Original scientific paper

https://doi.org/10.31803/tg-20240909000328

Implementation of Birds Detection System using the YOLOv5 Model

Durodola Folasade ; Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria *
Eludiora Safiriyu ; Department of Computer Science and Engineering, Obafemi Awolowo University, Ile Ife, Nigeria
Owoeye Samuel ; Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria
Makinde Kayode ; Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria
Folaranmi Olaniyi ; Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria
Kamil-Bello Furqan ; Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria
Daodu Sakira ; Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria

* Corresponding author.

Abstract

This work considers a bird detection system using the YOLOv5 model in handling the challenge of avian-induced crop damages in agriculture. In this study, machine learning and mechatronics will be combined to develop an effective adaptive bird deterrent system. The methodology of the study will comprise data collection from farms and Kaggle, data augmentation techniques, and implementation on Google Colab. The baseline models developed with primary and secondary datasets are compared with a hybrid model and principal model on an augmented dataset containing 14,500 samples. Results show the impact of both dataset size and augmentation techniques is on model performance. The Hybrid Model returned a true positive rate for bird detection as high as 89%, while the principal model had further improvements, returning an accuracy of 85% in bird-type classification. The research proves that augmentation, diversity of datasets, and threshold optimization are three relevant areas related to the robustness of models for bird detection. Future work could be done by increasing dataset size, fine-tuning the model, and fusing multiple modalities to enhance real-world performance.

Keywords

artificial intelligence; bird identification; data augmentation; precision agriculture; YOLOv5

Hrčak ID:

351723

URI

https://hrcak.srce.hr/351723

Publication date:

15.12.2026.

Visits: 0 *