Research Article
On the Power of Deep Learning for Pest Bird Detection in Agriculture
@INPROCEEDINGS{10.1007/978-3-032-15154-4_8, author={Osias No\`{\i}l N. F. Tossou and Yawoa Makafui Sandra Atopia and Mouhamadou Lamine Ba and Idy Diop and Arlindo Veiga}, title={On the Power of Deep Learning for Pest Bird Detection in Agriculture}, proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 8th International Conference, InterSol 2025, Ouagadougou, Burkina Faso, July 3--4, 2025, Proceedings}, proceedings_a={INTERSOL}, year={2026}, month={11}, keywords={Pest Bird YOLO Faster R-CNN C3Det Object Detection Precision Agriculture}, doi={10.1007/978-3-032-15154-4_8} }- Osias Noël N. F. Tossou
Yawoa Makafui Sandra Atopia
Mouhamadou Lamine Ba
Idy Diop
Arlindo Veiga
Year: 2026
On the Power of Deep Learning for Pest Bird Detection in Agriculture
INTERSOL
Springer
DOI: 10.1007/978-3-032-15154-4_8
Abstract
Pest birds represent significant threats to agricultural productivity and cause substantial economic losses around the world. Traditional pest control methods and tracking models are often laborious, harmful to the environment, and ineffective in practice. This paper explores the potential of deep learning models to improve pest bird detection and classification in agricultural settings. We evaluated the performance of three robust state-of-the-art object detection models, which are YoLOv8, Faster R-CNN, and C3Det, using various dataset configurations comprising high-resolution bird images, low-resolution bird images, and a mixed configuration. Our results show that while YOLOv8 excels in real-time detection of high-resolution bird images with a precision of 96.15%, C3Det is better detecting low-resolution bird images with a precision of 97.48%. These findings highlight the power of deep learning models to revolutionize pest-bird management.