Automated classification of bees and hornet using acoustic analysis of their flight sounds

Kawakita, Satoshi; Ichikawa, Katsuhiko

doi:10.1007/s13592-018-0619-6

Cited by 38 publications

(49 citation statements)

References 33 publications

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“…The fusion of coefficients increases the efficiency and reliability of the system making the proposed approach unique in terms of insect species recognition. Compared to previous works, this proposal is capable of identifying and classifying 343 species of either katydids crickets or cicadas on the level of specific species with higher success rates than previous systems, as shown in [14][15][16][17][18].…”

Section: State Of the Artmentioning

confidence: 89%

“…The 57 mosquito recordings used in this work were labeled with a success rate of 92.2% detecting the presence or absence of mosquitoes surpassing human labelers. More recently, in [18], the authors identified three bee species and a hornet analyzing their flight sounds. The method was able to correctly distinguish between flight sounds and environmental sounds, but only had a precision of 70-90% in terms of species-specific identification.…”

Section: State Of the Artmentioning

confidence: 99%

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Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

et al. 2019

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This work introduces a new approach for automatic identification of crickets, katydids and cicadas analyzing their acoustic signals. We propose the building of a tool to identify this biodiversity. The study proposes a sound parameterization technique designed specifically for identification and classification of acoustic signals of insects using Mel Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC). These two sets of coefficients are evaluated individually as has been done in previous studies and have been compared with the fusion proposed in this work, showing an outstanding increase in identification and classification at species level reaching a success rate of 98.07% on 343 insect species.

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Section: State Of the Artmentioning

confidence: 89%

Section: State Of the Artmentioning

confidence: 99%

Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

et al. 2019

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“…Machine Learning techniques have demonstrated high efficiency and accuracy for classification of bumblebees and other groups of bees based on the characteristics of their buzzing-sounds [ 19 , 35 , 36 ]. Therefore, we chose some of the most common used ML classifiers to recognize the taxonomic identity of bees during visits to tomato flowers (according to S1 Table ): Logistic Regression [ 52 ], Support Vector Machines [ 53 , 54 ], Random Forest [ 55 ], Decision Trees [ 56 , 57 ], and a classifier ensemble [ 58 , 59 ]—a combination of multiple and diversified classifiers to generate a single classifier model.…”

Section: Methodsmentioning

confidence: 99%

“…These studies sought to differentiate the bee buzzing sounds from other sounds (cricket chirping and ambient noise) [ 31 ], recognize the presence of the queen in a beehive and detect an orphaned colony [ 32 , 33 ], or identify the circadian rhythm of a honeybee colony [ 34 ]. However, only three studies address the problem of automatic bee species classification, and these deal with twelve, two and four classes respectively [ 19 , 35 , 36 ]. Random Forest, Support Vector Machines, and Logistic Regression are the most applied classifiers, and Mel Frequency Cepstral Coefficients (MFCC) is the most used feature extraction strategy (see S1 Table ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds

et al. 2021

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Bee-mediated pollination greatly increases the size and weight of tomato fruits. Therefore, distinguishing between the local set of bees–those that are efficient pollinators–is essential to improve the economic returns for farmers. To achieve this, it is important to know the identity of the visiting bees. Nevertheless, the traditional taxonomic identification of bees is not an easy task, requiring the participation of experts and the use of specialized equipment. Due to these limitations, the development and implementation of new technologies for the automatic recognition of bees become relevant. Hence, we aim to verify the capacity of Machine Learning (ML) algorithms in recognizing the taxonomic identity of visiting bees to tomato flowers based on the characteristics of their buzzing sounds. We compared the performance of the ML algorithms combined with the Mel Frequency Cepstral Coefficients (MFCC) and with classifications based solely on the from fundamental frequency, leading to a direct comparison between the two approaches. In fact, some classifiers powered by the MFCC–especially the SVM–achieved better performance compared to the randomized and sound frequency-based trials. Moreover, the buzzing sounds produced during sonication were more relevant for the taxonomic recognition of bee species than analysis based on flight sounds alone. On the other hand, the ML classifiers performed better in recognizing bees genera based on flight sounds. Despite that, the maximum accuracy obtained here (73.39% by SVM) is still low compared to ML standards. Further studies analyzing larger recording samples, and applying unsupervised learning systems may yield better classification performance. Therefore, ML techniques could be used to automate the taxonomic recognition of flower-visiting bees of the cultivated tomato and other buzz-pollinated crops. This would be an interesting option for farmers and other professionals who have no experience in bee taxonomy but are interested in improving crop yields by increasing pollination.

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“…Harmonic scanning radars can detect insects flying at low altitudes at a range of several hundred meters, but insects need to be tagged with a radar transponder and must be within line-ofsight (53,54). Collectively, the use of radar technology in entomology can provide valuable shown that even species classification can be done using machine learning on audio data, for example for birds (58), bats (61), grasshoppers (62), and bees (63). Although, it has been argued that the use of pseudo-acoustic optical sensors rather than actual acoustic sensors is a more promising technology because of the much improved signal-to-noise ratio in these systems, which may be a particularly important point for bioacoustics in entomology (64).…”

Section: Radar Acoustic and Other Solutions For In Situ Monitoringmentioning

confidence: 99%

Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2020

Preprint

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ABSTRACTMost animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is still sparse. Insect populations are challenging to study and most monitoring methods are labour intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors that can effectively, continuously, and non-invasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the lab. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behaviour, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to the big data outputs to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) Validation of image-based taxonomic identification, 2) generation of sufficient training data, 3) development of public, curated reference databases, and 4) solutions to integrate deep learning and molecular tools.Significance statementInsect populations are challenging to study, but computer vision and deep learning provide opportunities for continuous and non-invasive monitoring of biodiversity around the clock and over entire seasons. These tools can also facilitate the processing of samples in a laboratory setting. Automated imaging in particular can provide an effective way of identifying and counting specimens to measure abundance. We present examples of sensors and devices of relevance to entomology and show how deep learning tools can convert the big data streams into ecological information. We discuss the challenges that lie ahead and identify four focal areas to make deep learning and computer vision game changers for entomology.

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Automated classification of bees and hornet using acoustic analysis of their flight sounds

Cited by 38 publications

References 33 publications

Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds

Deep learning and computer vision will transform entomology

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