  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 annotations, which is time-consuming and
labor-intensive. As such, label-efficient learning methods, especially
semi-supervised learning, have gained increased attention in the broader domain
of computer vision and have demonstrated promising performance. These methods
aim to utilize a small number of labeled data samples along with a great number
of unlabeled samples to develop high-performing models comparable to the
supervised learning counterpart trained on a large amount of labeled data
samples. In this study, we assess the effectiveness of a semi-supervised
learning framework for multi-class weed detection, employing two well-known
object detection frameworks, namely FCOS and Faster-RCNN. Specifically, we
evaluate a generalized student-teacher framework with an improved pseudo-label
generation module to produce reliable pseudo-labels for the unlabeled data. To
enhance generalization, an ensemble student network is employed to facilitate
the training process. Experimental results show that the proposed approach is
able to achieve approximately 76\% and 96\% detection accuracy as the
supervised methods with only 10\% of labeled data in CottenWeedDet3 and
CottonWeedDet12, respectively. We offer access to the source code, contributing
a valuable resource for ongoing semi-supervised learning research in weed
detection and beyond.
