Systematic Literature Review: Implementation of an Internet of Things (IoT)-Based Smart Irrigation System for Chili Plants
Keywords:
Irigasi Cerdas, Internet of Things (IoT), Machine Learning, Pertanian Presisi, Systematic Literature ReviewAbstract
Smart agriculture has become an important solution to address the global water crisis and the increasing demand for food. Traditional irrigation systems are often inefficient, leading to water wastage and poor crop yields. In contrast, smart irrigation systems that utilize the Internet of Things (IoT) and Machine Learning (ML) are capable of monitoring and automating environmental conditions in real time. In this Systematic Literature Review (SLR), ten scientific articles from various databases were systematically analyzed. The purpose of this review is to examine, synthesize, and evaluate recent research on the implementation of IoT- and ML-based smart irrigation systems. This review identifies various system architectures, types of sensors (such as soil moisture, temperature, pH, and water flow), microcontrollers (including Arduino UNO, NodeMCU ESP8266, ESP32, and Raspberry Pi), IoT platforms (such as Blynk, Node-RED, and Favoriot), as well as commonly used machine learning algorithms. The results show that integrating sensors with fuzzy logic or K-Nearest Neighbor (KNN) algorithms can achieve irrigation decision-making accuracy of up to 98.3%. However, several challenges remain, including lack of standardization, power limitations, data security issues, and implementation costs. This study is expected to support researchers and practitioners in developing more flexible, efficient, and sustainable smart irrigation systemsReferences
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