Independent geospatial research · Open source
Make the assumptions as traceable as the results.
Repeatable agricultural suitability analysis with explicit provenance.
AI-generated editorial artwork; not a project screenshot or research result.
01 / Context
The problem worth solving.
Agricultural suitability analysis is difficult to trust when preprocessing, assumptions, validation choices, and data lineage are not captured consistently.
02 / My contribution
Where I made a difference.
I created a configuration-driven geospatial workflow for agricultural suitability analysis. Deterministic fingerprints and provenance manifests capture how an experiment was run; spatial cross-validation and sensitivity analysis help evaluate the effect of modeling and validation choices.
03 / System design
Connecting the pieces.
- 01Configuration & spatial inputs
- 02Fingerprint & provenance
- 03Suitability analysis
- 04Spatial validation & sensitivity
04 / Engineering tradeoffs
The decisions behind the system.
Configuration over one-off scripts
Explicit configuration makes inputs and assumptions reviewable across runs. It adds structure up front so experiments can be repeated when datasets or regions change.
Validation that respects geography
Nearby observations can share spatial structure. Spatial cross-validation makes geographic separation an explicit part of evaluation instead of relying only on a random split.
An output is only part of the record
Fingerprints and manifests preserve lineage alongside results. Sensitivity analysis exposes dependence on assumptions; it does not establish universal suitability or production readiness.
05 / Outcome & evidence
Work you can evaluate.
The project makes agricultural modeling experiments traceable, repeatable, and easier to evaluate across changing datasets and regions.
An independent research workflow, not a Bayer production system. Source code is the primary evidence; no unverified benchmark or deployment claim is implied.