The world is estimated to be home to over 300,000 species of vascular plants. In the face of the ongoing biodiversity crisis, expanding our understanding of these species is crucial for the advancement of human civilization, encompassing areas such as agriculture, construction, and pharmacopoeia. However, the labor-intensive process of plant identification undertaken by human experts poses a significant obstacle to the accumulation of new data and knowledge. Fortunately, recent advancements in automatic identification, particularly through the application of deep learning techniques, have shown promising progress. Despite challenges posed by data-related issues such as a vast number of classes, imbalanced class distribution, erroneous identifications, duplications, variable visual quality, and diverse visual contents (such as photos or herbarium sheets), deep learning approaches have reached a level of maturity which gives us hope that in the near future we will have an identification system capable of accurately identifying all plant species worldwide. The PlantCLEF2023 challenge aims to contribute to this pursuit by addressing a multi-image (and metadata) classification problem involving an extensive set of classes (80,000 plant species). This paper provides an overview of the challenge's resources and evaluations, summarizes the methods and systems employed by participating research groups, and presents an analysis of key findings.