Abstract :

The Health Of Honeybee Colonies Plays A Critical Role In Global Agriculture Due To Their Importance In Pollination And Ecosystem Stability. However, Traditional Methods Of Monitoring Beehive Health Are Labor-intensive, Intrusive, And Require Expert Knowledge. This Project Presents An Intelligent, Non-invasive System For Classifying The Health Status Of Beehives Using Acoustic Signals And Machine Learning Techniques. The Proposed System Captures Audio Signals Generated Within The Hive And Analyzes Them To Determine Whether The Colony Is In A Healthy Or Stressed State.The System Employs Advanced Audio Signal Processing Techniques To Extract Meaningful Features From Hive Sounds. A Total Of 17 Features Are Derived, Including Mel-Frequency Cepstral Coefficients (MFCCs), Spectral Centroid, Spectral Bandwidth, Spectral Rolloff, And Zero-crossing Rate. These Features Capture Both Time-domain And Frequency-domain Characteristics Of The Audio Signals, Providing A Comprehensive Representation Of Hive Activity.A Random Forest Classifier Is Used To Train The Model On Labeled Datasets Of Beehive Sounds. The Classifier Learns Patterns Associated With Different Hive Conditions, Such As Normal Activity, Swarming, Or Stress Due To Environmental Factors Or Disease. Once Trained, The Model Can Accurately Classify New Audio Inputs, Providing Real-time Insights Into Hive Health.The System Is Implemented With A User-friendly Graphical Interface Using Python’s Tkinter Library. Users Can Upload Audio Files, View Classification Results, And Visualize Signal Characteristics Through FFT Plots, MFCC Spectrograms, And Spectral Feature Graphs. These Visualizations Enhance Interpretability And Provide Deeper Insights Into The Acoustic Patterns Of Beehives.Experimental Results Demonstrate That The Proposed System Achieves High Accuracy In Classifying Hive Health Conditions. The Use Of Machine Learning Significantly Reduces Human Intervention And Enables Continuous Monitoring. This Approach Is Scalable And Can Be Integrated With IoT Devices For Real-time Field Deployment.Overall, This Project Contributes To Precision Apiculture By Providing An Efficient, Cost-effective, And Non-invasive Solution For Monitoring Beehive Health. It Has The Potential To Improve Honey Production, Reduce Colony Losses, And Support Sustainable Agricultural Practices

Published:

07-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1571-1581

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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