Forecasting yield with three seasons of sensor data
A forecasting model built on soil, weather and harvest data that cut waste by nearly a third.
Sensors everywhere, insight nowhere
Verdant had instrumented its fields for three seasons but the data sat in vendor dashboards nobody cross-referenced. Planting and harvest decisions were still made on experience and gut feel, and over-production in good seasons went to waste.
One data pipeline, one model, plain-language forecasts
We consolidated sensor, weather and harvest records into a single pipeline and trained a yield model per crop and field. Forecasts are delivered as a weekly briefing the farm managers actually read, with confidence ranges rather than single numbers.
Nearly a third less waste in the first full season
Planting volumes now track forecast demand, cutting waste by 31%. Forecast accuracy sits at 92% across the main crops, and the model retrains automatically after each harvest.
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