Apollo
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Superintelligent vision for the growing world
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Stress shows on a single leaf days before it shows across a field. By the time discoloration is visible from the road, the yield is already paying for it.
Agriculture's answer has been more hardware: multispectral rigs, fixed sensors, satellite contracts. Precision farming exists, but only for the operations that can afford it.
Apollo takes the opposite path. We build the intelligence layer, not the sensor. Our models read the imagery farms already produce, from a phone at the end of a row to a drone over a thousand hectares, and return a diagnosis in seconds.
We're not adding equipment to the farm. We're teaching the cameras it already has to diagnose.
Row-level stress mapping from standard imagery
Diagnosis in the field, from a phone. A photo returns the condition, a confidence score, and what to do next, delivered on the web and through Telegram in the languages farmers actually speak.
Standard drone imagery becomes a row-level stress map. Flag the exact plants that need attention weeks before symptoms are visible from the ground, on hardware farms already fly.
Diagnostics as infrastructure. Scan history, alerts, and an API that plugs crop intelligence into lending, insurance, and procurement decisions across the supply chain.
Trained on the PlantVillage corpus: 38 disease and stress conditions across the crops that feed the region.
A ResNet-18 classifier tuned for leaf pathology, exported to ONNX and served in milliseconds on commodity hardware.
A language model turns raw probabilities into plain guidance: what the condition is, how it spreads, what to apply.
The same engine behind every surface, so a smallholder's phone and an agribusiness dashboard see the same truth.
40%
of global crops are lost to pests and disease each year
$220B
in annual economic losses worldwide
72h
typical delay of manual scouting behind the plant
A field agronomist in every pocket, at the cost of a photo.
Fleet-scale monitoring across estates, with row-level precision.
Objective crop-health evidence for lending, claims, and risk models.
Early regional outbreak signals, built from ground truth instead of satellites alone.
A ResNet-18 classifier trained on the PlantVillage dataset: more than 70,000 leaf images across 38 conditions.
Apollo won the AI500 Hackathon hosted by Agrobank, validated by the largest agricultural bank in Uzbekistan.
Multi-crop coverage, scan history, and a mobile app built for the people who walk the rows.
Autonomous drone passes, live monitoring, and a public API for the wider industry.