# ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

arXiv: https://arxiv.org/abs/1709.00938 · HTML: https://mlsanity.com/paper/1709.00938/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2017.

## Built on by (most cited first, 11 of 11)

- [SinGAN-Seg: Synthetic training data generation for medical image segmentation](https://mlsanity.com/paper/2107.00471/) (2021)
- [Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.07789/) (2021)
- [Self-Supervised Leaf Segmentation under Complex Lighting Conditions](https://mlsanity.com/paper/2203.15943/) (2022)
- [DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot](https://mlsanity.com/paper/2310.12787/) (2023)
- [Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review](https://mlsanity.com/paper/2204.04707/) (2022)
- [Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case](https://mlsanity.com/paper/2102.12295/) (2021)
- [Enhanced Droplet Analysis Using Generative Adversarial Networks](https://mlsanity.com/paper/2402.15909/) (2024)
- [Multi-Class Multi-Instance Count Conditioned Adversarial Image Generation](https://mlsanity.com/paper/2103.16795/) (2021)
- [Inside Out: Transforming Images of Lab-Grown Plants for Machine Learning Applications in Agriculture](https://mlsanity.com/paper/2211.02972/) (2022)
- [Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming](https://mlsanity.com/paper/2407.14119/) (2024)
- [Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery](https://mlsanity.com/paper/2502.18320/) (2025)

## Abstract (arXiv)

In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128x128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.