| dc.contributor.author |
Kwabla Amenyedzi, Destiny |
|
| dc.contributor.author |
Kazeneza, Micheline |
|
| dc.contributor.author |
Vodacek, Anthony |
|
| dc.contributor.author |
Julius Maginga, Theofrida |
|
| dc.contributor.author |
Nzanywayingoma, Frederic |
|
| dc.contributor.author |
Nsengiyumva, Philibert |
|
| dc.contributor.author |
Bamurigire, Peace |
|
| dc.contributor.author |
Ndashimye, Emmanuel |
|
| dc.date.accessioned |
2023-10-31T10:25:12Z |
|
| dc.date.available |
2023-10-31T10:25:12Z |
|
| dc.date.issued |
2023-07-07 |
|
| dc.identifier.uri |
https://repository.rsif-paset.org/xmlui/handle/123456789/288 |
|
| dc.description |
Full text: https://doi.org/10.1109/EUROCON56442.2023.10198956 |
en_US |
| dc.description.abstract |
In Africa the realization of the Sustainable Development Goal 2 (SDG-2) of zero hunger by 2030 is threatened by agricultural pests. Acoustic technology offers one method for pest detection in agriculture. AudioMoth microphones were deployed in three farms in Rwanda. Band filters were applied to the files for listening to pest calls for labeling. The WAV files were processed using Scikit-maad package in Python to spectrograms, which allowed for visualization. The regions of interest were selected to be used for labeling. Results suggest that birds are the dominant pest during the morning sections of the day while frogs are active at night. The contribution of this work is to provide a roadmap towards labeling for detection of agricultural pests using machine learning at the edge. |
en_US |
| dc.publisher |
IEEE Xplore |
en_US |
| dc.subject |
AudioMoth , acoustics , agriculture , audio signal processing , pest , Rwanda , Africa |
en_US |
| dc.title |
Signal Preprocessing Towards IoT Acoustic Data for Farm Pest Detection |
en_US |
| dc.type |
Article |
en_US |