CNN based Framework for Classification of Mosquitoes based on its Wingbeats

Arpitha, M S; Rani, Shilpa; Lavanya, M.

doi:10.1109/icicv50876.2021.9388600

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The selected machine learning algorithm (s) dictates the type of features extracted. For instance, literature shows that shallow machine learning algorithms were trained on numerical features (Zhang and Guo, 2010 ; Yazgaç et al, 2016 ; Kawakita and Ichikawa, 2019 ; Noda et al, 2019 ), while deep learning algorithms were trained on numerical or spectrogram image features (Dong et al, 2018 ; Kiskin et al, 2020 ; Arpitha et al, 2021 ; Tey et al, 2022 ). Herein, we extracted numerical (chroma, MFCC, and Linear Frequency Cepstral Coefficients (LFCC)) and image (i.e., spectrograms) features since they were widely used by other researchers (Noda et al, 2016 , 2019 ; Yazgaç et al, 2016 ; Zamanian and Pourghassem, 2017 ; Dong et al, 2018 ; Kawakita and Ichikawa, 2019 ; Tey et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…Automatic sound/audio signal processing can be improved using machine learning (Noda et al, 2016 , 2019 ; Phung et al, 2017 ; Kawakita and Ichikawa, 2019 ). Machine learning has successfully been deployed in the identification and classification of insects based on their species (Phung et al, 2017 ; Zamanian and Pourghassem, 2017 ), acoustics (Amlathe, 2018 ; Kiskin et al, 2020 ; Zhang et al, 2021 ), wingbeats (Arpitha et al, 2021 ; Kim et al, 2021 ), etc. For instance, Kawakita and Ichikawa ( 2019 ) explored the classification of bees and hornets based on their flight sounds using the support vector machine (SVM) algorithm combined with Mel-frequency cepstral coefficient (MFCC) features.…”

Section: Introductionmentioning

confidence: 99%

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A convolutional neural network with image and numerical data to improve farming of edible crickets as a source of food—A decision support system

Kyalo,

Tonnang,

Egonyu

et al. 2024

Front. Artif. Intell.

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Crickets (Gryllus bimaculatus) produce sounds as a natural means to communicate and convey various behaviors and activities, including mating, feeding, aggression, distress, and more. These vocalizations are intricately linked to prevailing environmental conditions such as temperature and humidity. By accurately monitoring, identifying, and appropriately addressing these behaviors and activities, the farming and production of crickets can be enhanced. This research implemented a decision support system that leverages machine learning (ML) algorithms to decode and classify cricket songs, along with their associated key weather variables (temperature and humidity). Videos capturing cricket behavior and weather variables were recorded. From these videos, sound signals were extracted and classified such as calling, aggression, and courtship. Numerical and image features were extracted from the sound signals and combined with the weather variables. The extracted numerical features, i.e., Mel-Frequency Cepstral Coefficients (MFCC), Linear Frequency Cepstral Coefficients, and chroma, were used to train shallow (support vector machine, k-nearest neighbors, and random forest (RF)) ML algorithms. While image features, i.e., spectrograms, were used to train different state-of-the-art deep ML models, i,e., convolutional neural network architectures (ResNet152V2, VGG16, and EfficientNetB4). In the deep ML category, ResNet152V2 had the best accuracy of 99.42%. The RF algorithm had the best accuracy of 95.63% in the shallow ML category when trained with a combination of MFCC+chroma and after feature selection. In descending order of importance, the top 6 ranked features in the RF algorithm were, namely humidity, temperature, C#, mfcc11, mfcc10, and D. From the selected features, it is notable that temperature and humidity are necessary for growth and metabolic activities in insects. Moreover, the songs produced by certain cricket species naturally align to musical tones such as C# and D as ranked by the algorithm. Using this knowledge, a decision support system was built to guide farmers about the optimal temperature and humidity ranges and interpret the songs (calling, aggression, and courtship) in relation to weather variables. With this information, farmers can put in place suitable measures such as temperature regulation, humidity control, addressing aggressors, and other relevant interventions to minimize or eliminate losses and enhance cricket production.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A convolutional neural network with image and numerical data to improve farming of edible crickets as a source of food—A decision support system

Kyalo,

Tonnang,

Egonyu

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

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Cicada Species Recognition Based on Acoustic Signals

et al. 2022

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Traditional methods used to identify and monitor insect species are time-consuming, costly, and fully dependent on the observer’s ability. This paper presents a deep learning-based cicada species recognition system using acoustic signals to classify the cicada species. The sound recordings of cicada species were collected from different online sources and pre-processed using denoising algorithms. An improved Härmä syllable segmentation method is introduced to segment the audio signals into syllables since the syllables play a key role in identifying the cicada species. After that, a visual representation of the audio signal was obtained using a spectrogram, which was fed to a convolutional neural network (CNN) to perform classification. The experimental results validated the robustness of the proposed method by achieving accuracies ranging from 66.67% to 100%.

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CNN based Framework for Classification of Mosquitoes based on its Wingbeats

Cited by 2 publications

References 11 publications

A convolutional neural network with image and numerical data to improve farming of edible crickets as a source of food—A decision support system

A convolutional neural network with image and numerical data to improve farming of edible crickets as a source of food—A decision support system

Cicada Species Recognition Based on Acoustic Signals

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