  Image classification is a crucial task in modern weed management and crop
intervention technologies. However, the limited size, diversity, and balance of
existing weed datasets hinder the development of deep learning models for
generalizable weed identification. In addition, the expensive labelling
requirements of mainstream fully-supervised weed classifiers make them cost-
and time-prohibitive to deploy widely, for new weed species, and in
site-specific weed management. This paper proposes a novel method for Weed
Contrastive Learning through visual Representations (WeedCLR), that uses
class-optimized loss with Von Neumann Entropy of deep representation for weed
classification in long-tailed datasets. WeedCLR leverages self-supervised
learning to learn rich and robust visual features without any labels and
applies a class-optimized loss function to address the class imbalance problem
in long-tailed datasets. WeedCLR is evaluated on two public weed datasets:
CottonWeedID15, containing 15 weed species, and DeepWeeds, containing 8 weed
species. WeedCLR achieves an average accuracy improvement of 4.3\% on
CottonWeedID15 and 5.6\% on DeepWeeds over previous methods. It also
demonstrates better generalization ability and robustness to different
environmental conditions than existing methods without the need for expensive
and time-consuming human annotations. These significant improvements make
WeedCLR an effective tool for weed classification in long-tailed datasets and
allows for more rapid and widespread deployment of site-specific weed
management and crop intervention technologies.
