Ijraset Journal For Research in Applied Science and Engineering Technology
Wildlife Bird Migration Forecasting and Analysis: A Hybrid AI-Embedded Paradigm for Real-Time Ecological Surveillance
Authors: Sumana Chodavarapu, Varun Dhandharia, K. B. Ramesh
DOI Link: https://doi.org/10.22214/ijraset.2025.71783
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Abstract
Migratory birds are crucial health indicators of the environment, but monitoring their patterns has grown more challenging with fast-paced environmental changes. This thesis suggests an intelligent, low-cost system combining embedded electronics and machine learning to provide real-time bird migration analysis. The design revolves around the LPC2148 ARM7 microcontroller with GPS, temperature, and accelerometer sensors integrated into it. Designed on Proteus simulation, the circuit provides energy-aware and scalable field deployment. The natural migration pattern is simulated using Fibonacci and sigmoid-based models to identify biologically plausible behaviour. Sophisticated AI algorithms such as LSTM for sequence forecasting, Random Forest for behaviour classification, and Autoencoders for anomaly detection were used on synthetic and open-source Movebank datasets. Outcomes display high accuracy, minimal energy expenditure, and real-world usability. This research is a promising contribution to smart wildlife telemetry and conservation technology.
Introduction
This work presents a lightweight AI-embedded system for real-time bird migration tracking that detects movement, stress, and migration phases. Using the LPC2148 microcontroller, it integrates GPS, temperature, and accelerometer sensors, communicating data via low-power ZigBee/LoRa protocols. Biologically inspired models—Fibonacci segmentation and sigmoid curve analysis—capture energy-efficient movement and migration phases. Machine learning models (LSTM, Random Forest, Autoencoder) predict GPS positions, classify behaviors, and detect anomalies. Simulations with synthetic and Movebank data showed high accuracy (around 89-93%) and efficient power use, validating the system as an extensible, interpretable tool for wildlife monitoring.
Conclusion
The envisioned system fills the gap between embedded systems and AI to facilitate real-time, understandable bird migration tracking. It features high accuracy, low energy cost, and biological correspondence. Deep learning inference on microcontrollers, solar-powered modules for long-term deployment, and blockchain-secured telemetry are future upgrades. This work provides a solid foundation for scalable conservation tools with cross-disciplinary influence over ecology, AI, and embedded design.
References
[1] Bridge E.S. et al., \"Technology on the Move: Recent Trends in Avian Tracking\", Bioscience, 2011. [2] Kays R. et al., \"Terrestrial animal tracking as an eye on life and planet\", Science, 2015. [3] Hochreiter S., Schmidhuber J., \"Long Short-Term Memory\", Neural Computation, 1997. [4] Movebank.org – Animal Movement Data Repository [5] Breiman L., \"Random Forests\", Machine Learning, 2001 [6] S. Mallapur et al., \"Design of IoT-based Animal Monitoring System\", IJRASET, 2020 [7] R. Sharma et al., \"A Smart Zigbee-Based Wildlife Tracking System\", Procedia CS, 2019 [8] K. B. Ramesh et al., \"LPC2148 Implementation for Remote Monitoring\", IJERA, 2021.
Copyright
Copyright © 2025 Sumana Chodavarapu, Varun Dhandharia, K. B. Ramesh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Paper Id : IJRASET71783
Publish Date : 2025-05-29
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here
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