# A survey of datasets for computer vision in agriculture

arXiv: https://arxiv.org/abs/2502.16950 · HTML: https://mlsanity.com/paper/2502.16950/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 69 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Self-Supervised Pretraining Improves Self-Supervised Pretraining](https://mlsanity.com/paper/2103.12718/) (2021)
- [DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning](https://mlsanity.com/paper/1810.05726/) (2018)
- [Comprehensive Exploration of Synthetic Data Generation: A Survey](https://mlsanity.com/paper/2401.02524/) (2024)
- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [Semi-Supervised and Unsupervised Deep Visual Learning: A Survey](https://mlsanity.com/paper/2208.11296/) (2022)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain](https://mlsanity.com/paper/2306.04557/) (2023)
- [FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework](https://mlsanity.com/paper/2408.06190/) (2024)
- [A Public Image Database for Benchmark of Plant Seedling Classification Algorithms](https://mlsanity.com/paper/1711.05458/) (2017)
- [Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems](https://mlsanity.com/paper/2110.04960/) (2021)
- [SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images](https://mlsanity.com/paper/2310.10861/) (2023)
- [Crop identification using deep learning on LUCAS crop cover photos](https://mlsanity.com/paper/2305.04994/) (2023)
- [Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking](https://mlsanity.com/paper/2205.11108/) (2022)
- [Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein Instances](https://mlsanity.com/paper/2312.08805/) (2023)
- [A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields](https://mlsanity.com/paper/2405.04305/) (2024)
- [Public Computer Vision Datasets for Precision Livestock Farming: A Systematic Survey](https://mlsanity.com/paper/2406.10628/) (2024)

## Built on by (most cited first, 5 of 5)

- [PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach](https://mlsanity.com/paper/2505.01823/) (2025)
- [AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot](https://mlsanity.com/paper/2508.18694/) (2025)
- [Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN](https://mlsanity.com/paper/2509.15181/) (2025)
- [AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards](https://mlsanity.com/paper/2505.14029/) (2025)
- [Sesame Plant Segmentation Dataset: A YOLO Formatted Annotated Dataset](https://mlsanity.com/paper/2601.07970/) (2026)

## Abstract (arXiv)

In agricultural research, there has been a recent surge in the amount of Computer Vision (CV) focused work. But unlike general CV research, large high-quality public datasets are sparsely available. This can be partially attributed to the high variability between different agricultural tasks, crops and environments as well as the complexity of data collection, but it is also influenced by the reticence to publish datasets by many authors. This, as well as the lack of a widely used agricultural data repository, are impactful factors that hinder research in applied CV for agriculture as well as the usage of agricultural data in general-purpose CV research. In this survey, we provide a large number of high-quality datasets of images taken on fields. Overall, we find 45 datasets, which are listed in this paper as well as in an online catalog on the project website: https://smartfarminglab.github.io/field_dataset_survey/.