IoT-Enabled K-Nearest Neighbors (KNN)-Based Soil Nutrient Recommendation System for Rice (Oryza Sativa L.) Cultivation

Rannie M. Sumacot; Jessie R. Paragas

doi:10.21512/commit.v19i2.12538

Authors

Rannie M. Sumacot Southern Leyte State University
Jessie R. Paragas Eastern Visayas State University

DOI:

https://doi.org/10.21512/commit.v19i2.12538

Keywords:

Soil Nutrient Recommendation, Internet of Things, Microcontroller, Soil NPK, Machine Learning, K-Nearest Neighbor

Abstract

Soil nutrient assessment is essential for optimizing crop yield. Still, existing machine learningbased soil nutrient recommendation systems face several challenges, including limited real-time adaptability, inconsistent integration with IoT frameworks, and a lack of scalability for smallholder use. Many of these systems rely heavily on static, pre-collected datasets and lack the capability to respond dynamically to field conditions. These limitations reduce the practical effectiveness of such models in achieving precision agriculture goals, particularly in resource-constrained environments. These limitations also hinder efficient soil fertility management, leading to ineffective fertilizer application, nutrient imbalances, and reduced crop productivity. To address these issues, the researchers develop an Internet of Things (IoT)-enabled K-Nearest Neighbors (KNN)-based soil nutrient recommendation system specifically for rice (Oryza Sativa L.) cultivation. The system integrates an RS485 Integrated Soil Nitrogen, Phosphorus, and Potassium (NPK) Sensor with an Arduino-based IoT framework to continuously monitor essential nutrients: nitrogen (N), phosphorus (P), and potassium (K). The collected data are processed using Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Linear Regression, and KNN. After rigorous model training on Google Colab, KNN outperforms other models with an accuracy of 98%, making it the optimal choice for predictive soil fertility assessment. This system provides accurate and automated fertilizer recommendations to improve soil management efficiency and sustainability by combining real-time IoT monitoring with machine learning. The research contributes to precision agriculture by offering a scalable data-driven approach that enhances crop yield, reduces fertilizer waste, and minimizes environmental impact.

Dimensions

Plum Analytics

Author Biographies

Rannie M. Sumacot, Southern Leyte State University

Public Administration Department, Faculty of Governance and Development Studies

Jessie R. Paragas, Eastern Visayas State University

Information Technology Department, College of Engineering

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