A Novel Methodology based Soil characteristic Analysis using Machine Learning Techniques

Every area of knowledge has been digitized since the advent of the computer, allowing individuals who have access to computer resources to access the information available. As a result, data is increasing at an exponential rate and in an enormous quantity in every area. Researchers have a particular interest in the agricultural sector, which is one such topic. We proposed three new classification methods to overcome these limitations: fast k-Nearest Neighbor, which generates training set prototypes using either the Elbow method or the Silhouette method, training set reduction k-Nearest Neighbor, which reduces training set using prototype selection, and hybrid k-Nearest Neighbor classification methods, which combine both prototype generation and prototype selection mechanisms to generate a prototype from an original training set The goal of all of these approaches is to cut down on the time and space needed for the classification job performed by the k-Nearest Neighbor classifier to be completed. We have tested our novel classification methods using a soil health card agricultural data set, and our results show that these approaches are successful at solving the issues we have identified.