# Deep Leaf Segmentation Using Synthetic Data

arXiv: https://arxiv.org/abs/1807.10931 · HTML: https://mlsanity.com/paper/1807.10931/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2018.

## Built on by (most cited first, 19 of 19)

- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [Self-Supervised Leaf Segmentation under Complex Lighting Conditions](https://mlsanity.com/paper/2203.15943/) (2022)
- [Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"](https://mlsanity.com/paper/2307.06322/) (2023)
- [Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case](https://mlsanity.com/paper/2102.12295/) (2021)
- [Unsupervised Pre-Training for 3D Leaf Instance Segmentation](https://mlsanity.com/paper/2401.08720/) (2024)
- [Enhanced Droplet Analysis Using Generative Adversarial Networks](https://mlsanity.com/paper/2402.15909/) (2024)
- [GMT: Guided Mask Transformer for Leaf Instance Segmentation](https://mlsanity.com/paper/2406.17109/) (2024)
- [Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review](https://mlsanity.com/paper/2104.08732/) (2021)
- [LeafMask: Towards Greater Accuracy on Leaf Segmentation](https://mlsanity.com/paper/2108.03568/) (2021)
- [Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing](https://mlsanity.com/paper/2210.07466/) (2022)
- [LAESI: Leaf Area Estimation with Synthetic Imagery](https://mlsanity.com/paper/2404.00593/) (2024)
- [VibrantLeaves: A principled parametric image generator for training deep restoration models](https://mlsanity.com/paper/2504.10201/) (2025)
- [DeepSeagrass Dataset](https://mlsanity.com/paper/2103.05226/) (2021)
- [Seed Classification using Synthetic Image Datasets Generated from Low-Altitude UAV Imagery](https://mlsanity.com/paper/2110.02846/) (2021)
- [AGP: A Novel Arabidopsis thaliana Genomics-Phenomics Dataset and its HyperGraph Baseline Benchmarking](https://mlsanity.com/paper/2508.14934/) (2025)
- [ST-DETrack: Identity-Preserving Branch Tracking in Entangled Plant Canopies via Dual Spatiotemporal Evidence](https://mlsanity.com/paper/2512.15445/) (2025)
- [GRAFT: Biological Graph and Hypergraph Benchmarks for Linked Gene Expression and Phenotypic Trait Prediction in Arabidopsis thaliana](https://mlsanity.com/paper/2606.27413/) (2026)
- [Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data](https://mlsanity.com/paper/2607.18576/) (2026)

## Abstract (arXiv)

Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping. Applying state-of-the-art machine learning approaches to tackle leaf instance segmentation requires a large amount of manually annotated training data. Currently, the benchmark datasets for leaf segmentation contain only a few hundred labeled training images. In this paper, we propose a framework for leaf instance segmentation by augmenting real plant datasets with generated synthetic images of plants inspired by domain randomisation. We train a state-of-the-art deep learning segmentation architecture (Mask-RCNN) with a combination of real and synthetic images of Arabidopsis plants. Our proposed approach achieves 90% leaf segmentation score on the A1 test set outperforming the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge (LSC). Our approach also achieves 81% mean performance over all five test datasets.