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

Destiny Kwabla Amenyedzi, Micheline Kazeneza, Ipyana Issah Mwaisekwa, Frederic Nzanywayingoma, Philibert Nsengiyumva, Peace Bamurigire, Emmanuel Ndashimye and Anthony Vodacek ()
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Destiny Kwabla Amenyedzi: African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali 3900, Rwanda
Micheline Kazeneza: African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali 3900, Rwanda
Ipyana Issah Mwaisekwa: African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali 3900, Rwanda
Frederic Nzanywayingoma: African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali 3900, Rwanda
Philibert Nsengiyumva: African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali 3900, Rwanda
Peace Bamurigire: African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali 3900, Rwanda
Emmanuel Ndashimye: Department of Information Technology, Regional ICT Center of Excellence Bldg, Kigali Innovation City, Carnegie Mellon University Africa, Bumbogo BP6150, Kigali, Rwanda
Anthony Vodacek: Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA

Agriculture, 2024, vol. 15, issue 1, 1-19

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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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