Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives
Honeybees play a vital role in preservation of an healthy environment. Bees not only provide pollination services but also produce honey, beeswax, and royal jelly. Beekeeping has a rich history and substantial economic potential worldwide, but swarming remains a crucial challenge for maintaining pro...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10701518/ |
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| author | Kainat Iqbal Bayan Alabdullah Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park |
| author_facet | Kainat Iqbal Bayan Alabdullah Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park |
| author_sort | Kainat Iqbal |
| collection | DOAJ |
| description | Honeybees play a vital role in preservation of an healthy environment. Bees not only provide pollination services but also produce honey, beeswax, and royal jelly. Beekeeping has a rich history and substantial economic potential worldwide, but swarming remains a crucial challenge for maintaining profitability. Swarming, a typical colony reproductive process in honeybees, significantly impacts beekeepers profitability by lowering the number of bees in hives and thus effecting honey production. Monitoring of these beehives is therefore of paramount importance to keep an eye on their irregular behavior. Swarm prediction can be done by visually inspecting hives, monitoring temperature, or analyzing acoustic features with machine learning. Acoustic monitoring is instrumental in detecting changes in colony behavior since it overcomes the constraints of visual inspections and is not affected by external factors like temperature. In this paper, we aim to evaluate various state-of-the-art machine learning and deep learning models for swarm prediction by studying wave plot features, Mel Spectrogram, and Melfrequency Cepstral coefficients (MFCC). We use Naive Bayes, K-nearest Neighbors (KNN), and Support Vector Machines (SVM) as machine learning models and Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), and Transformers as deep learning models for comparison purposes. We apply these models on a well-known honey bees audio dataset provided by the NU-hive project and consider classification metrics such as accuracy, precision, recall, and F1 score for the comparative evaluation of our models. Our evaluation demonstrates SVM as the best-performing machine learning algorithm. In particular, SVM with Mel Spectrogram as input data, achieved an accuracy of around 97%. On the other hand, CNN outperformed all the models and achieved an accuracy of 99%, using MFCC features as input data. As a result of these encouraging outcomes, we understand that our results can help the researchers to choose which AI model is more suitable for them to design beehive monitoring systems for accurate identification of abnormal situations in beehives. |
| format | Article |
| id | doaj-art-4d5b3606579f4daba440159402f96378 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4d5b3606579f4daba440159402f963782025-08-20T01:47:50ZengIEEEIEEE Access2169-35362024-01-011214840514842110.1109/ACCESS.2024.347189510701518Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in BeehivesKainat Iqbal0Bayan Alabdullah1Naif Al Mudawi2Asaad Algarni3Ahmad Jalal4https://orcid.org/0009-0000-8421-8477Jeongmin Park5https://orcid.org/0000-0001-8027-0876School of Computing, National University of Computer and Emerging Sciences, Islamabad, PakistanDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi ArabiaDepartment of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi ArabiaFaculty of Computing and AI, Air University, Islamabad, PakistanDepartment of Computer Engineering, Tech University of Korea, Gyeonggi-do, Siheung-si, South KoreaHoneybees play a vital role in preservation of an healthy environment. Bees not only provide pollination services but also produce honey, beeswax, and royal jelly. Beekeeping has a rich history and substantial economic potential worldwide, but swarming remains a crucial challenge for maintaining profitability. Swarming, a typical colony reproductive process in honeybees, significantly impacts beekeepers profitability by lowering the number of bees in hives and thus effecting honey production. Monitoring of these beehives is therefore of paramount importance to keep an eye on their irregular behavior. Swarm prediction can be done by visually inspecting hives, monitoring temperature, or analyzing acoustic features with machine learning. Acoustic monitoring is instrumental in detecting changes in colony behavior since it overcomes the constraints of visual inspections and is not affected by external factors like temperature. In this paper, we aim to evaluate various state-of-the-art machine learning and deep learning models for swarm prediction by studying wave plot features, Mel Spectrogram, and Melfrequency Cepstral coefficients (MFCC). We use Naive Bayes, K-nearest Neighbors (KNN), and Support Vector Machines (SVM) as machine learning models and Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), and Transformers as deep learning models for comparison purposes. We apply these models on a well-known honey bees audio dataset provided by the NU-hive project and consider classification metrics such as accuracy, precision, recall, and F1 score for the comparative evaluation of our models. Our evaluation demonstrates SVM as the best-performing machine learning algorithm. In particular, SVM with Mel Spectrogram as input data, achieved an accuracy of around 97%. On the other hand, CNN outperformed all the models and achieved an accuracy of 99%, using MFCC features as input data. As a result of these encouraging outcomes, we understand that our results can help the researchers to choose which AI model is more suitable for them to design beehive monitoring systems for accurate identification of abnormal situations in beehives.https://ieeexplore.ieee.org/document/10701518/Acousticsaudio signalsaudio classificationbee swarminghoney beeMel spectrogram |
| spellingShingle | Kainat Iqbal Bayan Alabdullah Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives IEEE Access Acoustics audio signals audio classification bee swarming honey bee Mel spectrogram |
| title | Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives |
| title_full | Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives |
| title_fullStr | Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives |
| title_full_unstemmed | Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives |
| title_short | Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives |
| title_sort | empirical analysis of honeybees acoustics as biosensors signals for swarm prediction in beehives |
| topic | Acoustics audio signals audio classification bee swarming honey bee Mel spectrogram |
| url | https://ieeexplore.ieee.org/document/10701518/ |
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