Fuzzy Based Irrigation Requirement Analysis Using FkNN and FRNN in Smart Irrigation System

Smart Irrigation System mainly concentrates on the water requirement analysis in the farm land based on the sensed data from the IoT systems. The water requirement by the plants is of greater importance as it not to be overwatered or under watered that degrades the growth of plant. The water requirement is based on various factors in the soil and weather that needs to be measured, classified, predicted and need to store it for the future use. AI techniques combined with the IoT systems are growing in today's technologies at higher rate. The Fuzzy based ANN techniques are very helpful in the classification and predicting the nonlinear and uncertain input data. The proposed work uses FkNN and FRNN for classification and prediction in the irrigation requirement analysis that shown 95.23% prediction accuracy.

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Publication Details

Published
2025-07-25
DOI
https://doi.org/10.1109/atigb66719.2025.11142234
Citations
9
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
7.35
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article

Fuzzy Based Irrigation Requirement Analysis Using FkNN and FRNN in Smart Irrigation System

Gnanajeyaraman Rajaram, A. Selvakumar, D. Suganya
9 citations
Water Quality Monitoring Technologies
7.35
article

Fuzzy Based Irrigation Requirement Analysis Using FkNN and FRNN in Smart Irrigation System

Gnanajeyaraman Rajaram, A. Selvakumar, D. Suganya
article en
9 citations

Abstract

Smart Irrigation System mainly concentrates on the water requirement analysis in the farm land based on the sensed data from the IoT systems. The water requirement by the plants is of greater importance as it not to be overwatered or under watered that degrades the growth of plant. The water requirement is based on various factors in the soil and weather that needs to be measured, classified, predicted and need to store it for the future use. AI techniques combined with the IoT systems are growing in today's technologies at higher rate. The Fuzzy based ANN techniques are very helpful in the classification and predicting the nonlinear and uncertain input data. The proposed work uses FkNN and FRNN for classification and prediction in the irrigation requirement analysis that shown 95.23% prediction accuracy.

Saveetha University (IN)
Openalex Percentile: Top 3%
Water Quality Monitoring Technologies
7.35
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