International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 13 Issue: 02 | Feb 2026
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p-ISSN: 2395-0072
VAN RAKSHAK Anti-Logging and Wild Fire Preventation System For De-Forestation Kedari V. K.1, Talekar Siddhesh Popat2, Shirsikar Aayush Mahendra3, Mule Yash Nandkishor4 1Lecturer, Dept. of Information Technology, Jaihind Comprehensive Educational Institue’s Jaihind Polytechnic
Kuran, Maharashtra, India
2, 3,4Final year Diploma Student, Jaihind Comprehensive Educational Institue’s Jaihind Polytechnic Kuran,
Maharashtra, India ----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The catastrophic effects of deforestation, which
The system employs a mix of machine learning, image processing, sensor networks, and the internet of things to detect illicit logging and possible wildfire dangers. Environmental sensors installed in woodlands record information including relative humidity, temperature, smoke density, noise patterns, and vibration signals produced by chainsaws and other heavy machines. Drones and camera systems record footage and stills of forest areas in real time. In order to standardize sensor values and eliminate noise, data pre-processing techniques are utilized. Important signs like a rapid increase in temperature, a high concentration of smoke, unusually high or low frequencies of sound, or the presence of unauthorized people can be located using feature extraction techniques. Normal forest conditions are distinguished from suspicious activities using classification and anomaly detection techniques. Early detection of anomalous patterns allows for prompt action by forest officials to prevent illicit logging and control wildfires.
include climate change and other natural disasters, are a scourge on humanity. One of the main reasons for deforestation is human avarice, which leads to actions like clearing forests for agriculture and other uses by cutting down trees and allowing them to burn. A great deal of the planet's vegetation has been cut down as a consequence of these terrible activities, which threatens the survival of many species of plants and animals. In order to prevent loggers from cutting down trees, governments and other organizations take many steps, including monitoring loggers while they are in transit and arresting those responsible. Unfortunately, capturing loggers has shown to be an ineffective solution to the problem of deforestation. So, to prevent loggers and criminals from acting, we have implemented machine learning utilizing the Mobile Net neural network deep learning model. The program is designed to detect when loggers are active using a hidden camera in the forest. It then notifies forest officials and provides a location map so that the criminals can be caught in the act.
Systems designed to prevent wildfires and antilogging operations employ linear regression to analyze trends and make predictions about continuous environmental factors. Fire risk, fire spread rate, and forest degradation trends are some of the dependent variables that are modelled in this model. Independent variables include weather conditions (such as temperature, humidity, and wind speed) and seasonal factors. Linear regression is useful for predicting when forests are most at danger and where they are most susceptible by examining environmental and incident data from the past. Identifying patterns of deforestation over time, calculating the likelihood of fires, and bolstering prevention efforts are all areas where it shines. Environmental authorities are able to better allocate resources and devise proactive forest conservation programs with the use of linear regression, which offers obvious insights due to its interpretability and simplicity.
Key Words: Deep Learning, Mobile Net Neural Network, Location Map, Machine Learning, Deforestation.
1. INTRODUCTION With forest cover dwindling at an alarming rate, climate change intensifying, and human involvement with natural ecosystems on the rise, anti-logging and wildfire protection technologies are more crucial than ever. Deforestation creates a variety of problems, including soil erosion, increased carbon emissions, loss of biodiversity, and habitat disruption due to illegal logging and uncontrolled forest fires. The climate, ecological balance, and local inhabitants' lives are all greatly impacted by forests. Manual patrols and delayed reporting, the backbone of traditional forest monitoring approaches, frequently fall short in expansive, isolated, or densely forested regions. Authorities can react swiftly and limit damage with the help of an intelligent anti-logging and wildfire prevention technology that detects illicit activity and fire outbreaks early on. For the sake of environmental preservation, long-term climate and human life protection, and sustainable forest management, such systems are crucial.
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With Mobile Net, even devices with limited resources may perform efficient and accurate image-based detection, making it an essential component of this system. If you're looking for a lightweight convolutional neural network that can run in real-time on edge devices like cameras, drones, and embedded systems in forests, go no farther than Mobile Net. To automatically categorize scenarios as safe or dangerous, Mobile Net is trained on
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