# WeedsGalore: A Multispectral and Multitemporal UAV-based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields

arXiv: https://arxiv.org/abs/2502.13103 · HTML: https://mlsanity.com/paper/2502.13103/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 72 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Deep Deterministic Uncertainty: A Simple Baseline](https://mlsanity.com/paper/2102.11582/) (2021)
- [Evaluating Bayesian Deep Learning Methods for Semantic Segmentation](https://mlsanity.com/paper/1811.12709/) (2018)
- [On the Practicality of Deterministic Epistemic Uncertainty](https://mlsanity.com/paper/2107.00649/) (2021)
- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain](https://mlsanity.com/paper/2306.04557/) (2023)
- [weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming](https://mlsanity.com/paper/1709.03329/) (2017)
- [WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming](https://mlsanity.com/paper/1808.00100/) (2018)
- [Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation](https://mlsanity.com/paper/2008.07588/) (2020)
- [Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving](https://mlsanity.com/paper/2105.13688/) (2021)
- [BonnBot-I: A Precise Weed Management and Crop Monitoring Platform](https://mlsanity.com/paper/2307.12588/) (2023)
- [Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation](https://mlsanity.com/paper/2404.02580/) (2024)
- [Uncertainty in Real-Time Semantic Segmentation on Embedded Systems](https://mlsanity.com/paper/2301.01201/) (2022)

## Built on by (most cited first, 4 of 4)

- [Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance](https://mlsanity.com/paper/2603.29941/) (2026)
- [Lightweight Multispectral Crop-Weed Segmentation for Precision Agriculture](https://mlsanity.com/paper/2505.07444/) (2025)
- [ReLeaf: Benchmarking Leaf Segmentation across Domains and Species](https://mlsanity.com/paper/2605.03784/) (2026)
- [EgoMaize: A First-Person Maize Instance Segmentation Benchmark under Severe Field Occlusion](https://mlsanity.com/paper/2609.12350/) (2026)

## Abstract (arXiv)

Weeds are one of the major reasons for crop yield loss but current weeding practices fail to manage weeds in an efficient and targeted manner. Effective weed management is especially important for crops with high worldwide production such as maize, to maximize crop yield for meeting increasing global demands. Advances in near-sensing and computer vision enable the development of new tools for weed management. Specifically, state-of-the-art segmentation models, coupled with novel sensing technologies, can facilitate timely and accurate weeding and monitoring systems. However, learning-based approaches require annotated data and show a lack of generalization to aerial imaging for different crops. We present a novel dataset for semantic and instance segmentation of crops and weeds in agricultural maize fields. The multispectral UAV-based dataset contains images with RGB, red-edge, and near-infrared bands, a large number of plant instances, dense annotations for maize and four weed classes, and is multitemporal. We provide extensive baseline results for both tasks, including probabilistic methods to quantify prediction uncertainty, improve model calibration, and demonstrate the approach's applicability to out-of-distribution data. The results show the effectiveness of the two additional bands compared to RGB only, and better performance in our target domain than models trained on existing datasets. We hope our dataset advances research on methods and operational systems for fine-grained weed identification, enhancing the robustness and applicability of UAV-based weed management. The dataset and code are available at https://github.com/GFZ/weedsgalore