<p>Accurate classification of weed seedlings is a key challenge to address in order to advance precision weed management and reduce pesticide use. In this study, an original image dataset, the Weed Phenological Dataset (WPD), with annotated early growth stages is introduced. Furthermore, a novel deep learning taxonomic loss function for few-shot learning was evaluated. Using hierarchical structures to direct the classification process, this taxonomic loss function introduces dynamic margins during the computation. In experiments using the taxonomic loss using the ResNet-50 architectures across different plant image datasets, this taxonomic approach allowed better clustering according to the Silhouette scores when compared with triplet loss using 100 images per class. It also led to better identification of weed seedlings at early growth stages. Although the new taxonomic loss did not consistency improve classification results for all the deep learning model architectures, it open new research avenues for the robotization of agriculture.</p>