XCYPF: A flexible and extensible framework for agricultural Crop Yield Prediction

Precision agriculture is the technology driven approach for optimizing farm management in terms of inputs and outputs besides preserving resources. Towards this end many techniques came into existence. Data mining techniques are can be used towards precision agriculture. Numerous efforts have been made to exploit remote sensing data to build various indices for assessing productivity of crops. They include Temperature Condition Index (TCI), Vegetation Condition Index (VCI) and Normalized Difference Vegetation Index (NDVI). Crop yield prediction can help agriculture related departments and organizations to make strategic decisions. In this paper a novel framework named eXtensible Crop Yield Prediction Framework (XCYPF) is proposed that is flexible and extensible. It has provision for selection of crop, dependent and independent variables, datasets for crop yield prediction towards precision agriculture. The available indices are used along with rainfall data and surface temperature for crop yield prediction for rice and sugarcane crops.