# WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming

arXiv: https://arxiv.org/abs/1808.00100 · HTML: https://mlsanity.com/paper/1808.00100/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

## Built on by (most cited first, 22 of 22)

- [PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain](https://mlsanity.com/paper/2306.04557/) (2023)
- [A Survey of Deep Learning Techniques for Weed Detection from Images](https://mlsanity.com/paper/2103.01415/) (2021)
- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques](https://mlsanity.com/paper/2203.09091/) (2022)
- [Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images](https://mlsanity.com/paper/2109.02716/) (2021)
- [BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions](https://mlsanity.com/paper/2312.14706/) (2023)
- [WeedsGalore: A Multispectral and Multitemporal UAV-based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields](https://mlsanity.com/paper/2502.13103/) (2025)
- [A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics](https://mlsanity.com/paper/2407.13304/) (2024)
- [Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2108.01884/) (2021)
- [Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands](https://mlsanity.com/paper/2502.08678/) (2025)
- [The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics](https://mlsanity.com/paper/2508.21635/) (2025)
- [BAWSeg: A UAV Multispectral Benchmark for Barley Weed Segmentation](https://mlsanity.com/paper/2603.01932/) (2026)
- [A Parameter-efficient Convolutional Approach for Camouflaged Weed Detection in Multispectral Aerial Imagery](https://mlsanity.com/paper/2603.06655/) (2026)
- [Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming](https://mlsanity.com/paper/2210.11545/) (2022)
- [Precision Agriculture: Crop Mapping using Machine Learning and Sentinel-2 Satellite Imagery](https://mlsanity.com/paper/2403.09651/) (2023)
- [RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection](https://mlsanity.com/paper/2410.04983/) (2024)
- [Discrete Gaussian Process Representations for Optimising UAV-based Precision Weed Mapping](https://mlsanity.com/paper/2503.07210/) (2025)
- [Lightweight Multispectral Crop-Weed Segmentation for Precision Agriculture](https://mlsanity.com/paper/2505.07444/) (2025)
- [DistillFSS: Synthesizing Few-Shot Knowledge into a Lightweight Segmentation Model](https://mlsanity.com/paper/2512.05613/) (2025)
- [AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture](https://mlsanity.com/paper/2605.22366/) (2026)
- [Self-supervised training for high-resolution close-range multispectral remote sensing imagery](https://mlsanity.com/paper/2607.11366/) (2026)

## Abstract (arXiv)

We present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.