The "Leveraging Machine Learning Approaches to Decode Hive Sounds for Stress Prediction" project explores the potential of machine learning in analyzing beehive sounds for predicting stress levels within hives. Using an audio dataset containing recordings from hives with and without a queen, the project employs a variety of algorithms to decode sound patterns associated with stress. The dataset is processed using state-of-the-art techniques like Hubert, CNN combined with MFCC, and Chrome Features integrated with Random Forest. The goal is to identify stress-indicating patterns in hive sounds, offering valuable insights into the health of the colony. This research aims to develop an effective system for stress prediction using sound features, providing a non-invasive method for monitoring hive conditions. The system is built with a user-friendly interface, consisting of modules such as registration, login, and classification, and is powered by a Flask backend. The outcomes of this project could significantly enhance our ability to understand and mitigate stress in hives, which has a direct impact on the well-being of bee populations.

Keywords: Machine Learning, Hive Sounds, Stress Prediction, Audio Analysis, Bee Colony, Feature Extraction, Hubert, CNN, MFCC, Random Forest.