SemaiSens

Satellite and AI intelligence for Malaysian agriculture. Every pass becomes a field decision your team can act on, and prove later.

Everyone Can Show You a Green Map

SemaiSens is MKR’s agricultural intelligence platform. It puts satellite imagery, weather data and your own field records in one view.

The difference is what sits behind an alert. Each one carries the evidence that produced it, so your team can see what changed and check what happened after they acted. Built in Malaysia, for Malaysian fields.

Decisions Backed by Evidence

Every alert links back to the field, weather and satellite signals behind it. Your team can see what changed and why something is being recommended, rather than trusting a score they cannot open up.

Models That Know the Crop Calendar

Mature rubber drops its leaves every year. A generic tool reads that as stress and sends your team out to inspect a healthy block every February, until they stop trusting the alerts altogether. SemaiSens suspends the health band for those weeks and says nothing.

One View for Every Role

Growers see what to do next. Field teams get priorities with a location attached. Managers see the whole operation. All of it comes off the same record.

Your Farm Knowledge Stays Yours

Your field records and observations stay together, and they stay yours. What your team already knows becomes part of the guidance instead of sitting in a system nobody opens.

Four Crops, Modelled Properly

Flooded paddy, long-lived palms, rubber on a tapping rotation, pineapple planted in batches. Each one is modelled on its own terms.

Rice

A field still reading heading values at day 110 does not have a better canopy than its neighbours. It has delayed maturity.

29 crop guides

Oil Palm

A drought this month shows up as reduced bunch number and weight up to two years later. The model reads further back than the current pass.

27 crop guides

Rubber

For six to ten weeks a year, a perfectly healthy tapped block reads below band by design. The system stays quiet through it.

24 crop guides

Pineapple

A block reading above 0.75 is a scouting prompt, not a clean bill of health. Batch planting is modelled explicitly.

28 crop guides

From Boundary to Proof

Four steps. Each field action leaves a record of what changed, who went out, and what you can show afterwards.

01

Add Your Fields

Map a block, upload a boundary, or start from the farms already under your care.

02

Understand What Changed

Satellite, weather and on-ground context in one place, instead of three tools that do not talk to each other.

03

Assign the Next Action

Turn an alert into an inspection or a task, assigned with the context the field team needs to act on it.

04

Learn From the Outcome

Keep a record of what actually happened, so next season starts with better context than this one did.

Inside the Platform

Sentinel-2 at 10 m and Landsat at 30 m ingest automatically from the day you draw a boundary, a pass every three to seven days. Paid tiers add higher-cadence imagery. When cloud spoils a pass it is marked hollow rather than quietly skipped.

Indices matched to the crop rather than applied uniformly
Rule cards bound to published agronomic literature
Semai Advisor, which declines to answer when the evidence is thin
Scout tasks with guided routes for field teams
Ground truth findings that feed back and recalibrate the model
Regional and national views above the estate

Who It's For

Growers

Know which block needs attention and why, then go and deal with it.

Estate Teams

Field visibility and team coordination on one system, so priorities stop having to be chased down.

Agencies

Track patterns across regions, compare outcomes, and back programme decisions with evidence.

See SemaiSens in Your Farm's Context

Bring your crop, your location and the way your team works. Plans start free for a single block and scale up to estates and agencies.