Precision Agriculture: Forecasting Crop Prices Through Advanced Data Mining and Machine Learning Models

It is a challenging undertaking to predict the prices of agricultural products especially in India, where majority of the households are always faced with economic insecurities and variable incomes. Local production is hardly ever predictable and it is the case that extensive importance is assumed to patterns of behavior in the local markets often ruling out any utility of conventional practices. But, the discovery prospects of the unseen information delivered by the background of large agricultural data holds the possibility of inventive exploitation of a more intelligent, data-dense modelling procedure. Data mining determines covert connections and chain tendencies that may be deployed to construct unique anticipatory models that surpass conventional heuristics. We introduce a machine learning approach of SARIMAX as means of predicting the agricultural market prices, namely a time-series prediction method. The model involves prices at past periods and other external inputs, to indicate the intricate seasonality which is experienced in the agricultural crops data and the stochastic characteristics of the same data. The suggested method has phenomenal predictive power, R-squared of 98.36 %, and effectively represented agricultural price volatility that people experience in the real world. We applied data-based solutions to try to improve predictive accuracy and deliver some real-world utility to the user in their decision making. Being able to combine machine learning with data mining is an important step towards Precision Agriculture because predictive intelligence can potentially lead to financial outcomes for the agricultural industry and alleviate the effects of market volatility.