Satellite and Ground Sensor Fusion for Crop Disease Early Warning
A 340-farmer pilot with no structured proof of impact became a documented 91% renewal traction story, three revenue channels, and a term sheet at the third post-reframe investor meeting.
- Origin
- ICAR remote sensing research group
- Founded
- 2022
- Core Innovation
- Synthetic aperture radar, multispectral satellite, and IoT ground sensor fusion; 87% disease detection accuracy at 72-hour advance notice. Pilot scale: 340 farmers across two states; 34% average crop loss reduction documented.
- Capital Outcome
- ₹6.80 Crore seed equity from an AgriTech-focused early-stage fund
Diagnostic findings at engagement
- Pitch materials opened with technical model architecture description before establishing the underlying farmer problem, a sequencing identified during pitch rehearsal as causing audience disengagement within the first two minutes.
- The pricing model charged farmers ₹500 per acre per season as the sole revenue channel, with no analysis of farmer willingness to pay or of alternative payment structures such as insurance partnership or input-company bundling.
- Market sizing referenced a 200 million farmer addressable population with no segmentation, prioritisation, or geographic specificity.
- Pilot results from the 340-farmer deployment existed in raw form only: no structured testimonials, no before-and-after financial impact data per farmer, and no documented renewal or satisfaction metric.
- Two prior venture conversations had expressed interest but stalled, with both investors specifically requesting evidence of farmer willingness to pay before proceeding.
Service interventions applied
Traction Documentation
Converted the 340-farmer pilot into a structured traction document quantifying an average of ₹8,400 in crop loss averted per acre, a ₹2,100 reduction in pesticide expenditure, and a 91% renewal rate, supported by anonymised farmer case studies and photographic documentation.
Revenue Model Restructuring
Retained direct farmer pricing as a tertiary channel and constructed two primary B2B channels: an early-warning data feed licensed to crop insurance providers, projected to reduce their claims incidence by 18–22%, and a bundled advisory product sold through agricultural input companies.
Market Research and Segmentation
Replaced the undifferentiated 200 million farmer figure with a defined beachhead of 2.3 million soybean and cotton farmers across two states selected for high disease incidence and high existing crop insurance penetration, supported by a district-level deployment priority map.
Pitch Narrative Reconstruction
Revised the pitch opening to lead with the farmer-level problem and the 91% pilot renewal figure, deferring technical model architecture to a later section addressed only on investor request.
Financial Model Construction
Built a three-channel revenue model (insurance data licensing, agro-input advisory, and direct farmer subscription) with a bottom-up Year 1 revenue projection of ₹2.8 Crore derived from observed pilot unit economics rather than top-down market share assumption.
Investor Introductions
Facilitated introductions to three AgriTech-focused investors and arranged reference calls between the prospective lead investor and two of the 340 pilot farmers through an existing Farmer Producer Organisation relationship: a step the investor cited as decisive in the investment decision.
Position at engagement vs. position at close
Swipe the table sideways to compare.
| Dimension | At engagement | At close |
|---|---|---|
| Revenue Channels | One: farmer subscription only | Three: insurance, agro-input, direct subscription |
| Market Sizing | 200 million farmers, undifferentiated | 2.3 million beachhead with district-level prioritisation |
| Venture Outcome | Two stalled conversations | Term sheet at third meeting post-reframing |
| Capital Secured | ₹0 | ₹6.80 Crore |