Muhammad Rizqi Maulana, Miftahur Rohman

IoT System Design for Dragon Fruit Plants Lighting and Watering Automation Using Fuzzy Method

Introduction

Iot system design for dragon fruit plants lighting and watering automation using fuzzy method. Optimize dragon fruit growth with an IoT automation system using fuzzy logic for precise lighting and watering. Features ESP32, YL-69, LDR sensors, and Blynk for monitoring.

Abstract

Dragon fruit is a high-value commodity that has gained increasing popularity in the global market, yet requires intensive care, particularly in terms of precise watering and lighting management. In Indonesia, dragon fruit cultivation often faces challenges due to reliance on imprecise manual systems. Conventional systems currently available are frequently inefficient as they cannot dynamically adapt to fluctuating environmental conditions. Therefore, this research aims to develop an IoT-based automation system capable of optimizing dragon fruit growth through intelligent watering and lighting control using more adaptive fuzzy logic methods. The system is designed with an ESP32 microcontroller as the control center, integrated with a YL-69 soil moisture sensor and LDR light sensor. Both sensors were calibrated with high accuracy, showing errors of 2.85% for the moisture sensor and 2.80% for the light sensor respectively. Sensor data is then processed using fuzzy logic to generate proportional control through PWM modulation for water pump and LED light actuators. System implementation demonstrates robust performance, where lights turn on fully at light intensity ≤110 lux, dim at 110-640 lux, and turn off above 640 lux. Meanwhile, the pump operates at maximum capacity when soil moisture ≤45%, at half power between 45-70%, and stops above 70%. The Blynk platform is utilized for real-time environmental monitoring through a user-friendly mobile interface. Twenty-four hours test showed that the system is responsive and adaptive, and has the potential to increase water and energy efficiency compared to conventional systems. Keyword: Dragon Fruit, ESP32, Fuzzy logic, Internet of Things

Review

The paper presents an intriguing and highly relevant solution for optimizing dragon fruit cultivation, addressing the critical need for precise environmental control in this high-value crop. Focusing on an IoT-based automation system, the research aims to overcome the limitations of conventional manual or rigid systems prevalent in regions like Indonesia, which struggle to adapt to dynamic environmental changes. The core innovation lies in the intelligent integration of an ESP32 microcontroller with calibrated environmental sensors and a fuzzy logic approach to govern lighting and watering, promising a more adaptive and efficient cultivation method for enhanced plant growth. A key strength of this work is the application of fuzzy logic, which inherently offers a more nuanced and adaptive control mechanism compared to traditional threshold-based systems, enabling proportional responses for actuators. The abstract clearly demonstrates this through the specified control ranges for both light intensity (full, dim, off via PWM for LEDs) and soil moisture (max, half, stop for water pumps). The accurate calibration of the YL-69 and LDR sensors, with reported errors below 3%, further underscores the system's reliability in data acquisition. Moreover, the utilization of the Blynk platform for real-time monitoring enhances user interaction and management, while the successful 24-hour test confirms the system's responsiveness, adaptiveness, and potential for significant water and energy savings. While the abstract effectively outlines a promising system design and initial validation, a comprehensive review would benefit from further details on several aspects. The abstract mentions the "potential to increase water and energy efficiency," but a more rigorous comparative analysis with conventional systems, perhaps quantifying these savings over a longer period, would strengthen this claim. Details on the specific fuzzy logic rules and membership functions used would also be valuable for reproducibility and understanding the system's intelligence. Furthermore, the 24-hour test is a good start, but long-term performance data, including the system's actual impact on dragon fruit growth, yield, and quality, would be crucial. Future work could also explore the integration of additional environmental parameters (e.g., temperature, humidity), scalability for larger farms, and economic viability considerations to provide a more holistic assessment of this innovative approach.

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