Graph weeds net: A graph-based deep learning method for weed recognition

Abstract

Robotic weed control through weed detection has become increasingly important due to mounting pressure on herbicides from resistance and the large impact of weeds on agricultural productivity. One of the major challenges is accurate classification of weed species for selective targeting in crop situations, whilst the existing studies are often conducted in well-controlled settings with consistent lighting, species and backgrounds. Therefore, in this study, we propose a novel graph-based deep learning architecture, namely Graph Weeds Net (GWN), which aims to recognize multiple types of weeds from conventional RGB images collected from complex rangelands. GWN collects regional patterns in line with set image scopes and formulates multi-scale graph representations for weed classification. Additionally, GWN provides suggestions for key regions, creating opportunities for further within-image actions for robotic in-field systems. The architecture was evaluated on a recently published benchmark dataset, achieving the state-of-the-art performance with a top-1 accuracy 98.1%.