In this paper, the use of clustering techniques in the field of precision agriculture has been discussed. Types of clustering techniques discussed are k means clustering, mean shift clustering; Density based spatial clustering of applications with noise (DBSCAN), Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM) and Hierarchical clustering. As clustering is a method of identifying similar groups of data in a data set, Clustering has a huge number of uses spread crosswise over different spaces. In data science clustering analysis is used to gain some valuable insights from the data by looking at what groups the data point belongs to which group when clustering algorithm is applied. Few applications of cluster analysis in the field of agriculture are using k means, hierarchical agglomerative clustering approach, pam clustering method and divisive clustering approach to form the clusters based on soil fertility, crop production, irrigation requirements etc.

Precision Agriculture, k_means clustering, Density based spatial clustering of applications with noise(DBSCAN), Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM), Pam clustering, Hierarchial clustering

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