Abstract
Most deep learning-based weed detection methods either yield high accuracy, but are slow for real-time applications or too computationally intensive for implementation on smaller devices usable on resource-constrained platforms like UAVs; on the other hand, most of the faster methods lack good accuracy. In this work, two versions of the deep learning-based YOLOv5 object detection model – YOLOv5n and YOLOv5s - were evaluated for cassava detection as a step towards real-time weed detection. The performance of the models were compared when trained with different image resolutions. The robustness of the models were also evaluated under varying field conditions like illumination, weed density, and crop growth stages. YOLOv5s showed the best accuracy whereas YOLOv5n had the best inference speed. For similar image resolutions, YOLOv5s performed better, however, training YOLOv5n with higher image resolutions could yield better performance than training YOLOv5s with lower image resolutions. Both models were robust to variations in field conditions. The speed vs accuracy plot highlighted a range of possible speed/accuracy trade-offs to guide real-time deployment of the object detection models for cassava detection.
Competing Interest Statement
The authors have declared no competing interest.
