Digital Crop Science
Building models, simulations, and ML systems for plant-soil problems.
M.Sc. Crop Science with a digital focus. I combine domain expertise in plant physiology with modern software engineering to solve agricultural challenges.
What I Do
Three interconnected areas of expertise that I bring to every project.
ML & Computer Vision
Deep learning pipelines for plant phenotyping, image segmentation, and classification systems.
Modeling & Simulation
Mathematical models and simulations for soil-plant systems, rhizosphere dynamics, and growth prediction.
Research Engineering
Reproducible pipelines, CI/CD for research, containerization, and deployment of scientific tools.
Featured Work
Selected projects showcasing my approach to solving complex problems.
GPU-Accelerated Synthetic Data Generation for Crop Phenotyping
Physics-based sensor simulation using C++ ray-tracing to generate perfectly labeled training data. Dual virtual sensors (LiDAR + multispectral camera) provide structural point clouds and pixel-perfect segmentation masks for bean-wheat intercropping ML research.
Mechanistic Modeling of AMPA Persistence Across Soil Types
Meta-analysis using TESFO mechanistic modeling to evaluate glyphosate metabolite (AMPA) behavior across four soil types. Identified critical mobility-persistence trade-off requiring site-specific agricultural risk management.
GrowController: IoT Environmental Monitor
ESP32-based CO2, temperature, and humidity monitor with VPD calculation and Prometheus/Grafana integration.
Rapid Soil Water & Nitrogen Prediction via NIR Spectroscopy and ML
Non-invasive dual-model ML pipeline delivering soil predictions in <1 minute vs. traditional 24-48 hour lab assays. Combines SVR for water content (R²=0.844, MAE=1.55%) and Random Forest for nitrogen classification (98.1% balanced accuracy). Breakthrough: PCA reduced features 90% while increasing accuracy from 88.1% to 98.1%. Production-ready with OOD detection and confidence thresholding. Edge-optimized at 1.8 MB.
Nitrogen Loss Risk Spatial Analysis
Nationwide spatial risk mapping of German organic farms using Local Moran's I and Getis-Ord Gi* hotspot analysis. Cleaned 79,125 farm records, implemented IDW and Ordinary Kriging interpolation, and proved statistically significant clustering (p<0.05) linking sandy soils to high-risk zones.
Multispectral UAV Crop Classification Pipeline
End-to-end ML pipeline for temporal crop classification using 10-band UAV orthophotos. Built rigorous spatial zone-based data splits to prevent leakage, engineered 220 spectral features, and solved overfitting through PCA dimensionality reduction—achieving 0.96 F1-score with just 9 components.
How I Work
Scientific Rigor
Every model is grounded in domain knowledge and validated against real data.
Reproducibility
Version-controlled code, containerized environments, and documented workflows.
Clean Engineering
Type-safe code, tested pipelines, and maintainable architectures.
Collaboration
Clear documentation, modular design, and open communication.
Let’s Work Together
I’m currently seeking opportunities in AgTech, biotech, or research institutions. Whether it’s a PhD position or an industry role, I’m ready to contribute.