Intelligent Actuator Control in Smart Agriculture through Machine Learning and Sensor Data Integration

Aug 3, 2026

Smart agriculture leverages Internet of Things (IoT) technology to build intelligent greenhouses that monitor environmental conditions in real time and respond automatically. The study proposes a machine learning approach to analyze sensor signals, including temperature, humidity, water level, and soil nutrient concentrations (N, P, K), to determine optimal activation timing for actuators such as fans, irrigation systems, and water pumps. A gradient boosting model trained on a Kaggle dataset (37,922 records) achieves 99.62% predictive accuracy, then attains 99.55% validation accuracy in an IoT simulation using synthetic sensor data, supporting robust decision-making and improved resource efficiency.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Article Info

Article history and Keywords

Abstract

Introduction

Zero : Jurnal Sains, Matematika, dan Terapan E-ISSN : 2580-5754; P-ISSN : 2580-569X Volume 9, Number 1, 2025 DOI: 10.30829/zero.v9i1 .24421 Page: 150-161 Intelligent Actuator Control in Smart Agriculture through Machine Learning and Sensor Data Integration 1 Fadhillah Azmi Department of Electrical Engineering, Universitas Medan Area, Medan, Indonesia 2 M. Khalil Gibran Department of Computer Science, Universitas Islam Negeri Sumatera Utara, Medan, Indonesia 3 Insidini Fawwaz Department of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia 4 Rina Anugrahwaty Department of Telecommunication Engineering, Politeknik Negeri Medan, Medan, Indonesia 5 Amir Saleh Department of Computer Engineering and Informatics, Politeknik Negeri Medan, Medan, Indonesia | Article Info
Article history:
Accepted, 28 May 2025
Keywords:
Actuator Control Decisions; Gradient Boosting;
Internet of Things (IoT); Machine Learning;
Smart Argiculture.
Corresponding Author: | ABSTRACT
Smart agriculture leverages Internet of Things (IoT) technology to develop intelligent greenhouses capable of monitoring and responding to environmental changes in real time. This study proposes the use of machine learning to analyze real-time sensor data—such as temperature, humidity, water level, and soil nutrient levels (N, P, K)—to determine the optimal timing for activating actuators, including fans, irrigation systems, and water pumps. In the initial stage, the study utilized the "IoT Agriculture 2024" dataset from Kaggle, which consists of 37,922 records and 13 attributes describing crop and environmental conditions. This dataset was used to train a robust machine learning model based on gradient boosting to support intelligent actuator control decisions. The model demonstrated strong predictive accuracy, achieving 99.62%. In the final stage, the model was evaluated in a simulated IoT-based agricultural system using synthetic sensor data designed to mimic real-world readings of temperature, humidity, soil moisture, and nutrient concentrations. The model achieved a high validation accuracy of 99.55%, indicating its reliability and robustness within the simulated environment. These results demonstrate that the integration of machine learning with real-time sensor data is an effective strategy for automating actuator control in smart greenhouses. The proposed approach has the potential to reduce manual intervention, optimize resource utilization, and improve overall agricultural productivity. This study contributes to the advancement of adaptive, data-driven precision agriculture systems that support long-term food security.
This isan open access article under the CC BY-SA license.
| | --- | --- | | Amir Saleh,
Department of Computer Engineering and Informatics, Politeknik Negeri Medan, Medan, Indonesia
Email: [amirsalehnst1990@gmail.com](amirsalehnst1990@gmail.com)[sollyaryzalubis@gmail.com](sollyaryzalubis@gmail.com) | | | 1. INTRODUCTION
Smart agriculture has been widely used to address increasingly complex challenges in the agricultural sector, such as unpredictable climate change, limited natural resources, and increasing global food needs [1], [2] . The | | Journal homepage: [http://jurnal.uinsu.ac.id/index.php/zero/index](http://jurnal.uinsu.ac.id/index.php/zero/index) most significant technology in the smart agriculture revolution is the smart greenhouse, which integrates Internet of Things (IoT) technology to monitor and control environmental conditions automatically and in real time. By utilizing various sensors installed in the greenhouse, this system can measure environmental parameters that greatly affect plant growth, such as temperature, soil moisture, water level, and soil nutrient content, such as nitrogen (N), phosphorus (P), and potassium (K) [3], [4], [5] . The data obtained from the sensors will be connected to the IoT system and then

FAQs

What sensors and environmental variables are used for actuator control decisions?

The approach uses real-time sensor data including temperature, humidity, water level, and soil nutrient levels (N, P, K) to guide actuator timing decisions.

Which machine learning method is employed in the study?

The study trains a model using gradient boosting to learn from the dataset and support intelligent actuator control decisions.

How is the model evaluated for reliability?

After training on the Kaggle “IoT Agriculture 2024” dataset, the model is evaluated in a simulated IoT-based agricultural system using synthetic sensor data, achieving 99.55% validation accuracy.

Aug 3, 2026

Upload by Seraphina