Abstract:
Accurate environmental monitoring is essential for optimizing crop management in
precision agriculture, particularly in sensitive areas such as paddy fields. This thesis
introduces an IoT-based Wild Animal Monitoring System, specifically designed for
paddy fields. The system integrates multiple sensors, including a microphone, DHT11
temperature/humidity sensor, motion sensor, and a buzzer for real-time alerts. These
components provide insights into environmental conditions and potential wildlife
threats to crops. By analyzing sensor data, the system detects wildlife movement,
allowing for timely interventions that protect crops and boost agricultural productivity.
The system’s combination of motion sensors, microphones, and temperature/humidity
sensors offers a comprehensive approach to wildlife detection and management in
paddy fields. This study evaluates the effectiveness of the system in detecting wildlife
presence and enhancing crop protection efforts. The machine learning models employed
for processing the audio signals include Support Vector Machines (SVM), which
achieved 80% accuracy, and K-Nearest Neighbors (KNN), which attained 79%
accuracy. While SVM demonstrated a slight advantage in accuracy, KNN remains an
effective alternative due to its simplicity and efficiency in specific scenarios. Real
world testing validates the system’s practicality, providing continuous monitoring and
real-time alerts to help farmers respond quickly to wildlife threats in paddy fields.
Despite its effectiveness, the system faces challenges, including false positives,
environmental noise interference, and dependence on stable power and communication
infrastructure. Future research will focus on expanding the system’s capabilities to
detect a wider range of wildlife, improving machine learning models, and incorporating
advanced noise-filtering techniques and additional sensors. Integrating renewable
energy sources and developing resilient communication methods will enhance
reliability in diverse agricultural environments. This system represents significant
progress toward sustainable crop management and wildlife conservation in precision
agriculture.
