A Study on Variable Selections and Prediction for Crop Recommender System with Soil Nutrients Using Stochastic Model and Machine Learning Approaches

Abstract

To develop a crop recommendation system using soil nutrient data, you'll need a dataset containing details on soil nutrients and the crops that thrive in particular soil conditions. While I can't supply a specific dataset, I can offer guidance on the types of data you should seek or gather for building such a system. Machine learning, a subset of artificial intelligence (AI) and computer science, centers on harnessing data and algorithms to replicate human learning processes, steadily enhancing its precision over time. This paper considers crop recommender dataset with soil nutrients-related dataset like N, P, K, ph, EC, S, Cu, Fe, Mn, Zn, B, label. The machine learning approaches are used to analyze and predict the dataset using Logistic, Multilayer Perceptron, Simple Logistic, Hoeffding Tree, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.