A multi-farm global to local expert-informed machine learning system for strawberry yield forecasting

The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all aspects of the supply chain from staffing, supplier demand, food waste and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local machine learning system. Additionally, it investigates the ERA5 climate model's viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that by combining both the expert forecasts and the ERA5 climate model with the machine learning model, we can -- in most cases -- get better forecasts that outperform the growers' pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all plots and an RMSE of 0.0872 with ERA5 climate data included.