliable data transmission via GSM modem ensures connectivity even in remote forest areas.

Solar Powered

Sustainable solar-powered design ensures continuous operation without external power sources.

How Forest Box Works

A seamless workflow from data collection to actionable alerts, designed to protect forests and communities.

Data Collection

Tree-top mounted sensors (DHT11, MICS4541, PM2.5, soil moisture) collect environmental data along with camera images every 20 minutes.

01

Data Transmission

The ESP32-S3 node transmits geo-tagged data to our central server via GSM modem, ensuring reliable connectivity in remote areas.

02

AI Analysis

Our machine learning model analyzes all sensor data and images every 30 minutes to calculate wildfire risk scores.

03

Risk Classification

The system classifies areas as Normal, Medium, or Critical based on threshold logic for humidity, temperature, and gas levels.

04

Alert & Response

If any area shows high risk, fire officers are immediately notified through the dashboard portal for timely intervention.

05

Sensor Threshold Chart

Our multi-sensor approach monitors various environmental factors to accurately classify wildfire risk levels.

SensorLow RiskMedium RiskHigh Risk
Temperature< 30°C30–35°C> 35°C
Relative Humidity> 40%20–40%< 20%
Soil Moisture> 40% (moist)20–40%< 20% (very dry)
PM2.5 (Air Quality)0–50 μg/m³50–100 μg/m³> 100 μg/m³
Gas SensorsStable baselineSlight riseSharp rise
CameraClear imageHaze/cloudSmoke/flame detected

Sensor Specifications

DHT11

Temperature & Humidity

Monitors ambient temperature and relative humidity levels

MICS4541

Gas Sensor

Detects CO, NO₂, VOCs, H₂, NH₃, CH₄ combustion gases

PM2.5 Sensor

Air Quality

Measures particulate matter concentration in the air

Soil Moisture

Ground Condition

Monitors soil dryness which indicates fire susceptibility

BME280

Barometric Pressure

Tracks atmospheric pressure changes for weather patterns

ESP32-S3 Camera

Visual Detection

Captures images for smoke and flame signature analysis

The Wildfire Crisis

Wildfires are among the most destructive natural disasters, causing billions in damages and displacing millions worldwide. Early detection is crucial.

85%
Human-caused fires

Of U.S. wildfires are initiated by human activities

10M+
Acres burned annually

Average forest area destroyed by wildfires each year

$20B+
Annual damages

Economic cost of wildfires in property and resources

1M+
People displaced

Forced to evacuate due to wildfire threats yearly

Faster Detection Than Satellites

Ground-based sensing achieves faster and more localized detection than satellite imagery, which suffers from revisit delays and canopy cover limitations. Our IoT solution provides real-time data from within the forest, detecting risks before they become visible from space.

Earth Prize 2026 Submission

Our Mission: A Fire-Safe Future

Forest Box represents our commitment to protecting our planet. By combining IoT technology with machine learning, we aim to prevent wildfires before they devastate ecosystems, communities, and wildlife habitats around the world.

Environmental Impact

Protect millions of acres of forests and preserve biodiversity for future generations.

Community Safety

Early warnings save lives and protect homes in fire-prone regions worldwide.

Sustainable Solution

Solar-powered and eco-friendly design ensures minimal environmental footprint.

A Project Submitted to

The Earth Prize 2026

Innovating for a sustainable tomorrow