  Accuracy of crop price forecasting techniques is important because it enables
the supply chain planners and government bodies to take appropriate actions by
estimating market factors such as demand and supply. In emerging economies such
as India, the crop prices at marketplaces are manually entered every day, which
can be prone to human-induced errors like the entry of incorrect data or entry
of no data for many days. In addition to such human prone errors, the
fluctuations in the prices itself make the creation of stable and robust
forecasting solution a challenging task. Considering such complexities in crop
price forecasting, in this paper, we present techniques to build robust crop
price prediction models considering various features such as (i) historical
price and market arrival quantity of crops, (ii) historical weather data that
influence crop production and transportation, (iii) data quality-related
features obtained by performing statistical analysis. We additionally propose a
framework for context-based model selection and retraining considering factors
such as model stability, data quality metrics, and trend analysis of crop
prices. To show the efficacy of the proposed approach, we show experimental
results on two crops - Tomato and Maize for 14 marketplaces in India and
demonstrate that the proposed approach not only improves accuracy metrics
significantly when compared against the standard forecasting techniques but
also provides robust models.
