System Design for a Prototype Acoustic Network to Deter Avian Pests in Agriculture Fields

Publication Type

Journal Article

Name of Author

• Destiny Kwabla Amenyedzi, African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Rwanda;
• Micheline Kazeneza, African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda
• Ipyana Issah Mwaisekwa, African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda
• Frederic Nzanywayingoma, African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda
• Philibert Nsengiyumva, African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda
• Peace Bamurigire, African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda
• Emmanuel Ndashimye, Department of Information Technology, Regional ICT Center of Excellence Bldg, Kigali Innovation City, Carnegie Mellon University Africa
• Anthony Vodacek, Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, USA

Publication Date (Issue Year)

2024

Journal Name

MDPI-Agriculture

Abstract

Crop damage attributed to pest birds is an important problem, particularly in low-income countries. This paper describes a prototype system for pest bird detection using a Conv1D neural network model followed by scaring actions to reduce the presence of pest birds on farms. Acoustic recorders were deployed on farms for data collection, supplemented by acoustic libraries. The sounds of pest bird species were identified and labeled. The labeled data were used in Edge Impulse to train a tinyML Conv1D model to detect birds of interest. The model was deployed on Arduino Nano 33 BLE Sense (nodes) and XIAO (Base station) microcontrollers to detect the pest birds, and based on the detection, scaring sounds were played to deter the birds. The model achieved an accuracy of 96.1% during training and 92.99% during testing. The testing F1 score was 0.94, and the ROC score was 0.99, signifying a good discriminatory ability of the model. The prototype was able to make inferences in 53 ms using only 14.8 k of peak RAM and only 43.8 K of flash memory to store the model. Results from the prototype deployment in the field demonstrated successful detection and triggering actions and SMS messaging notifications. Further development of this novel integrated and sustainable solution will add another tool for dealing with pest birds.

Keywords

pest birds, Edge Impulse, feature selection, tinyML, Mel-Filterbank energy, Conv1D, acoustic network, edge

Grantee Name(s)

Emmanuel Ndashimye

Project Title

Agricultural Data from Acoustic Monitoring

Type of Grant

Research Award

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Funding Statement

This work is funded by Partnership for Skills in Applied Sciences, Engineering and Technology Regional Scholarship for Innovation Fund RSIF/RA/009 by International Centre of Insect Physiology and Ecology (icipe) to E. Ndashimye, The PASET Regional Scholarship and Innovation Fund as part of a Ph.D. work scholarship to D. K. Amenyedzi and M. Kazeneza, and the program hosted at the African Centre of Excellence in Internet of Things, University of Rwanda. We are also grateful to Global RIT for providing funding for D. K. Amenyedzi’s International Partner Institution placement at Rochester Institute of Technology, Rochester, NY, USA.

Recommended Citation

Amenyedzi, D. K., Kazeneza, M., Mwaisekwa, I. I., Nzanywayingoma, F., Nsengiyumva, P., Bamurigire, P., Ndashimye, E., & Vodacek, A. (2024). System Design for a Prototype Acoustic Network to Deter Avian Pests in Agriculture Fields. MDPI-Agriculture https://doi.org/10.3390/agriculture15010010