fairfolio
Saved Projects

Using an Ai-Equipped Drone for Early Detection of Fires

AJAS · 2025

Yujiang Zhu · and Ansh Hiranandani, Massachusetts Academy of Sciences

Overview

With the objective of addressing the need for rapid wildfire detection, this goal is achieved by partnering with the California Department of Forestry and Fire Protection and by pioneering a new approach to early wildfire detection. The drone uses autonomous drone technology to perform sweeps over plots of fire prone land and uses advanced machine learning algorithms to detect early signs of wildfires. Throughout testing, the drone demonstrated a 97% accuracy rate in correctly identifying early signs of wildfires, displaying the drone's potential to beat traditional methods by revolutionizing wildfire management through a much safer, safer, and more effective detection method. Only costing USD $1072, this device sets the new standard in disaster management technology, promising substantial improvements in firefighting efforts as the drone is actively deployed in California.

Competition history

  • AJAS 2025 Category not listed

Related projects

Closest projects by meaning, across every fair and year in the corpus.

Browse more like this

Source: AAAS Annual Meeting (Confex) / American Junior Academy of Science

Save projects to your library

Sign in with Google to keep track of projects you find interesting, organized into folders. An account also raises your daily allowance for “Has this been done?”, and lets you create a key for the MCP server with a much higher limit than anonymous use. Browsing stays public.

Continue with Google