Most systems look at one thing at a time: satellite in one place, weather in another, soil in a separate spreadsheet. Loomin brings every signal into a single line of reasoning — so it can explain why a plot will yield what it yields, not just hand you a number.
A plot’s yield doesn’t hinge on one variable. It depends on dozens that interact: the soil in that micro-site, how much it rained in the last 30 days, whether the field technician’s photo shows yellowing, how that plot responded last cycle. Look at them separately and you lose exactly what matters — how they relate.
One model reads the satellite, another the weather, another the soil. Someone combines the outputs in a spreadsheet, with no view of how they relate.
A physical crop model sets the agronomic baseline. On top of it, a model that integrates imagery, text and time series reads how the signals for that plot interact.
Each one captures something the others can’t. The intelligence is in how they combine.
Images of your field every 5 days at 10 m resolution. The model reads the full spectral bands — not just crop vigor (NDVI), but also chlorophyll, leaf moisture and heat stress.
Your field technician sends a photo from the field over Telegram — no new app. The model picks up pest symptoms, nutritional status and plant-level anomalies the satellite can’t see.
Plot-level time series and a 16-day forecast. Temperature, rainfall, humidity, solar radiation and wind, tied to the crop’s growth stage.
pH, N-P-K, organic matter and conductivity, plus your application history. The model links which inputs produced which results, cycle after cycle.
Every recorded harvest becomes part of the plot’s memory: how that soil, that variety and that microclimate responded in past years.
Already have weather stations, soil sensors or an ERP? We connect them. The model weighs your local data above regional data.
Your field technician sends a photo or a note from the field over Telegram. Loomin understands it, turns it into structured data and updates the plot’s forecast — instantly, with no forms and no new hardware.
The forecast isn’t a fixed number. It starts as a wide range and narrows every time new information comes in — until you’re deciding on real data, not a hunch.
It starts from the area’s historical weather, the physical crop model and the plot’s past yields. Wide range, right direction.
The first satellite images show the crop’s actual condition. The model adjusts for observed vs. expected vigor. If there’s an anomaly, the first field technician photo comes in.
Application records, an updated soil analysis and the actual weather of recent weeks allow fine-tuning. The model checks whether fertilization had the expected effect.
With every source integrated, the range converges early enough to adjust logistics, renegotiate transport and plan the harvest. The financial module calculates your margin at that point.
Every completed cycle becomes data for your operation. The model doesn’t just forecast — it gets more accurate for your specific plot over time.
The first cycle uses the crop’s parameters plus whatever data is available for your area. Working forecasts from day one.
The model has now seen how that soil, that variety and that microclimate respond. The correlations become specific to your operation.
After enough cycles, the model knows your operation’s unique patterns better than any global parameter.
We don’t reinvent crop physics or promise magic. We combine sources and models you can audit — and we tell you where every number comes from.
The European Space Agency’s Sentinel-2 program: 13 bands at 10 m, a 5-day revisit, open and permanent access. The same source governments rely on.
Crop growth simulation follows the same standards used by universities and agronomic research institutes in more than 60 countries, calibrated to conditions in the region.
On top of the physics, one model integrates imagery, text and time series into a single line of reasoning. Your data stays yours: it isn’t shared or used to train generic models, and you can export it anytime.
No recommendation is presented with more certainty than the evidence supports. Data honesty isn’t a feature: it’s how we work.
In 30 minutes, we’ll show you which sources we have for your area and what accuracy range you can expect — using your plots, not a sample.
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