# Generating Diverse Agricultural Data for Vision-Based Farming Applications

arXiv: https://arxiv.org/abs/2403.18351 · HTML: https://mlsanity.com/paper/2403.18351/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Infinite Photorealistic Worlds using Procedural Generation](https://mlsanity.com/paper/2306.09310/) (2023)
- [Synthetic Data for Deep Learning](https://mlsanity.com/paper/1909.11512/) (2019)
- [TartanVO: A Generalizable Learning-based VO](https://mlsanity.com/paper/2011.00359/) (2020)
- [Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing](https://mlsanity.com/paper/1810.08705/) (2018)
- [Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation](https://mlsanity.com/paper/1804.06534/) (2018)
- [DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning](https://mlsanity.com/paper/1810.05726/) (2018)
- [Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis](https://mlsanity.com/paper/2001.01306/) (2020)
- [Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks](https://mlsanity.com/paper/1612.07429/) (2016)
- [Synthetic data, real errors: how (not) to publish and use synthetic data](https://mlsanity.com/paper/2305.09235/) (2023)
- [Synthetic-to-Real Domain Adaptation using Contrastive Unpaired Translation](https://mlsanity.com/paper/2203.09454/) (2022)
- [Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection](https://mlsanity.com/paper/2108.13344/) (2021)
- [Synthetic Image Data for Deep Learning](https://mlsanity.com/paper/2212.06232/) (2022)

## Built on by (most cited first, 7 of 7)

- [AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark](https://mlsanity.com/paper/2504.10568/) (2025)
- [PlantDreamer: Achieving Realistic 3D Plant Models with Diffusion-Guided Gaussian Splatting](https://mlsanity.com/paper/2505.15528/) (2025)
- [Auditing Metrics for Synthetic Data Utility: A Cross-Domain Study of Appearance and Geometry](https://mlsanity.com/paper/2605.22467/) (2026)
- [LLMs for Enhanced Agricultural Meteorological Recommendations](https://mlsanity.com/paper/2408.04640/) (2024)
- [Enabling Plant Phenotyping in Weedy Environments using Multi-Modal Imagery via Synthetic and Generated Training Data](https://mlsanity.com/paper/2509.19208/) (2025)
- [Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data](https://mlsanity.com/paper/2607.18576/) (2026)
- [SynGallery: A Synthetic Gallery of Real Paintings for Instance-Level Artwork Recognition](https://mlsanity.com/paper/2607.18907/) (2026)

## Abstract (arXiv)

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture, such as semantic segmentation for autonomous weed control. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.