A Survey - Soil Feature Analysis Using Clustering Techniques and Predict Various Crops in Madurai District

Abstract

In Tamil Nadu, seasonal soil fertility, rainfall, and temperature account for more than 65% of agricultural output. Soils are one of the most valuable natural resources on the planet. The study’s goal was to analyze and survey the historical changes in soil parameter Index of Madurai District and Taluks, South India, using Clustering techniques of unsupervised learning to perform well for this proposed work. In this proposed work to involved, the survey should focus on the Soil parameters (N-Nitrogen, P-Phosphorus, K-potassium), rainfall, and temperature data retrieved from the agriculture government portal for the last five years. The study compares two machine learning clustering techniques, Hierarchical Clustering and K-Means Clustering in estimating soil features at Madurai Taluks. With the use of new agricultural technology, this proposed initiative intends to offer a better suggestion for obtaining an acceptable level of crop output to the Madurai surrounding blocks to get more benefits.