Title: Canopy centre-based fuzzy-C-means clustering for enhancement of soil fertility prediction

Authors: M. Sujatha; C.D. Jaidhar

Addresses: Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore – 575025, Karnataka, India ' Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore – 575025, Karnataka, India

Abstract: For plants to develop, fertile soil is necessary. Estimating soil parameters based on time change is crucial for enhancing soil fertility. Sentinel-2's remote sensing technology produces images that can be used to gauge soil parameters. In this study, values for soil parameters such as electrical conductivity, pH, organic carbon, and nitrogen are derived using Sentinel-2 data. In order to increase the clustering accuracy, this study suggests using Canopy centre-based fuzzy-C-means clustering and comparing it to manual labelling and other clustering techniques such as Canopy, density-based, expectation-maximisation, farthest-first, k-means, and fuzzy-C-means clustering, its usefulness is demonstrated. The proposed clustering achieved the highest clustering accuracy of 78.42%. Machine learning-based classifiers were applied to classify soil fertility, including Naive Bayes, support vector machine, decision trees, and random forest (RF). Dataset labelled with the proposed RF clustering classifier achieves a high classification accuracy of 99.69% with ten-fold cross-validation.

Keywords: clustering; classification; machine learning; remote sensing; soil fertility.

DOI: 10.1504/IJCSE.2024.136255

International Journal of Computational Science and Engineering, 2024 Vol.27 No.1, pp.90 - 102

Received: 10 Apr 2022
Received in revised form: 05 Sep 2022
Accepted: 29 Sep 2022
Published online: 25 Jan 2024 *

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