Customer A large manufacturer of plant protection products.
Challenge To develop a service for scenario modeling of spring and winter wheat gross yield forecast, taking into account the dependence on weather conditions.
Project Results Developed a set of models based on wheat gross yield time series decomposition into three components: forecast of area under crop, yield trend and yield deviations from trend.
In order to forecast the area under crop, Holt-Winters model was used, the forecast of the yield trend was estimated using linear regression from time, the deviations from the trend were projected using the LightGBM algorithm. The accuracy of the forecast for all the regions exceeds 80%. Developed a cloud service for scenario modelling based on the created ML-mod.
Business Value The accuracy of the forecast for all the regions exceeds 80% which severely improved Client's planning accuracy and contributed to optimization of production plan and marketing costs.