# Advancements in Weed Mapping: A Systematic Review

arXiv: https://arxiv.org/abs/2507.01269 · HTML: https://mlsanity.com/paper/2507.01269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 170 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Large Language Models in Plant Biology](https://mlsanity.com/paper/2401.02789/) (2024)
- [Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery](https://mlsanity.com/paper/2207.06673/) (2022)
- [Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm](https://mlsanity.com/paper/2410.09680/) (2024)
- [DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation](https://mlsanity.com/paper/2503.04006/) (2025)
- [Vision Transformers in Precision Agriculture: A Comprehensive Survey](https://mlsanity.com/paper/2504.21706/) (2025)
- [Designing and Implementing Data Warehouse for Agricultural Big Data](https://mlsanity.com/paper/1905.12411/) (2019)
- [Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges](https://mlsanity.com/paper/2308.06668/) (2023)
- [Precision Robotic Spot-Spraying: Reducing Herbicide Use and Enhancing Environmental Outcomes in Sugarcane](https://mlsanity.com/paper/2401.13931/) (2024)
- [FieldNet: Efficient Real-Time Shadow Removal for Enhanced Vision in Field Robotics](https://mlsanity.com/paper/2403.08142/) (2024)
- [WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification](https://mlsanity.com/paper/2505.18930/) (2025)
- [Site-specific weed management in corn using UAS imagery analysis and computer vision techniques](https://mlsanity.com/paper/2301.07519/) (2022)
- [Sugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review](https://mlsanity.com/paper/2404.16844/) (2024)
- [BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic Platform](https://mlsanity.com/paper/2405.09118/) (2024)
- [LMSeg: Unleashing the Power of Large-Scale Models for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2412.00364/) (2024)
- [A General-Purpose Data Harmonization Framework: Supporting Reproducible and Scalable Data Integration in the RADx Data Hub](https://mlsanity.com/paper/2503.02115/) (2025)

## Abstract (arXiv)

Weed mapping plays a critical role in precision management by providing accurate and timely data on weed distribution, enabling targeted control and reduced herbicide use. This minimizes environmental impacts, supports sustainable land management, and improves outcomes across agricultural and natural environments. Recent advances in weed mapping leverage ground-vehicle Red Green Blue (RGB) cameras, satellite and drone-based remote sensing combined with sensors such as spectral, Near Infra-Red (NIR), and thermal cameras. The resulting data are processed using advanced techniques including big data analytics and machine learning, significantly improving the spatial and temporal resolution of weed maps and enabling site-specific management decisions. Despite a growing body of research in this domain, there is a lack of comprehensive literature reviews specifically focused on weed mapping. In particular, the absence of a structured analysis spanning the entire mapping pipeline, from data acquisition to processing techniques and mapping tools, limits progress in the field. This review addresses these gaps by systematically examining state-of-the-art methods in data acquisition (sensor and platform technologies), data processing (including annotation and modelling), and mapping techniques (such as spatiotemporal analysis and decision support tools). Following PRISMA guidelines, we critically evaluate and synthesize key findings from the literature to provide a holistic understanding of the weed mapping landscape. This review serves as a foundational reference to guide future research and support the development of efficient, scalable, and sustainable weed management systems.