Fully Funded PhD Scholarship: AI-Driven Soil Insight and Precision Management Using Weed Indicators – Leiden University & Wetsus, Netherlands
Quick Overview
Leiden University and Wetsus are offering a fully funded PhD position to develop an AI-driven framework linking weed species composition and functional traits to soil properties and agroecosystem outcomes. The project integrates high-resolution optical sensing, plant trait analysis, and soil measurements to develop a non-destructive framework for soil characterization and field heterogeneity mapping. This research supports precision agriculture by using weeds as practical bio-indicators of soil status.
Key Details
| Position | PhD Researcher |
| Project | From Weeds to Decisions: AI-driven Soil Insight and Precision Management Using Weed Indicators |
| Institution | Leiden University & Wetsus |
| Location | Netherlands |
| Qualification | MSc in Soil Sciences, Plant Sciences, Environmental Sciences, Geo-Information Sciences, Data Sciences, or related field |
| Duration | Standard PhD duration |
| Funding | Fully funded |
| Supervisors | Prof. Dr. Martijn Bezemer (Leiden), Dr. Jiahui Gu (Wetsus), Dr. Mohamed Zakaria Hatim (Wetsus) |
| Apply Via | Wetsus application webpage |
Project Overview
Weed plant communities in agricultural fields are rarely randomly distributed, and their species patterns often reflect underlying soil chemical, physical, and biological properties. This project explores the use of weeds as practical bio-indicators of soil status by integrating high-resolution optical sensing, plant trait analysis, and soil measurements.
Proximal sensing enables spatially explicit species recognition and extraction of plant functional traits. These plant-derived indicators can be linked with soil properties describing nutrient availability, structure, moisture dynamics, and microbiota. The core idea is that weed plant traits encode signals of soil constraints and ecological niches. Using AI-driven approaches, the project aims to develop a non-destructive framework for soil characterization and field heterogeneity mapping, supporting precision agriculture.
Research Challenges
Although ecological evidence links weed communities to soil conditions, predictive plant–soil inference in agricultural fields remains limited. Weed distributions are highly dynamic, influenced by crop rotation, management history, disturbance, and plant–soil feedbacks, creating complex nonlinear patterns where species presence alone cannot explain soil variability.
Key challenges include:
- Reliable weed detection in mixed canopies
- Separating soil signals from management effects
- Integrating multi-scale datasets
- Accounting for microbiome interactions affecting weed and crop performance
- Translating optical plant traits into biologically meaningful indicators for robust modelling
Many existing AI approaches function as black boxes, limiting practical adoption. This project addresses these gaps by developing transparent AI frameworks that integrate species recognition, trait extraction, and soil sensing to predict soil properties while ecologically explaining disease risks and abiotic or biotic stress patterns.
Key Responsibilities
- Develop an AI-enabled framework linking weed species composition and functional traits to soil properties and agroecosystem outcomes
- Extract quantitative plant traits from spectral and imaging data
- Analyse soil physicochemical and microbiome datasets
- Construct models that infer soil conditions from plant indicators
- Apply interpretable machine learning techniques to quantify feature importance
- Investigate plant–soil feedback processes
- Assess whether weed characteristics can forecast subsequent crop performance, disease risk, and abiotic or biotic stress
- Conduct interdisciplinary experimentation, field campaigns, data analysis, and predictive modelling
- Develop spatial decision-support tools enabling farmers to estimate soil variability through weed observations
Candidate Profile
Required:
- Master’s degree in Soil Sciences, Plant Sciences, Environmental Sciences, Geo-Information Sciences, Data Sciences, or a related discipline combining quantitative methods with ecological or agricultural sciences
- Experience with soil or vegetation field sampling
- Comfortable with data analysis and statistical modelling
- Independent and curious about ecological processes
- Enjoy fieldwork
- Motivated to work across disciplinary boundaries
Advantageous:
- Familiarity with machine learning methods
- GIS experience
- Spectral data analysis experience
- Driver’s license
- Ability to communicate in Dutch
Keywords
Proximal sensing; GIS; field spectroscopy; soil biology; plant traits; machine learning
What They Offer
| Benefit | Details |
|---|---|
| Position | PhD Researcher |
| Funding | Fully funded |
| Institutions | Leiden University & Wetsus |
| Research Environment | International and dynamic |
| Training | Interdisciplinary research across ecology, agriculture, and data science |
Application Tips
- Highlight your field sampling experience – Experience with soil or vegetation sampling is required.
- Showcase quantitative skills – Data analysis and statistical modelling experience is essential.
- Demonstrate machine learning knowledge – Familiarity with ML methods is advantageous.
- Emphasize interdisciplinary interest – The project combines ecology, agriculture, and data science.
- Submit a complete application in English – Applications must be complete and in English.
Apply Now
Apply via the Wetsus application webpage.
Application Deadline: 16 October 2026
Supervisors: Prof. Dr. Martijn Bezemer (Leiden University), Dr. Jiahui Gu and Dr. Mohamed Zakaria Hatim (Wetsus)
Project Partners: Soil
Frequently Asked Questions
- What is the project focus? AI-driven soil insight and precision management using weed indicators.
- What is the qualification requirement? Master’s in Soil Sciences, Plant Sciences, Environmental Sciences, Geo-Information Sciences, Data Sciences, or related field.
- Is fieldwork required? Yes, the position involves field campaigns and interdisciplinary experimentation.
- What skills are advantageous? Machine learning, GIS, and spectral data analysis.
- Is Dutch language required? Ability to communicate in Dutch is a plus, not mandatory.
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