Per-Tree Agricultural Intelligence

Sankhya Farms — 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.

Live Demo
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.

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Per-Tree Resolution
Every tree maintains its own historical archive — moisture, EC, pH, temperature, and every intervention. Not a field average. Each tree, individually.
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Continuous Sub-Daily Data
Sensor readings captured multiple times per day. Rate-of-change analysis — not snapshots — reveals whether a tree is actively taking up nutrients or accumulating salt.
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Longitudinal Feedback Loop
Measure → Act → Observe → Learn → Refine. Each season, intervention, and outcome compounds into a richer model. Accuracy increases with time.
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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.
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Visual Pest & Disease Detection
50MP fixed and roving cameras image insects on traps, flowers, and fruit; high-resolution leaf imagery surfaces fungal, bacterial, and viral disease before it spreads. Threats are flagged at the tree, the zone, and the day they appear — driving bio-pesticide application at the right point, not the whole orchard.
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Open Hardware
Sensor schematics are open. No proprietary lock-in on the data collection layer. The intelligence is the product — not the hardware. Download Gerbers →
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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

The system uses proprietary signals to recognise patterns in real-time soil data — distinguishing active nutrient uptake from leaching, wastage, and other loss pathways that look identical to conventional monitoring.

Fertilizer delivery is timed to coincide precisely with the uptake window. Applications outside that window are deferred — not because the tree doesn't need nutrients, but because it cannot absorb them yet.

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.

DimensionSankhyaField-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

What Sankhya Tells You

Exact answers for each tree,
every day

Not averages. Not generic advice. The system reads your tree's soil in real time and tells you precisely when to feed, what to feed, and how much — based on what that specific tree is doing right now.

💧
50–70% of fertilizer fed to orchard trees is wasted
Fertilizer applied outside the active uptake window achieves nothing — the tree cannot absorb it regardless of quantity. The system identifies exactly when each tree is consuming, by detecting EC decline against stable moisture, and tells you: feed now, this tree is ready. Outside that window, it tells you to hold. The result is an orchard where inputs go in only when they will actually be taken up.
⚗️
What to feed — matched to the state of the rhizosphere
The root zone is not a static environment. Its chemical and biological state changes hour by hour — and which inputs the tree can actually absorb depends entirely on that state at the moment of application. The system reads the current rhizosphere condition and tells you what to apply now, what to hold, and what to prepare for next. The same input applied two days apart can produce entirely different outcomes. Timing and state-matching is the difference.
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Root zone temperature — protect yield before damage shows
When soil temperature exceeds 28°C, nutrient uptake efficiency drops and the tree begins spending energy on heat stress rather than fruit development. The system flags the approaching threshold and prescribes a timed cold-water pulse through the drip line at 14:30 — before the hottest window opens, not after. A 1°C reduction in root zone temperature during a heat event is sufficient to hold the tree in its productive operating band.
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Pre-flush detection — 2 to 3 days early
A flush is the highest-demand event in a tree's cycle. The system detects the pattern that precedes visible bud break by 2–3 days and tells you: this tree is preparing to flush, raise your inputs now. Trees fed at the right moment produce stronger flushes with higher fruit set. Trees fed too late or not at all lose that cycle entirely.
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Root stress alert — before any visible symptom
A healthy tree actively pulls EC down nocturnally — when stomata are closed, any EC decline is pure metabolic uptake. If a tree's EC stops declining during nocturnal windows while its neighbours continue, the system flags it. Possible causes include root rot, nematodes, compaction, or early vascular disease. You get the alert days to weeks before the tree shows you anything visible — when intervention is still low-cost.
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Pest & disease — caught early, treated locally
A single infested tree becomes a hundred infested trees in a week. The system runs 50MP imagery on insect traps, flowers, and fruit to spot pests at low population density, and high-resolution leaf imagery to detect fungal, bacterial, and viral disease before it spreads. Bio-pesticide protocols are recommended at the exact tree and the exact day — not blanket-sprayed across the orchard. Yield protection without the chemical load.
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Yield targets grounded in real data
The gap between average orchard yields and top-performing orchards is almost entirely management quality — not soil, not variety, not rainfall. The system addresses the primary yield-loss mechanisms simultaneously: poor feed timing, incorrect rhizosphere state at application, heat stress, missed flush windows, undetected root stress, and pest or disease pressure caught late. Preliminary assessment supports a 40% yield improvement over unmanaged baselines when all six are managed concurrently.

7-Day Output

Immediate action

Feed or hold decision. Input type and volume. Timing window. Any alert conditions active on this tree today.

14-Day Output

Monitoring guidance

pH trajectory and when to switch input chemistry. Moisture trend and upcoming irrigation needs. Uptake index movement.

21-Day+ Output

Structural planning

Phenological stage positioning. Pre-flush preparation targets. Orchard-wide triage — which trees need attention next cycle.

Our Position

"The real moat isn't the model — it's the data."
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.
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.
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 generates zone-level irrigation recommendations (Hold, Mild, Strong, or Emergency bands) and early stress alerts, but all actions require operator approval before execution.
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.
Yield cannot be optimised without pest and disease control — and chemical-heavy management destroys margin and quality. Sankhya uses 50-megapixel imagery on insect traps, flowers, and fruit to detect pest pressure at low population density, and high-resolution leaf imagery to spot fungal, bacterial, and viral disease early. Threats are localised to the specific tree or zone, and bio-pesticide protocols are recommended at the affected point — not blanket-sprayed across the orchard. The objective is full pest control with the lowest possible chemical load and the highest fruit quality.
Yes. 50MP fixed cameras and roving capture units image trees, traps, flowers, fruit, and leaves on a regular cadence. Fixed CCTV covers critical infrastructure (water tanks, fertigation bench, pathways), and drone-based aerial surveys are supported for canopy-level assessment. All visual data is indexed alongside soil sensor readings, so canopy observations and root-zone conditions sit in the same time-series record.
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.

Questions? Email [email protected] — 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