Every tree
tells a story.
We read it
in real time.
Individual tree-level sensor intelligence for high-value horticulture. Continuous sub-daily data. Flush cycles predicted days ahead of visible confirmation. Built on a live 2,100-tree Alphonso mango testbed.
The Platform
Not field averages.
Individual tree intelligence.
Every precision agriculture platform starts by averaging your orchard into a polygon. Sankhya starts by giving each tree its own identity - its own sensor history, its own behavioral baseline, its own predictive model.
Tree-Level Resolution
Each tree has its own EC, moisture, pH and temperature sensors. Healthy trees don't mask struggling ones. Struggling ones get found.
Continuous Sub-Daily Data
Not lab reports. Not weekly snapshots. Live sensor streams capturing inflection points - EC decline during active uptake, moisture floors before stress sets in.
Compounding Intelligence
Every season, every intervention, every outcome recorded per tree. The system learns what each specific tree responds to - not averages from a crop database.
Predictive, Not Reactive
EC decline correlated with moisture stability is active uptake. We detect flush cycles 2-3 days before visible confirmation and optimize interventions ahead of need.
Open Hardware
ESP32-based nodes with RS-485 sensors. Schematics openly shared. The moat is 300+ days of longitudinal per-tree data and the AI built on top of it.
Pure SaaS Intelligence
Cloudflare edge infrastructure. Dual AI model architecture - continuous auditing and deep agronomic analysis - accessible from any device, anywhere on the farm.
Live Case Study - Tree #47
Pradyumna
Near-dead on arrival. The system watched, the data spoke, and a targeted intervention protocol brought it back - with a flush cycle predicted two days before a single leaf appeared.
As Pradyumna recovered, EC began declining while soil moisture held stable. This is the signal: active root uptake preparing for a vegetative flush. Field-averaged platforms wash this signal out entirely. At tree level, it is unmistakable.
Competitive Reality
The resolution gap
is structural.
Field-averaged platforms can add more features. They cannot add individual tree resolution. That requires owning the orchard and running sensors through multiple seasons.
Architecture
Built for the edge,
designed to compound.
The real moat isn't the model - it's the data. Three hundred days of individual tree sensor history, with documented intervention outcomes, is not buildable by throwing money at the problem. It requires owning the orchard, running the sensors, and iterating through actual seasons.
Ready to see your orchard
at tree level?
We're onboarding a small cohort of Alphonso mango growers for the first external deployment. Sensors, setup, and the intelligence layer - everything included.
Request Access