Novel Machine Learning-based Soil Characteristic Analysis

Since the computer's invention, every subject of knowledge has been digitalized, allowing computer users to view all available information. Because of this, data in every industry is growing exponentially. This article explains why researchers study agriculture. We projected three new classification approaches to overawe these restrictions: Hybrid KNN classification methods produce and choose prototypes from an initial training set. These methods include training set reduction KNN, which uses prototype selection to reduce training sets, training set reduction, which creates training set prototypes utilizing either the Elbow or Silhouette technique, and hybrid classification approaches, which use both prototype generation & selection mechanisms. If any of these strategies are to succeed, the KNN classifier must finish its classification work faster and use less space. Utilizing a soil fitness card agricultural dataset, we tested our unique classification algorithms and found that they solve our concerns.