Abstract
Honeybees play a crucial role in the agriculture industry because they pollinate approximately 75% of all flowering crops. However, every year, the number of honeybees continues to decrease. Consequently, numerous researchers in various fields have persistently attempted to solve this problem. Acoustic scene classification, using sounds recorded from beehives, is an approach that can be applied to detect changes inside beehives. This method can be used to determine intervals that threaten a beehive. Currently, studies on sound analysis, using deep learning algorithms integrated with various data preprocessing methods that extract features from sound signals, continue to be conducted. However, there is little insight into how deep learning algorithms recognize audio scenes, as demonstrated by studies on image recognition. Therefore, in this study, we used a mel spectrogram, mel-frequency cepstral coefficients (MFCCs), and a constant-Q transform to compare the performance of conventional machine learning models to that of convolutional neural network (CNN) models. We used the support vector machine, random forest, extreme gradient boosting, shallow CNN, and VGG-13 models. Using gradient-weighted class activation mapping (Grad-CAM), we conducted an analysis to determine how the best-performing CNN model recognized audio scenes. The results showed that the VGG-13 model, using MFCCs as input data, demonstrated the best accuracy (91.93%). Additionally, based on the precision, recall, and F1-score for each class, we established that sounds other than those from bees were effectively recognized. Further, we conducted an analysis to determine the MFCCs that are important for classification through the visualizations obtained by applying Grad-CAM to the VGG-13 model. We believe that our findings can be used to develop a monitoring system that can consistently detect abnormal conditions in beehives early by classifying the sounds inside beehives.
Highlights
Honeybees play a vital role in pollinating most food crops, which are essential for sustaining human life
We conducted several experiments by applying features extracted from sound to the support vector machine (SVM), random forest, XGBoost, shallow convolutional neural network (CNN), and VGG13 models
Discussion is study aimed to verify the performance of a beehive sound classification model using various machine learning algorithms, by implementing a mel spectrogram, mel-frequency cepstral coefficients (MFCCs), Model SVM Random Forest XGBoost Shallow CNN VGG-13
Summary
Honeybees play a vital role in pollinating most food crops, which are essential for sustaining human life. Only a few reported studies used CNN models by applying feature extraction methods [16, 17]. In the case of the three machine learning algorithms (SVM, random forest, and XGBoost), the mean and standard deviation for each time section were obtained by extracting statistics from the preprocessed data and using them as input data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.