Accuracy of crop price forecasting techniques plays an important role in enabling the supply chain planners and government bodies to take necessary actions by estimating market factors such as demand and supply. In emerging economies such as India, the crop prices at marketplaces are manually entered, which can be prone to human-induced errors like 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. Furthermore, we propose a crop-specific context-based model selection strategy using trend analysis to deal with high fluctuations in crop prices. To show the efficacy of the proposed approach, we show experimental results using two different time-series feature representations on two crops - Tomato and Maize for 14 marketplaces in India and demonstrate that the proposed approach provides improved accuracy metrics when compared to standard forecasting techniques.