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Automated Classification of Bees and Hornet Using Acoustic Analysis of Their Flight Sounds Satoshi Kawakita, Kotaro Ichikawa

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Automated Classification of Bees and Hornet Using Acoustic Analysis of Their Flight Sounds Satoshi Kawakita, Kotaro Ichikawa View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Archive Ouverte en Sciences de l'Information et de la Communication Automated classification of bees and hornet using acoustic analysis of their flight sounds Satoshi Kawakita, Kotaro Ichikawa To cite this version: Satoshi Kawakita, Kotaro Ichikawa. Automated classification of bees and hornet using acoustic anal- ysis of their flight sounds. Apidologie, Springer Verlag, 2019, 50 (1), pp.71-79. 10.1007/s13592-018- 0619-6. hal-02436359 HAL Id: hal-02436359 https://hal.archives-ouvertes.fr/hal-02436359 Submitted on 13 Jan 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Apidologie (2019) 50:71–79 Original article * INRA, DIB and Springer-Verlag France SAS, part of Springer Nature, 2019 DOI: 10.1007/s13592-018-0619-6 Automated classification of bees and hornet using acoustic analysis of their flight sounds 1 2 Satoshi KAWAKITA , Kotaro ICHIKAWA 1Western Region Agricultural Research Center, National Agriculture and Food Research Organization, 6-12-1 Nishifukatsu-cho, Fukuyama, Hiroshima 721-8514, Japan 2Field Science Education and Research Center, Kyoto University, Kyoto 606-8502, Japan Received3July2018– Revised 29 October 2018 – Accepted 19 November 2018 Abstract – To investigate how to accurately identify bee species using their sounds, we conducted acoustic analysis to identify three pollinating bee species (Apis mellifera , Bombus ardens , Tetralonia nipponensis )andahornet (Vespa simillima xanthoptera ) by their flight sounds. Sounds of the insects and their environment (background noises and birdsong) were recorded in the field. The use of fundamental frequency and mel-frequency cepstral coefficients to describe feature values of the sounds, and supported vector machines to classify the sounds, correctly distinguished sound samples from environmental sounds with high recalls and precision (0.96–1.00). At the species level, our approach could classify the insect species with relatively high recalls and precisions (0.7–1.0). The flight sounds of V.s. xanthoptera , in particular, were perfectly identified (precision and recall 1.0). Our results suggest that insect flight sounds are potentially useful for detecting bees and quantifying their activity. species classification / Hymenoptera / machine learning / acoustic analysis 1. INTRODUCTION habitat use (Miller-Struttmann et al. 2017;Hill et al. 2018). Monitoring insect activity and detect- Monitoring insect activity is useful for many ing insects are thus useful in both agricultural purposes, such as pest control and monitoring production and ecological research. beneficial insects. For pest control, it is important Several methods for monitoring insects auto- to spray pesticides at the right time, but scheduling matically have been developed to date. For exam- pesticide application is difficult for farmers since ple, image processing and analysis techniques are the occurrence of pest species is hard to predict. used to identify orchard insects automatically For pollination in greenhouses, monitoring the (Wen and Guyer 2012), and Zhu et al. (2017) activity of bees is useful in managing their activity developed a method to detect Lepidoptera species and knowing when to replace nest boxes (Fisher in digital images using a cascade architecture and Pomeroy 1989;Morandinetal.2001). Detec- which combines deep convolutional neural net- tion of insects can also be used to better under- works and supported vector machines. stand the biodiversity of pollinators and their Another way to monitor insect activity is to use acoustic or vibrational information. Such analysis can be used at night or in places where it is Electronic supplementary material The online version of this article (https://doi.org/10.1007/s13592-018-0619-6) impractical to use digital cameras, such as under- contains supplementary material, which is available to ground or in dense grass. For example, Celis- authorized users. Murillo et al. (2009) studied birdsong to investi- Corresponding author: S. Kawakita, gate bird species and density in a range of places, [email protected] and reported that acoustic analysis performed bet- Manuscript editor: Monique Gauthier ter than the human ear. In addition, acoustic 72 S. Kawakita, K. Ichikawa analysis was used in postharvest management for surveys, and we expect that the methods developed monitoring insects such as rice weevils, Sitophilus in this study will contribute to the monitoring of oryzae , in grain storage (Fleurat-Lessard et al. hornet and bee activities in an ecological context. 2006; Njoroge et al. 2017). Towsey et al. (2014) demonstrated that the use of acoustic indices 2. MATERIALS AND METHODS could identify the cicada chorus in the natural environment, and Lampson et al. (2013) devel- Sounds were sampled using a microphone oped automatic identification methods for stink (AT9905, Audio-Technica, Tokyo, Japan) con- bugs (Euschistus servus and Nezara viridula ) nected to a portable linear PCM recorder (R-05 using acoustic analysis of intraspecific substrate- WAVE/MP3 Recorder, Roland, Shizuoka, Japan). borne vibrational signals. Recently, Gradišek et al. The microphone was connected with the edge of a (2017) tried to discriminate bumblebee species metal stick, and we gently approached the flying using the acoustic features of their flight sounds, bee and hornet species with the microphone. The and found that the different species differed in sounds were sampled at 44.1 kHz with a resolu- their flight sounds. In this way, acoustic/ tion of 16 bits. The raw sound data were processed vibrational-based monitoring technology is be- in Adobe Audition CC sound analysis software coming popular, but previous studies have fo- (Adobe Systems Incorporated, CA, USA). cused on the Cicadae and Orthoptera (Obrist The experiments were conducted in rural areas et al. 2010) or specific bee species such as bumble or remote forests in Fukuyama and Kyoto, west- bees (De Luca et al. 2014;Gradišek et al. 2017; ern Japan. We collected the flying sounds of Miller-Struttmann et al. 2017), and, to our knowl- A. mellifera , B. ardens , T. nipponensis ,and edge, there are still few studies that focus on V. simillima xanthoptera . We chose these species identifying different types of bees by their sounds. since they are commonly observed in the country- Especially, in practical sense, distinguishing pred- side in Japan (especially B. ardens , A. mellifera , ators and pollinators are important for beekeepers and V. simillima xanthoptera ). In terms of the or ecologist so that investigating whether the body size, V. simillima xanthoptera was largest acoustic analysis can identify bee species into among four species, and B. ardens was slightly functional group is informative. larger than other two pollinator species (unpub- The objective of our study was to develop lished data). The bees were all female and their methods to distinguish bee species from environ- sounds were recorded when they approached mental sounds recorded under natural field condi- flowering herbs. The flight sounds of V. s. tions. Here, we analyzed the flight sounds of three xanthoptera were recorded when they hovered bee species which are popular pollinators in Japan, close to honey bee nest boxes. In Adobe Audition including western honey bees, Apis mellifera CC, we extracted 200 samples of A. mellifera and (Apidae: Apinae), Bombus ardens (Apidae: B. ardens sounds, 160 samples of T. nipponensis Bombus), Tetralonia nipponensis (Apidae: sounds, and 120 samples of V. s. xanthoptera Eucerini), and one hornet species, the Japanese sounds in wav file format. Most recordings were yellow hornet, Vespa simillima xanthoptera 0.3 to 1.0 s long. We also collected 200 recordings (Vespidae: Vespa), which is a predator of honey- of background sounds and unspecified birdsong bees in Japan. We expect that technology that can (mostly from sparrows). Most of the background identify such insects against background noise will sounds we heard were wind sounds, and sounds be useful for the evaluation of pollination services, made by leaves swaying in the wind. To under- and the study of behavioral ecology. Bees produce stand the sound features of the four insect species, specific flight sounds, and some insect species, we investigated the fundamental frequency of such as hornets, produce particularly distinctive each species by inspection of spectrums of their sounds. As such, we expected that flight sounds flight sounds using Adobe Audition CC. of some bees could be identified automatically We used machine learning techniques to clas- using acoustic features. Monitoring predator-prey sify sound recordings as the sounds made by the relationships is particularly important in ecological three bee species, the hornet species, birdsong, Automated classification of bee flight sounds 73 and background sounds. We split the sample data a hyperplane which divides a certain dataset into into training data (80% of total samples) for cali- different classes. The
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