# Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein Instances

arXiv: https://arxiv.org/abs/2312.08805 · HTML: https://mlsanity.com/paper/2312.08805/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [CenterMask: single shot instance segmentation with point representation](https://mlsanity.com/paper/2004.04446/) (2020)
- [PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain](https://mlsanity.com/paper/2306.04557/) (2023)
- [Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain](https://mlsanity.com/paper/2210.07879/) (2022)
- [Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming](https://mlsanity.com/paper/1806.03413/) (2018)

## Built on by (most cited first, 4 of 4)

- [A survey of datasets for computer vision in agriculture](https://mlsanity.com/paper/2502.16950/) (2025)
- [From Web Data to Real Fields: Low-Cost Unsupervised Domain Adaptation for Agricultural Robots](https://mlsanity.com/paper/2410.23906/) (2024)
- [3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different Sensors](https://mlsanity.com/paper/2503.13188/) (2025)
- [AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining](https://mlsanity.com/paper/2608.07984/) (2026)

## Abstract (arXiv)

Robot perception is far from what humans are capable of. Humans do not only have a complex semantic scene understanding but also extract fine-grained intra-object properties for the salient ones. When humans look at plants, they naturally perceive the plant architecture with its individual leaves and branching system. In this work, we want to advance the granularity in plant understanding for agricultural precision robots. We develop a model to extract fine-grained phenotypic information, such as leaf-, stem-, and vein instances. The underlying dataset RumexLeaves is made publicly available and is the first of its kind with keypoint-guided polyline annotations leading along the line from the lowest stem point along the leaf basal to the leaf apex. Furthermore, we introduce an adapted metric POKS complying with the concept of keypoint-guided polylines. In our experimental evaluation, we provide baseline results for our newly introduced dataset while showcasing the benefits of POKS over OKS.