# GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER

arXiv: https://arxiv.org/abs/2204.00294 · HTML: https://mlsanity.com/paper/2204.00294/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [Fruit recognition from images using deep learning](https://mlsanity.com/paper/1712.00580/) (2017)
- [Recurrent Instance Segmentation](https://mlsanity.com/paper/1511.08250/) (2015)
- [Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming](https://mlsanity.com/paper/1806.03412/) (2018)
- [Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.07789/) (2021)
- [Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks](https://mlsanity.com/paper/2105.10325/) (2021)
- [Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture](https://mlsanity.com/paper/2106.10118/) (2021)
- [Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1905.00458/) (2019)
- [Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision](https://mlsanity.com/paper/2201.02885/) (2022)
- [Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods](https://mlsanity.com/paper/2012.04555/) (2020)

## Built on by (most cited first, 15 of 15)

- [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)
- [Data-Centric Digital Agriculture: A Perspective](https://mlsanity.com/paper/2312.03437/) (2023)
- [BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions](https://mlsanity.com/paper/2312.14706/) (2023)
- [Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower](https://mlsanity.com/paper/2305.15149/) (2023)
- [A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics](https://mlsanity.com/paper/2407.13304/) (2024)
- [Zero-shot Hierarchical Plant Segmentation via Foundation Segmentation Models and Text-to-image Attention](https://mlsanity.com/paper/2509.09116/) (2025)
- [CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture](https://mlsanity.com/paper/2305.10084/) (2023)
- [Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks](https://mlsanity.com/paper/2312.03443/) (2023)
- [FOLIAGE: Towards Physical Intelligence World Models Via Unbounded Surface Evolution](https://mlsanity.com/paper/2506.03173/) (2025)
- [AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot](https://mlsanity.com/paper/2508.18694/) (2025)
- [Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning](https://mlsanity.com/paper/2401.17985/) (2024)
- [3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different Sensors](https://mlsanity.com/paper/2503.13188/) (2025)
- [ReLeaf: Benchmarking Leaf Segmentation across Domains and Species](https://mlsanity.com/paper/2605.03784/) (2026)
- [AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining](https://mlsanity.com/paper/2608.07984/) (2026)

## Abstract (arXiv)

This article presents GrowliFlower, a georeferenced, image-based UAV time series dataset of two monitored cauliflower fields of size 0.39 and 0.60 ha acquired in 2020 and 2021. The dataset contains RGB and multispectral orthophotos from which about 14,000 individual plant coordinates are derived and provided. The coordinates enable the dataset users the extraction of complete and incomplete time series of image patches showing individual plants. The dataset contains collected phenotypic traits of 740 plants, including the developmental stage as well as plant and cauliflower size. As the harvestable product is completely covered by leaves, plant IDs and coordinates are provided to extract image pairs of plants pre and post defoliation, to facilitate estimations of cauliflower head size. Moreover, the dataset contains pixel-accurate leaf and plant instance segmentations, as well as stem annotations to address tasks like classification, detection, segmentation, instance segmentation, and similar computer vision tasks. The dataset aims to foster the development and evaluation of machine learning approaches. It specifically focuses on the analysis of growth and development of cauliflower and the derivation of phenotypic traits to foster the development of automation in agriculture. Two baseline results of instance segmentation at plant and leaf level based on the labeled instance segmentation data are presented. The entire data set is publicly available.