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. 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%.
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. 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%.