High-resolution drone imagery, ground-based sensor networks and weather data feed predictive models that turn multispectral pixels into plot-by-plot decisions: what to irrigate, what to treat, and what each field will actually yield.
The challenge
Processing terabytes of multispectral drone imagery in near real-time. Models that generalise across crop types, soil conditions and climate zones. The high variability inherent in agricultural systems. And an interface accessible to non-technical farm operators driving the sprayer, not a notebook.
The approach
A multi-modal architecture fuses satellite imagery, drone data, IoT soil sensors and weather forecasts into unified predictions. A custom computer-vision pipeline segments individual plant-health indicators, while a temporal graph neural network captures the spatial relationships between field zones. The system improves continuously through active learning from farmer feedback.
Adoption in the field, not accuracy in a notebook, is what carried it to a 3.5x first-season ROI.
The results
Measurable improvements across every pilot farm, with ROI averaging 3.5x within the first growing season.
- 22%
- Higher yields
- 30%
- Less water
- 45%
- Less pesticide
Crop yields rose 15–22%, water usage fell 30%, and pesticide application dropped 45% through targeted spot-treatment instead of blanket spraying.