Services / Crop classification, disease, and nutrient prediction
Hyperspectral Data Analysis
Hyperspectral sensors read hundreds of narrow bands where a camera reads three. We turn those spectra into agronomic answers: what is growing, where disease is starting, and which nutrients are short. One workflow spans handheld spectrometers, airborne cubes, and open satellite hyperspectral missions.
Capabilities
What the work involves
Crop prediction and classification
Spectral signatures separate crop types and growth stages across a season. Classifiers built with PCA feature extraction, support vector machines, and ensemble methods.
Crop disease detection
Narrow-band indices and full-spectrum classifiers flag disease response before it is visible to scouting, enabling targeted intervention.
Nutrient status prediction
Visible-near-infrared spectra predict nitrogen and nutrient deficiency at leaf and canopy scale, separating nutrient stress from water stress.
Sensor-agnostic pipelines
One workflow from field spectrometers to airborne cubes and open hyperspectral missions such as EnMAP and PRISMA: calibration, preprocessing, model, map.
Tooling
- Vis-NIR spectroscopy
- EnMAP
- PRISMA
- PCA
- SVM and ensembles
- Python
Deliverables
What you receive
- Trained classification or prediction model with documented error bounds
- Field-scale prediction maps as GIS-ready layers
- Preprocessing and calibration pipeline, handed over with documentation
- Methods report suitable for regulatory or funding review
Where it applies
Markets served by this service
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