A Data-Driven Approach to Soil Fertility Assessment Using Extreme Learning Machine

Soil fertility is a critical factor in agricultural productivity and sustainability. Accurate soil fertility assessment can lead to better crop management and yield optimization. This paper presents a data-driven approach to soil fertility assessment using an Extreme Learning Machine (ELM), an advanced machine learning algorithm known for its fast-learning speed and high generalization capability. The study involves comprehensive exploratory data analysis (EDA) and feature engineering to identify the most relevant soil properties influencing fertility. A dataset comprising various soil attributes was preprocessed, standardized, and subjected to feature selection techniques such as Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA). The ELM model was trained on this refined dataset and evaluated for its performance in predicting soil fertility ranks. This research highlights the potential of using ELM for soil fertility assessment, offering a robust and efficient tool for agricultural stakeholders. The findings can contribute to more informed decision-making in soil management and crop planning, ultimately enhancing agricultural productivity.