Developing a Conceptual Framework for Soil Property Analysis and Crop Yield Prediction Using Machine Learning Techniques

Abstract

The most important single factor is soil fertility which influence crop sustainability and agricultural productivity. The necessity to use data-driven approaches to assess the health of the soil and propose the crops that should be grown in it has become a crucial issue because the accuracy of agriculture is required increasingly frequently. Based on the dataset of the Soil Health Card (SHC) of the Government of India, the presented study provides a conceptual framework that involves the application of the machine learning approaches to analyse soil characteristics and predict its agricultural productivity. The framework is based on twelve important soil parameters: sulphur (S), nitrogen (N), zinc (Zn), phosphorus (P), electrical conductivity (EC), potassium (K), manganese (Mn), copper (Cu), boron (B), iron (Fe), organic carbon (OC), and pH to cluster soil samples into the categories of low, medium, and high soil fertility by using the K-means algorithm. To suggest the correct crops that must be grown in each of the fertility categories, the Random Forest Classifier is then trained after the clustering. The model is checked by K-Fold cross-validation (k=5) and Holdout (80/20 split) to make sure that in unseen data strong generalization will be achieved. An average performance of 91 percent in K-Fold, and zero in holdout validation showing no inaccuracies in dividing the test set and an RMSE and MAE also zero, results indicate high performance and no mistakes in classification. Also, the proposed methodology enhances the agronomic decision-making with the help of AI-based crop proposals targeting each of the fertility classes. This study is an indication of the efficiency of the integration of supervised and unsupervised methods in agricultural informatics. It attracts interest in how intelligent models can high-grade the use of resources, encourage sustainable agriculture and endow growers with useful information based on real-life DO data.