WeedScout: Real-Time Autonomous blackgrass Classification and Mapping using dedicated hardware

TL;DR

WeedScout introduces a real-time, AI-powered system for blackgrass detection and mapping in agriculture, utilizing optimized YOLO models on edge hardware to improve weed management and reduce environmental impact.

Contribution

The paper presents a novel real-time blackgrass detection system using optimized YOLO models on NVIDIA Jetson Nano, with datasets and models made publicly available for further research.

Findings

Achieved real-time blackgrass detection with optimized YOLO models.

Demonstrated effective deployment of AI on edge hardware for agriculture.

Provided datasets and model weights to support future research.

Abstract

Blackgrass (Alopecurus myosuroides) is a competitive weed that has wide-ranging impacts on food security by reducing crop yields and increasing cultivation costs. In addition to the financial burden on agriculture, the application of herbicides as a preventive to blackgrass can negatively affect access to clean water and sanitation. The WeedScout project introduces a Real-Rime Autonomous Black-Grass Classification and Mapping (RT-ABGCM), a cutting-edge solution tailored for real-time detection of blackgrass, for precision weed management practices. Leveraging Artificial Intelligence (AI) algorithms, the system processes live image feeds, infers blackgrass density, and covers two stages of maturation. The research investigates the deployment of You Only Look Once (YOLO) models, specifically the streamlined YOLOv8 and YOLO-NAS, accelerated at the edge with the NVIDIA Jetson Nano (NJN). By optimising inference speed and model performance, the project advances the integration of AI into agricultural practices, offering potential solutions to challenges such as herbicide resistance and environmental impact. Additionally, two datasets and model weights are made available to the research community, facilitating further advancements in weed detection and precision farming technologies.