Audio-based identification of beehive states

Nolasco, Ines; Terenzi, Alessandro; Cecchi, Stefania; Orcioni, Simone; Bear, Helen L.; Benetos, Emmanouil

doi:10.48550/arxiv.1811.06330

Cited by 2 publications

(3 citation statements)

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“…For feature extraction, a classical MFCCs methodology was implemented. MFCCs has been effectively used in the preprocessing step of acoustic patterns of bee colonies [ 10 , 15 , 24 , 30 , 35 , 36 ]. The classical methodology includes the following stages: a pre-emphasis filter, windowing of the signal, application of the fast Fourier transform, warping the frequencies on a Mel scale, the application of a triangular filter bank, and the inverse discrete cosine transform.…”

Section: Methodsmentioning

confidence: 99%

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Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources

Robles-Guerrero

Saucedo-Anaya

Guerrero-Mendez

et al. 2023

Sensors

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In precision beekeeping, the automatic recognition of colony states to assess the health status of bee colonies with dedicated hardware is an important challenge for researchers, and the use of machine learning (ML) models to predict acoustic patterns has increased attention. In this work, five classification ML algorithms were compared to find a model with the best performance and the lowest computational cost for identifying colony states by analyzing acoustic patterns. Several metrics were computed to evaluate the performance of the models, and the code execution time was measured (in the training and testing process) as a CPU usage measure. Furthermore, a simple and efficient methodology for dataset prepossessing is presented; this allows the possibility to train and test the models in very short times on limited resources hardware, such as the Raspberry Pi computer, moreover, achieving a high classification performance (above 95%) in all the ML models. The aim is to reduce power consumption and improves the battery life on a monitor system for automatic recognition of bee colony states.

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Section: Methodsmentioning

confidence: 99%

“…For the detection of queenless bee colonies, Nolasco et al [ 30 ] implemented a classification methodology based on previous works, SVM and a CNN models were implemented for the task. The dataset consists of the beehive sound of the NU-Hive project.…”

Section: State Of the Artmentioning

confidence: 99%

Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources

Robles-Guerrero

Saucedo-Anaya

Guerrero-Mendez

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Solomes and Stowell found it is possible to automatically identify species of birds according to features of their vocalizations [ 16 ], and results indicate a reliable accuracy of 87% for the classification of 12 call classes. Other researchers have used deep learning methods to automatically recognize different states of a beehive using audio as the input [ 17 ]. Kiskin et al utilized a dataset of audio recordings of mosquitoes with 70.3% accuracy to classify species by their flight tones [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Giant Panda Attributes from Their Vocalizations Based on Squeeze-and-Excitation Network

Zhao

Zhang

Hou

et al. 2022

Sensors

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The giant panda (Ailuropoda melanoleuca) has long attracted the attention of conservationists as a flagship and umbrella species. Collecting attribute information on the age structure and sex ratio of the wild giant panda populations can support our understanding of their status and the design of more effective conservation schemes. In view of the shortcomings of traditional methods, which cannot automatically recognize the age and sex of giant pandas, we designed a SENet (Squeeze-and-Excitation Network)-based model to automatically recognize the attributes of giant pandas from their vocalizations. We focused on the recognition of age groups (juvenile and adult) and sex of giant pandas. The reason for using vocalizations is that among the modes of animal communication, sound has the advantages of long transmission distances, strong penetrating power, and rich information. We collected a dataset of calls from 28 captive giant panda individuals, with a total duration of 1298.02 s of recordings. We used MFCC (Mel-frequency Cepstral Coefficients), which is an acoustic feature, as inputs for the SENet. Considering that small datasets are not conducive to convergence in the training process, we increased the size of the training data via SpecAugment. In addition, we used focal loss to reduce the impact of data imbalance. Our results showed that the F1 scores of our method for recognizing age group and sex reached 96.46% ± 5.71% and 85.85% ± 7.99%, respectively, demonstrating that the automatic recognition of giant panda attributes based on their vocalizations is feasible and effective. This more convenient, quick, timesaving, and laborsaving attribute recognition method can be used in the investigation of wild giant pandas in the future.

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Audio-based identification of beehive states

Cited by 2 publications

References 0 publications

Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources

Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources

Automatic Recognition of Giant Panda Attributes from Their Vocalizations Based on Squeeze-and-Excitation Network

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