Per-Tree Agricultural Intelligence

Every tree tells a story.
We read it in real time.

Sankhya Intelligence converts continuous soil and environmental data into structured, per-tree decision support — optimizing when and how each tree is fed and irrigated.

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2,100
Trees monitored
300+
Days of data
1
Tree resolution
2–3d
Flush lead time

The Platform

Intelligence at the level
that matters

Field averages leave value on the table. Sankhya models each tree individually — tracking how it responds to your interventions across seasons.

🌳
Per-Tree Resolution
Every tree maintains its own historical archive — moisture, EC, pH, temperature, and every intervention. Not a field average. Each tree, individually.
📡
Continuous Sub-Daily Data
Sensor readings captured multiple times per day. Rate-of-change analysis — not snapshots — is what reveals whether a tree is actively taking up nutrients or accumulating salt.
🔁
Longitudinal Feedback Loop
Measure → Act → Observe → Learn → Refine. Each season, intervention, and outcome compounds into a richer model. Accuracy increases with time.
🔮
Predictive, Not Reactive
The system identifies stress conditions 2–3 days before visible symptoms. Interventions happen at the right moment — not after the tree shows you the problem.
🔧
Open Hardware
Sensor schematics are open. No proprietary lock-in on the data collection layer. The intelligence is the product — not the hardware.
☁️
Pure SaaS Delivery
The intelligence layer runs on Cloudflare's global edge. No on-premise infrastructure. Accessible anywhere, updated continuously.
Live Signal

What the system sees

A nocturnal EC decline with stable moisture is not a deficiency signal — it is an active ion uptake event. The system distinguishes these patterns from salt accumulation, evaporation, and structural imbalance.

This distinction — invisible to one-time readings — drives fertilization timing that reduces waste by eliminating applications during low-uptake windows.

SYSTEM OUTPUTS: 7-day action · 14-day monitoring · 21-day structural plan

Tree Node · Active ● Uptake Window
Soil EC
0.82 dS/m ↓ –0.18 nocturnal
Soil Moisture
34.2% → stable
Root Zone pH
6.1 ↓ cycling
Soil Temp
26.4°C → optimal
EC decline with stable moisture confirmed. Nutrient application window active. Next recommended feed: within 6 hours.

Why It's Different

Not another farm app

Field-level averages, lab reports, and weather APIs are table stakes. The moat is continuous sub-daily data on each individual tree, compounding over seasons.

Dimension Sankhya Field-level platforms
ResolutionIndividual treeField or lot average
Data sourceContinuous in-soil sensorsLab reports, satellite, weather APIs
CadenceSub-daily, timestampedWeekly to monthly
Trigger modelPhysiological signal (EC/moisture Δ)Calendar or threshold alert
Prediction2–3 day flush / stress lead timeReactive observation
LearningCompounds across seasons per treeResets each season

Technical Overview

How the system works

Sankhya Intelligence is a longitudinal feedback system. It does not interpret readings in isolation — it evaluates change over time, in context.

System Definition
Sankhya converts soil and environmental measurements into structured decision support over time. Each tree is modeled individually. The system is designed for precision orchard management and perennial crop systems.
Core Feedback Loop
Measure → Act → Observe → Learn → Refine. The system evaluates rate-of-change values and response patterns to distinguish nutrient uptake from salt accumulation, evaporation, biological consumption, seasonal stress, and structural imbalance.
Data Sources
Manual entry (handheld moisture, EC, pH, temperature readings) or continuous sensor integration. Collection frequency influences resolution but does not change the modeling logic. The system is measurement-agnostic.
Analytical Architecture
Each tree maintains its own historical measurement archive, intervention history, seasonal context, and root-zone behavior patterns. Outputs are structured and derived from trend analysis — not static thresholds.
Decision-Support Model
Sankhya provides context-aware interpretation, risk identification, root-zone balance assessment, early stress detection, and intervention timing refinement. Final decisions remain with the grower.
Crop Scope & Data Ownership
The system is crop-agnostic by design, currently deployed in orchard environments. All primary data belongs to the grower. Sankhya does not sell identifiable farm data.

Output horizons

7-day: Short-term action guidance  ·  14-day: Monitoring guidance  ·  21-day+: Structural planning insights

Last updated: 22nd February 2026

Our Position

"The real moat isn't the model — it's the data."
300+ days of longitudinal per-tree sensor data is not replicable overnight.

FAQ

Frequently asked questions

Sankhya Intelligence is a per-tree agricultural intelligence system that converts soil and environmental measurements into structured, long-term decision support. Instead of treating an orchard as one unit, Sankhya evaluates each tree individually — analyzing moisture, EC, pH, temperature, interventions, and seasonal patterns over time to improve irrigation and nutrition strategy. It is not a dashboard. It is a learning system built around real tree response.
The free version is for growers, orchard owners, and serious tree enthusiasts who want to understand how per-tree intelligence works before implementing deeper monitoring. It demonstrates how Sankhya interprets time-series data and derives decisions from historical context.
No. You may manually input readings from handheld moisture, EC, and pH meters. The more consistent and frequent your measurements, the more precise the system becomes. Continuous IoT-based monitoring is optional and typically used by commercial operators who want automated, high-frequency data capture. Sankhya is measurement-agnostic — it learns from the data you provide.
A one-time reading tells you what is happening right now. Sankhya evaluates how conditions change over time and how trees respond to your actions. It operates on a feedback loop: Measure → Act → Observe → Learn → Refine. This compounding memory allows the system to distinguish nutrient uptake from salt accumulation, evaporation from biological consumption, and short-term fluctuation from structural imbalance. Improvement comes from context — not isolated numbers.
Sankhya is crop-agnostic by design. It is currently deployed in orchard systems, but the underlying intelligence applies to perennial crops and tree-based agriculture more broadly. The architecture is designed to scale across species and regions.
No. Sankhya provides structured insight and forward-looking guidance, but final decisions remain with the grower. The system augments judgment — it does not replace it.
Every season, intervention, and outcome adds context. As historical depth increases, the system becomes better at identifying patterns, reducing uncertainty, and refining recommendations. This cumulative learning — not isolated data — forms the core of Sankhya Intelligence.
No. Data ownership remains with the grower. Individual farm data is never sold. Where anonymized pattern analysis is used to improve the system, it is done without exposing identifiable farm information.
Growers seeking live per-tree monitoring, deeper diagnostic modeling, continuous sensor integration, or advanced intervention tracking may request access to the commercial tier. There is no obligation to upgrade.

Still have questions? Email admin@sankhyafarms.com — we reply personally.

Early Access

Ready to see your trees
as individuals?

We are onboarding a small external cohort. If you manage a perennial orchard and want tree-level intelligence, apply now.

Request Access