Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection

TL;DR

This paper compares YOLOv9, YOLOv10, and RT-DETR models for real-time weed detection, analyzing their accuracy and speed across different configurations to guide optimal model selection for smart spraying systems.

Contribution

It provides a comprehensive evaluation of multiple object detection models and configurations specifically for weed detection in agriculture, highlighting their trade-offs.

Findings

YOLOv10 achieves higher accuracy than YOLOv9 and RT-DETR.

Inference time varies significantly with model size and hardware.

Optimal model choice depends on the specific accuracy-speed requirements.

Abstract

This paper presents a comprehensive evaluation of state-of-the-art object detection models, including YOLOv9, YOLOv10, and RT-DETR, for the task of weed detection in smart-spraying applications focusing on three classes: Sugarbeet, Monocot, and Dicot. The performance of these models is compared based on mean Average Precision (mAP) scores and inference times on different GPU and CPU devices. We consider various model variations, such as nano, small, medium, large alongside different image resolutions (320px, 480px, 640px, 800px, 960px). The results highlight the trade-offs between inference time and detection accuracy, providing valuable insights for selecting the most suitable model for real-time weed detection. This study aims to guide the development of efficient and effective smart spraying systems, enhancing agricultural productivity through precise weed management.