Forestguard Ai: Acoustic Detection Of Chainsaw Activity For Illegal Logging Monitoring

Abstract

Illegal Logging Is A Major Environmental Problem That Affects Forest Ecosystems, Biodiversity, And Natural Resources. Conventional Forest Monitoring Techniques Based Mainly On Manual Patrols And Visual Inspection Can Be Difficult To Scale Across Large And Remote Forest Regions. This Paper Presents ForestGuard AI, A Software-based Acoustic Monitoring Prototype For Detecting Possible Chainsaw Activity From Short Environmental Audio Recordings. The Proposed System Processes Audio By Resampling It To 22,050 Hz, Converting It Into Fixed Four-second Segments, Normalizing The Waveform, And Extracting 13 Mel-Frequency Cepstral Coefficients (MFCCs). A Onedimensional Convolutional Neural Network (1D-CNN) Is Then Used To Classify The Input Into Two Categories: Chainsaw Sound And Normal Forest Sound. A Flask-based Web Application Provides An Interactive Monitoring Interface With Audio Upload, Browser Microphone Recording, Prediction Display, Detection History, Statistical Visualization, Alert Notification, CSV Export, And Daily Report Generation. The Current Implementation Is Designed As A Proofofconcept And Does Not Claim That An Acoustic Prediction Alone Proves Illegal Logging. The Small Prototype Dataset Also Limits The Ability To Make Field-level Generalization Claims. The Proposed Architecture Can Be Extended In Future Work With GPS-enabled Acoustic Sensor Nodes, Edge Computing, Wireless Communication, Solar Power, Geofencing, And Human Verification Workflows.

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