# OpenPlant: A Large-Scale Benchmark Dataset for Agricultural Plant Classification Using CNNs, ViTs, and VLMs

## TL;DR

OpenPlant is a large, diverse dataset of plant images designed to improve deep learning models for agricultural classification tasks.

## Contribution

Introduces OpenPlant, a large-scale, open dataset with 635,176 images across 1,167 plant species for benchmarking agricultural classification models.

## Key findings

- OpenPlant includes diverse plant growth stages, structures, and environmental conditions with verified annotations.
- The dataset was used to benchmark 10 CNNs, 6 ViTs, and 12 VLMs for agricultural plant classification.
- Results provide insights for improving deep learning models in precision agriculture.

## Abstract

Accurate plant classification based on deep learning is important for precision agriculture, such as weed control, crop monitoring, and smart farming systems. The accuracies of deep learning models rely on datasets. Although many datasets have been proposed in recent decades, they have the common limitations in terms of scale, less environmental diversity, and challenges of data integration. To solve these problems, in this paper, we introduce a new dataset named OpenPlant, which is a large-scale and open dataset containing 635,176 RGB images across 1167 plant species. OpenPlant includes diverse growth stages of plants, plant structures, and environmental conditions, and its annotations were carefully verified to ensure quality. The proposed OpenPlant can be a benchmark for agricultural plant classification. In this paper, we benchmarked 10 widely used convolutional neural networks (CNNs), 6 vision transformers (ViTs), and 12 vision–language models (VLMs) to provide a comprehensive evaluation. The OpenPlant dataset offers a comprehensive benchmark for agricultural research using deep learning and the results provide insights into future directions.