Balancing complexity and performance of machine learning models for avian pests sound detection in agricultural environments
dc.contributor.author | Kazeneza, Micheline | |
dc.contributor.author | Bosman, Anna Sergeevna | |
dc.contributor.author | Amenyedzi, Destiny Kwabla | |
dc.contributor.author | Hanyurwimfura, Damien | |
dc.contributor.author | Ndashimye, Emmanuel | |
dc.contributor.author | Vodacek, Anthony | |
dc.date.accessioned | 2025-07-29T11:26:37Z | |
dc.date.available | 2025-07-29T11:26:37Z | |
dc.date.issued | 2025-06 | |
dc.description.abstract | Agricultural pest control traditionally relies on inefficient visual inspections. Acoustic monitoring offers a promising alternative by analyzing pest-specific sounds. While effective, implementing acoustic monitoring in agricultural settings faces practical constraints, particularly the limited computational resources available in remote farming environments. This necessitates optimized machine learning (ML) solutions for low-power edge devices. This study evaluates ML models for bird pest detection on resource-constrained platforms. We evaluated convolutional neural networks (CNNs), recurrent neural networks (RNNs), and traditional ML models by comparing standalone and knowledge-distilled versions of EfficientNetB0 and gated recurrent unity (GRU) against EfficientNetB4, Long short-term memory (LSTM), MobileNetV2, LightGBM, and support vector machine (SVM). Analysis revealed significant performance variations across computational requirements. LightGBM achieved 98% accuracy with minimal resources (8,500 parameters, 7KB, 0.6ms inference), demonstrating good efficiency. SVM (97% accuracy) and distilled GRU (86% accuracy) also showed favorable performance-to-resource ratios. Knowledge distillation substantially enhanced the accuracy of EfficientNetB0 (from 73% to 98%) and modestly improved GRU (from 84% to 86%). We examined platform compatibility across computing tiers, discovering that while high-performance edge devices (Jetson Nano, Raspberry Pi 4) support all studied models effectively, microcontrollers require specialized approaches. Advanced microcontrollers (such as ESP32-S3 and STM32H7) can accommodate optimized implementations, while highly constrained platforms (such as Arduino Nano) require TinyML techniques. This research contributes 1) an on-farm audio dataset, 2) comprehensive cross-model evaluation metrics, and 3) deployment optimization strategies for acoustic pest detection systems in resource-constrained agricultural environments. | |
dc.description.department | Computer Science | |
dc.description.librarian | hj2025 | |
dc.description.sdg | SDG-02: Zero Hunger | |
dc.description.sdg | SDG-09: Industry, innovation and infrastructure | |
dc.description.sponsorship | This work was supported in part by the icipe-World Bank Financing, in part by the World Bank-Korea Trust Fund for Partnership for Skills in Applied Sciences, Engineering and Technology (PASET)-Regional Scholarship and Innovation Fund (RSIF), and in part by the University of Pretoria through PASET-RSIF Research Funds. | |
dc.description.uri | http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 | |
dc.identifier.citation | M. Kazeneza, A.S. Bosman, D.K. Amenyedzi, D. Hanyurwimfura, E. Ndashimye and A. Vodacek, "Balancing Complexity and Performance of Machine Learning Models for Avian Pests Sound Detection in Agricultural Environments," in IEEE Access, vol. 13, pp. 105813-105827, 2025, doi: 10.1109/ACCESS.2025.3580620. | |
dc.identifier.issn | 2169-3536 (online) | |
dc.identifier.other | 10.1109/ACCESS.2025.3580620 | |
dc.identifier.uri | http://hdl.handle.net/2263/103663 | |
dc.language.iso | en | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.rights | © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. See https://creativecommons.org/licenses/by/4.0. | |
dc.subject | Machine learning | |
dc.subject | Knowledge distillation | |
dc.subject | Deep learning | |
dc.subject | Bird pest detection | |
dc.subject | Acoustics | |
dc.subject | Computational modeling | |
dc.subject | Recording | |
dc.subject | Complexity theory | |
dc.subject | Accuracy | |
dc.subject | Monitoring | |
dc.subject | Spectrogram | |
dc.subject | Computer architecture | |
dc.subject | Acoustic monitoring | |
dc.subject | Agricultural pest control | |
dc.title | Balancing complexity and performance of machine learning models for avian pests sound detection in agricultural environments | |
dc.type | Article |