Fertilizer Recommendation Using Ensemble Filter-Based Feature Selection Approach

Abstract

Precise application of fertilizer is essential for sustainable agricultural yield. Machine learning-based classifiers are vital in evaluating soil fertility without contaminating the environment. This work uses machine learning-based classifiers such as Classification and Regression Tree, Extra Tree, J48 Decision Tree, Random Forest, REPTree, Naive Bayes, and Support Vector Machine to classify soil fertility. Initially, soil classification was conducted using chemical measurements of 11 soil parameters such as Electrical Conductivity, pH, Organic Carbon, Boron, Copper, Iron, Manganese, Phosphorus, Potassium, Sulphur, and Zinc. The traditional laboratory analysis of soil chemical parameters is time-consuming and expensive. This research work focuses on developing a robust machine learning-based classification approach by employing prominent features recommended by the ensemble filter-based feature selection. To overcome the inconsistency in generating different feature scores, an ensemble filter-based feature selection is devised using three different filter-based feature selection approaches: Information Gain, Gain Ratio, and Relief Feature. Two different datasets are used to evaluate the robustness of the proposed approach. Obtained experimental results demonstrated that the proposed approach with the Random Forest classifier achieved the highest Accuracy of 99.96% and 99.90% for dataset-1 and dataset-2, respectively. The proposed method reduces the inconsistency in feature selection by eliminating a common parameter from both datasets. It minimizes the cost of soil fertility classification by using relevant soil parameters. The classification results are used to provide fertilizer prescriptions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.