LSTM-H: A Hybrid Deep Learning Model for Accurate Livestock Movement Prediction in UAV-Based Monitoring Systems - Ayub Bokani; Elaheh Yadegaridehkordi; Salil S. Kanhere (Drones)
Accurately predicting livestock movement is a cornerstone of precision agriculture, enabling smarter resource management, improved animal welfare, and enhanced productivity. However, the unpredictable and dynamic nature of livestock behavior poses significant challenges for traditional mobility prediction models. This study introduces LSTM-H, a hybrid deep learning model that combines the sequential learning power of Long Short-Term Memory (LSTM) networks with the real-time correction capabilities of Kalman Filters (KFs) to enhance livestock movement prediction within UAV-based monitoring frameworks. The results demonstrate that LSTM-H achieves a mean error of just 11.51 m for the first step and 40.68 m over a 30-step prediction horizon, outperforming state-of-the-art models by 4.3–14.8 times. Furthermore, LSTM-H exhibits robustness across noisy and dynamic conditions, with a 90% probability of errors below 13 m, as shown through cumulative error analysis. This enhanced accuracy enables UAVs to optimize flight trajectories, reducing energy consumption and improving monitoring efficiency in real-world agricultural settings. By bridging deep learning and adaptive filtering, LSTM-H not only enhances prediction accuracy but also paves the way for scalable, real-time livestock and UAV monitoring systems with transformative potential for precision agriculture.