| Abstract | Feature extraction assumes a critical part in analyzing agricultural datasets and acquiring
experiences for decision-making. In this paper we propose a methodology that consolidates Sequential Pattern
Mining (SPM) and Random Forest (RF) techniques for feature extraction in agricultural datasets. The SPM
RF approach uses the temporal request of occasions in agricultural information to extract regular sequential
patterns, which are then used to determine enlightening features. These features are thusly incorporated with
the first dataset to make a feature-improved dataset. The Random Forest calculation is utilized to prepare a
prescient model utilizing the improved features. Experimental results show the viability of the proposed SPM
RF approach in extracting significant features from agricultural datasets, empowering further developed
prediction in the agricultural area.
Keywords: Feature Extraction, Sequential Pattern Mining, Random Forest and Agricultural;
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