Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Species

2026-03-22

AI Summary

This work addresses the challenge of visual counting in highly diverse plant contexts, where existing methods struggle with morphological variation, growth stages, and environmental conditions, and lack fine-grained benchmarks grounded in botanical taxonomy. We present TPC-268, the first plant counting dataset that integrates the Linnaean taxonomic hierarchy (from kingdom to species) with multi-scale observations spanning remote-sensing canopy views to microscopic tissue images. The dataset encompasses two kingdoms—Plantae and Fungi—with 268 countable categories across 242 species, comprising 10,000 images and 678,050 instance point annotations. Built upon the class-agnostic counting (CAC) paradigm, TPC-268 features taxonomy-consistent and scale-aware data splits, enabling standardized evaluation of state-of-the-art CAC methods and supporting hierarchical reasoning and species-aware assessment in fine-grained biological visual counting.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multi-instance/Multi-view Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Visually cataloging and quantifying the natural world requires pushing the boundaries of both detailed visual classification and counting at scale. Despite significant progress, particularly in crowd and traffic analysis, the fine-grained, taxonomy-aware plant counting remains underexplored in vision. In contrast to crowds, plants exhibit nonrigid morphologies and physical appearance variations across growth stages and environments. To fill this gap, we present TPC-268, the first plant counting benchmark incorporating plant taxonomy. Our dataset couples instance-level point annotations with Linnaean labels (kingdom -> species) and organ categories, enabling hierarchical reasoning and species-aware evaluation. The dataset features 10,000 images with 678,050 point annotations, includes 268 countable plant categories over 242 plant species in Plantae and Fungi, and spans observation scales from canopy-level remote sensing imagery to tissue-level microscopy. We follow the problem setting of class-agnostic counting (CAC), provide taxonomy-consistent, scale-aware data splits, and benchmark state-of-the-art regression- and detection-based CAC approaches. By capturing the biodiversity, hierarchical structure, and multi-scale nature of botanical and mycological taxa, TPC-268 provides a biologically grounded testbed to advance fine-grained class-agnostic counting. Dataset and code are available at https://github.com/tiny-smart/TPC-268.

Problem

• fine-grained counting
• plant taxonomy
• class-agnostic counting
• biodiversity
• multi-scale plant counting

Innovation

• fine-grained counting
• plant taxonomy
• class-agnostic counting
• multi-scale dataset
• hierarchical labeling