METHOD

One method. Three teams who need the same truth.

Estate Management

Palm census, black bunch count, ripeness grading and harvest-round planning, block by block, every morning.

Finance Management

A rolling forecast of cost, P&L and cash that moves with the crop, with every variance traced to the general ledger.

IT & Data

On-premise deployment, a governed data model, APIs to your ERP, full audit trail and role-based access.

The AIX loop

Every output passes a gate before the next step may use it. The last gate closes the loop: what happened in the field trains the next forecast, so the estate gets sharper every cycle.

SIX-STEPS-SIX-GATES-NOTHING-MOVES-ON-UNT_Actual Size (1).png

Capture

Palm and bunch images, harvest records, weighbridge tickets, rainfall.

Gate 1 · Metadata check: source, timestamp, GPS inside the block polygon.

Count & grade

Palm census, vacant points, black bunch count, FFB ripeness classes.

Gate 2 · Precision and recall against a grader-checked sample. Low-confidence images go to people.

Forecast

Monthly FFB by block from palm age, BBC, rainfall lags, water deficit and fertiliser history.

Gate 3 · Rolling-origin backtest; MAPE reported for each forecast horizon.

Budget

Harvesting labor, fertilizer rounds, FFB transport and crop recovery are repriced as the forecast moves.

Gate 4 · Every variance traced to the GL line; changes above threshold need approval.

Decide

Harvest rounds and intervals, rosters and fertiliser applications, with the evidence in view.

Gate 5 · AI proposes, a named manager decides; the reason is logged.

Execute

Rounds, rosters, and applications are completed, and the tickets flow back to step one.

Gate 6 · Execution compared to plan next morning; the result trains the next forecast.

1

Vision AI

See every hectare.
Trained on your own images, running on your own GPU servers. Capture → annotation (ground truth) → training → evaluation → deployment → inference. Object detection and segmentation, measured by mAP@0.5IoU and a per-class confusion matrix | Written labelling rules for hidden bunches, second-grader review, active learning on hard images | Outputs: palm census and SPH, vacant points, BBC for the 3–6 month outlook, ripeness grading

2

Data fusion

One record everyone trusts.

Weighbridge, field records, weather, imagery and ERP joined on block ID + date + palm ID in a common data model.
ELT pipelines with schema validation at every stage | Data lineage: every value keeps its origin, time and location | ABW = ticket kg ÷ bunch count; field → weighbridge → mill mass balance flags loose-fruit and transport loss

AIX-Method-Fusion-Mass-Balance-1x1_7.mp4

3

AI/ML forecast

Forecast every tonne.

Monthly tonnes by block, division and estate, each with a stated range. Plan on the median, budget for the range.

Features: YAP, planting material, BBC, sex ratio, lagged rainfall (6–24 months), water deficit, fertiliser, past yield | Gradient-boosted treestime seriesquantile regression for P10/P50/P90 | Rolling-origin backtest, MAPE by horizon, drift monitoring triggers retraining

4

Dynamic budgeting

A budget that moves with the crop.

Annual outlook = actual to date + forecast remaining. Cost, P&L and cash update in seconds when the crop or the price shifts.

Drivers: FFB tonnes → harvesting cost per tonne, labour days, transport trips, fertiliser needs | Scenarios: dry spellhigh cropprice shift | Versioned budgets, approval workflow, variance traced to the GL