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Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing.

S Hamed Javadi1,2, Angela Guerrero2, Abdul M Mouazen2

  • 1Interuniversity Micro-Electronics Center (IMEC), Kapeldreef 75, 3001 Leuven, Belgium.

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For precision agriculture, k-means clustering with normalized soil data effectively delineates management zones (MZs). A new pipeline (CaSP) automates this process, improving variable rate applications for farming inputs.

Keywords:
clusteringfeature selectionmanagement zone delineationprecision agriculture

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Area of Science:

  • Agricultural Science
  • Data Science
  • Geospatial Analysis

Background:

  • Precision agriculture (PA) relies on accurate management zone (MZ) delineation for variable rate input application.
  • Optimal clustering algorithms and data formats for MZ delineation remain debated.

Purpose of the Study:

  • To evaluate clustering algorithms and data formats for MZ delineation in PA.
  • To develop an automated pipeline for MZ delineation and smoothing.

Main Methods:

  • Compared k-means, fuzzy C-means, hierarchical, mean shift, and DBSCAN algorithms.
  • Utilized soil fertility, Sentinel-2, and yield data with vis-NIR spectroscopy.
  • Implemented feature selection and range normalization, followed by DBSCAN-based smoothing.

Main Results:

  • K-means demonstrated superior performance for MZ delineation.
  • Data normalization (range normalization) and feature selection significantly improved delineation quality.
  • A novel clustering and smoothing pipeline (CaSP) was developed and validated.

Conclusions:

  • K-means clustering with normalized data and feature selection is recommended for MZ delineation.
  • The CaSP pipeline offers an automated solution for efficient variable rate application in PA.