Integrated machine learning model for wheat yield prediction using agronomic and meteorological factors: A case study from Punjab, India

Abstract

The study aims to develop a Prediction Model for the wheat yield based on meteorological and agronomic factors using Data Mining techniques. The target area of the Research is the cultivators in the Patiala district of Punjab, India. The researcher has applied the knowledge of the wheat morphology and phenology to build the model. Crop yield is affected by several agronomic factors such as soil type and date of sowing, and meteorological factors such as temperature and rainfall. The existing models apply classification or regression techniques to all the identified factors for the prediction of crop yield. This study divides the set of factors into two categories - the factors which are responsible for year-wise variation in yield, and the factors which are responsible for the individual variation of yield for a particular year among various cultivators. It is found that the year-wise yield variation for a particular cultivator (or a particular region) may be attributed to meteorological factors, whereas the agronomic factors are responsible for variation in yield among different cultivators. So, two models have been proposed; the first model - henceforth known as Block-wise Average Yield Prediction model (BAY model) predicts the Block-wise Average Yield based on temperature, rainfall and the yield data, and the second model - henceforth known as Yield Class Prediction model (YC model) predicts the Yield Class based on soil, management practices and yield data. Finally, these models, i.e., BAY model and YC model are integrated into the final model - Final Yield Prediction model (FYP model) to predict the final yield of a particular cultivator.