Evaluation of Soil Quality for Crop Prediction Based on Feature Selection in Machine Learning

Abstract

Farmers can practice an effective understanding of the soil's idiosyncrasies allowing for more crops to be grown with fewer resources. Soil prediction relies heavily on calcium, phosphorus, pH, and soil organic carbon. These traits have a substantial impact on crop productivity. The method employs two independent machine learning models, Using KNN and random forest regression; specific soil parameters can be predicted. Crop productivity is boosted as a result of accurate crop prediction. This is where machine learning in the field of crop prediction comes into play. Crop forecast is influenced by geographic, meteorological, and soil characteristics. An integral aspect of the prediction process used by feature selection techniques is choosing the proper features for the right crop or crops. This paper uses categorization approaches to recommend the appropriate crop or crops for the area and conducts a comparative study of multiple wrapper feature selection methods. According to the experimental findings, the adaptive bagging classifier and recursive feature elimination approach surpass the competition. Based on the Africa soil property prediction dataset, the performance of these models is assessed. Knowing the characteristics of the soil in their particular terrain will be useful to the farmers. This study investigates how effectively various machine learning approaches can predict soil qualities crucial to agriculture using spectroscopic data.