Leveraging Machine Learning Approaches to Decode Hive Sounds for Stress Prediction

The motive of this project is to develop an intelligent system that analyzes hive sounds to assess the stress levels of bees using machine learning techniques. Honeybee colonies are highly sensitive to environmental changes, diseases, and human interference, which can affect their behavior and productivity. By decoding hive acoustics, we aim to identify stress patterns early, enabling proactive intervention to maintain colony health. This approach provides a non-invasive, real-time monitoring solution, enhancing the understanding of bee behavior, supporting sustainable apiculture practices, and ultimately contributing to improved pollination efficiency and ecosystem stability.