his project implements a Forest Fire Detection and Alert System using sensor data (temperature, humidity, and smoke levels) to monitor forest fire risks.

Real-time forest fire detection using satellite data (MODIS, VIIRS) and CNN-based classification.

online early forest fire detection system based on drone platform.

The current forest surveillance systems methods consume a lot of resources and are less efficient, not reliable and require a constant human presence whose tasks can be easily automated using new technology. To solve these problems we propose an autonomous surveillance system which uses object detection to identify specified animals. It is capable of monitoring forest fires, intruders, wildlife etc, all at once and alerts the concerned officials immediately and precisely. It has a hybrid object detection system using HAAR and Backpropagation neural network algorithms which can be used to train and detect animals and predict from the data obtained respectively. This helps in detecting various unwanted visitors, dangerous animals, or restricted tools into the forest. The system can not only store the video feed but can also determine population , track a specific animal or human and sends the pictures to your email directly along with real-time video monitoring via the internet which allows the users to monitor from anywhere in the world and sends instant alerts to your phone via an SMS even in remote areas in case of emergencies, and it stores all the data in a repository. We can control the system using a windows app which allows us to select which animals to be detected by the camera modules and their alert levels along with other settings and also provides a detailed analysis on various things like forest fires, animal population, trespassed areas etc, to users in simple charts. It is a smart, automatic, modular system which is cheap and easily expandable.

Fire Eye is a robust and fixed lookout system capable of real-time forest fire and smoke detection. We have use Flask Python web framework to develop the user interface of our application.

PyroScan AI is a GenAI-powered multi-agent system for real-time forest fire prediction across 10 global zones. Designed for researchers and disaster teams, it includes agents for fire detection, weather analysis, and historical data mining. Deployable via CLI or Docker.

This project utilizes 🔬 machine learning algorithms to predict 🔥 forest fires using inputs such as 🌡️ temperature, 🧪 oxygen level, and 💧 humidity. The trained model can analyze the inputs and provide a prediction of the likelihood of a forest fire occurring. The prediction results are displayed through a 🌐 Flask web application.

Forest Fire Detection Research involves the development of a computer vision-based system designed to detect forest fires. It leverages object detection techniques to identify visual signs of fire and smoke in images, aiming to support early warning systems and wildfire prevention efforts.

Leverage Convolutional Neural Networks to develop an efficient system for early forest fire detection. This technology helps protect natural resources and prevent wildfires.

This is a hardware and software system for real time monitoring and detection of forest fires. With its help remote recognition of wood fires is possible as well as high-accuracy positioning of flame base. Hardware part of the system consists of a set of intellectual sensors which are installed inside the forese. The action range of sensors is 250 - 500m depending on the RF device and type of sensor. As for the sensors, video cameras, infrared imagers and other intellectual equipment are used. They detect fire areas by a number of measures and under different conditions. If the sensor detects a fire, the information is transmitted to control unit via various communication channels: optical, radio, wire, GSM, etc. In such a way forest data are transmitted to the software part of our project where they are processed and analyzed. The system will automatically find and identify the fire area. Thereafter the information is passed to special departments via built-in alerting service, Internet and even mobile networks. Characteristics of the System • Fire detection accuracy - up to 250 m • Direction detection accuracy -- 0.5° • Possibility to integration data from other information sources (weather and satellite information). • Possibility of efficient scaling and broadening of the system for coverage range extension. • Number of users - without limit. • Possibility to get information on mobile phones. • Automatic detection of potentially dangerous objects (smoke and flame). Advantages of System 1. Automatization of monitoring 2. Centrally managed monitoring of large areas 3. Opportunity detect fires at an early stage and its spread 4. High accuracy of fire detection 5. Decrease of human factor role when detecting fires 6. Low cost of installation and exploitation of the system in comparison with other forms of monitoring 7. Flexibility of the system depending on relief and customer wants

Early detecting forest fires using IoT and deep learning algorithms.

This is a deep learning project that detects Fire or Non_Fire from images using a ResNet50 model. The app is built with Streamlit and deployed on Streamlit Cloud.

The purpose of this architecture is to simulate a forest fire detection system. The architecture consists of several blocks. A microcontroller connected to a flame sensor that notifies a fire, a UAV that automatically receives the notification and starts its mission to extinguish the fire and a ground system for constant monitoring of the forest zone and the status of the drone.

Iot Based Forest Fire Detection System

The Fire Detection and Monitoring System is a Django-based web application that integrates fire detection capabilities with weather monitoring to provide real-time updates and alerts. It utilizes a pre-trained cascade classifier for fire detection, weather APIs for local weather data, and email notifications for critical alerts.

A Deep Learning approach to detect forest fires from images or videos.

Forest Fire Detection using InceptionNet: GitHub repo for an accurate ML-based system. Collect diverse fire/non-fire images, preprocess data, train InceptionNet model, evaluate with metrics, and deploy for real-time monitoring. Mitigate wildfires with early detection. Open to collaboration.

This is a smart forest fire detection and alerting system using Arduino UNO R3, equipped with GSM and GPS modules, a DHT sensor, and more. The system alerts the forest and fire departments when it detects abnormal temperature/humidity or fire events.

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The main aim of this project is to monitor atmospheric conditions in the Forest ,alerts when the parameters hits the threshold values and if we send a message to it then it sends back the current parameters levels. it is an SMS based system. I used Arduino-NANO, SIM900a, GPS, Flame sensor, Smoke sensor-MQ2 and temparature sensor

Distribuited systems and algorithms for fire detection in forests using drones

IoT system for forest fire risk analysis, early detection, and spread prediction

The automated detection of forest fires is important for protecting natural ecosystems. Accurately identifying flames in forest pictures is fundamental for creating advanced fire surveillance and early alert systems. This project presents a pipeline of a U-Net model trained on the Flame dataset.

Wildfires is a natural disaster causing irreparable damage to local ecosystem. Computer vision and deep learning in order to solve the problem detecting forest fires. Deep Learning for Forest Fire Detection. By using UAV & Information Processing System, we can process and analysis images in real time. Fine-Tuning Technique. Early Detection and Early Rescue.

A deep learning-based forest fire detection system using CNNs and computer vision techniques for real-time fire classification and early warning.

This project utilizes 🔬 machine learning algorithms to predict 🔥 forest fires using inputs such as 🌡️ temperature, 🧪 oxygen level, and 💧 humidity. The trained model can analyze the inputs and provide a prediction of the likelihood of a forest fire occurring. The prediction results are displayed through a 🌐 Flask web application.

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A safety-critical IoT and machine learning system that predicts and detects forest fire risks at an early stage through intelligent environmental monitoring.

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FireGuardAI is an advanced AI-powered system designed to detect forest fires at an early stage using machine learning and deep learning techniques. By analyzing satellite imagery, sensor data, and environmental conditions such as temperature, humidity, and wind speed, the model accurately identifies potential fire outbreaks in real-time.