TY - JOUR
T1 - Application of Deep Learning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species
AU - Khalighifar, Ali
AU - Jiménez-García, Daniel
AU - Campbell, Lindsay P.
AU - Ahadji-Dabla, Koffi Mensah
AU - Aboagye-Antwi, Fred
AU - Ibarra-Juárez, Luis Arturo
AU - Peterson, A. Townsend
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Oxford University Press on behalf of Entomological Society of America.All rights reserved.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.
AB - Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.
KW - bioacoustics
KW - convolutional neural networks
KW - smartphones
KW - transfer learning
KW - vector-borne diseases
UR - http://www.scopus.com/inward/record.url?scp=85123645962&partnerID=8YFLogxK
U2 - 10.1093/jme/tjab161
DO - 10.1093/jme/tjab161
M3 - Article
C2 - 34546359
AN - SCOPUS:85123645962
SN - 0022-2585
VL - 59
SP - 355
EP - 362
JO - Journal of Medical Entomology
JF - Journal of Medical Entomology
IS - 1
ER -