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Title: Automatic detection of alien plant species in action camera images using the chopped picture method and the potential of citizen science
Authors: Takaya, Kosuke
Sasaki, Yu
Ise, Takeshi  kyouindb  KAKEN_id
Author's alias: 高屋, 浩介
佐々木, 優
伊勢, 武史
Keywords: deep learning
chopped picture method
alien plant
Solidago altissima
action camera
citizen science
computer vision
Issue Date: 2022
Publisher: Japanese Society of Breeding
Journal title: Breeding Science
Volume: 72
Issue: 1
Start page: 96
End page: 106
Abstract: Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost.
DOI(Published Version): 10.1270/jsbbs.21062
PubMed ID: 36045894
Appears in Collections:Journal Articles

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