Spectrogram-Based Deep Learning Models for Acoustic Identification of Honey Bees in Complex Environmental Noises

The rapid decline of honey bee populations presents an urgent ecological and agricultural concern, demanding innovative and scalable monitoring solutions. This study proposes a deep learning-based system for non-invasive classification of honey bee buzzing sounds to distinguish bee activity from complex environmental noise—a fundamental challenge for real-world acoustic monitoring. Traditional machine learning models using features like Mel Frequency Cepstral Coefficients (MFCCs) and spectral statistics performed well on curated datasets but failed under natural conditions due to overlapping acoustic signatures and inconsistent recordings. To address this gap, we built a diverse dataset combining public bee audio with recordings from the Honeybee Research Center at the National Agricultural Research Centre (NARC), Pakistan, capturing various devices and natural environments. Audio signals were converted into mel spectrograms and chromograms, enabling pattern learning via pre-trained convolutional neural networks. Among tested architectures—EfficientNetB0, ResNet50, and MobileNetV2—MobileNetV2 achieved the highest generalization, with 95.29% accuracy on spectrograms and over 90% on chromograms under an 80% confidence threshold. Data augmentation improved robustness to noise, while transfer learning enhanced adaptability. This work forms part of a broader project to develop a mobile application for real-time hive health monitoring in natural environments, where distinguishing bee buzzing from other sounds is the crucial first step. Beyond binary classification, the proposed approach offers potential for detecting hive health issues through acoustic patterns, supporting early interventions and contributing to global bee conservation efforts.