Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection

arXiv:2403.03390v1 Announce Type: cross
Abstract: Effective weed control plays a crucial role in optimizing crop yield and enhancing agricultural product quality. However, the reliance on herbicide application not only poses a critical threat to the environment but also promotes the emergence of resistant weeds. Fortunately, recent advances in precision weed management enabled by ML and DL provide a sustainable alternative. Despite great progress, existing algorithms are mainly developed based on supervised learning approaches, which typically demand large-scale datasets with manual-labeled …

abstract advances application arxiv class control cs.cv cs.lg detection eess.iv emergence environment evaluation frameworks however performance precision product quality reliance role semi-supervised semi-supervised learning supervised learning the environment threat type weeds