Soil Fertility Feature Extraction and Reduction Algorithm for Optimizing Agricultural Yield Prediction

Agricultural datasets, such as those provided by the Indian Chamber of Food and Agriculture (ICFA), contain critical parameters that influence soil fertility and crop yield, including nitrogen, phosphorus, potassium, soil pH, dew point, and temperature. The challenge lies in reducing the dimensionality of these datasets while retaining the most informative features to advance the accuracy and interpretability of predictive models. This paper introduces the Soil Fertility Feature Extraction and Reduction Algorithm (SFFERA), a novel algorithm designed to address this challenge effectively. SFFERA integrates multiple feature reduction techniques to ensure a comprehensive and efficient selection process. Initially, correlation analysis is employed to identify and eliminate redundant features, ensuring that highly correlated features do not skew the model. Subsequently, Information Gain (IG) is calculated to determine the consequence of each attribute with respect to the target variable, ensuring that only the most informative atttributess are retained. Principal Component Analysis (PCA) is then applied to transform the dataset into a lower-dimensional space, preserving as much variance as possible while reducing complexity. Finally, Recursive Feature Elimination (RFE) with a decision tree classifier is utilized to iteratively select the most significant features, further refining the dataset. By combining these techniques, SFFERA effectively reduces the dimensionality of agricultural datasets while maintaining the integrity of the information crucial for predictive modeling. The application of SFFERA to the ICFA dataset demonstrates significant improvements in model performance, highlighting the algorithm's potential to enhance agricultural productivity predictions. This comprehensive approach ensures that the most relevant features are extracted, leading to more accurate and reliable predictive models in agriculture.