An adaptive approach to the bee hive state identification using neural networks and audio analysis

Abstract

Problem statement. Monitoring the queen bee is crucial for the health and produc-tivity of a bee colony. The queen plays a vital role in reproduction and maintaining the colony's population. Utilizing neural networks, such as CNNs along with sound analysis, can be a valuable tool for monitoring queen bees and assessing their behavior and health within the hive. Purpose. Finding the best way to preprocess audio data and review it is an important task that, if performed well, will help to track the bee hive population and its health state in general. Related work. With the development of deep learning, several studies were done on their application along with sound analysis in bee hive state identi-fication, such as swarming or bee queen detection. Materials and methods. A public la-beled dataset “To bee or not to bee” with bee hive sounds was used for training. It was split by source and then cut into pieces 4 seconds each. Then, CNN models were trained using different audio feature extraction methods, such as MFCCs and STFT. First, it was trained on a first audio set, and then trained models were utilized to analyze their per-formance on the evaluation set of the second audio set. Results and discussion. According to the training and evaluation results, MFCCs-based models have given constant good results, and when used on a limited audio dataset, pre-trained model showed better per-formance than the one trained from zero. Conclusions. The experiment showed that MFCCs is a better performant feature extraction method for a task of the bee hive sounds analysis and bee queen presence identification. Also, even though training a model on a full audio set results in better performance, pre-trained models can detect a bee queen absence in another hive even after an additional training on a limited audio dataset.