IoT-Based Automated Irrigation for Pak Choy Using Aquaculture Wastewater

Abstract

Efficient irrigation management and nutrient utilization are essential factors in improving pak choy productivity. This study proposes an Internet of Things (IoT)-based automated irrigation system that integrates an ESP32 microcontroller, soil moisture sensors, rain sensors, Telegram Bot, and n8n workflow automation. The system applies growth-stage-based irrigation scheduling and utilizes catfish pond wastewater as an alternative nutrient source. Irrigation volumes were dynamically adjusted according to plant growth stages, consisting of 0.65 L (days 1–14), 0.63 L (days 15–21), and 0.37 L (days 22–35). System performance was evaluated through communication testing, confusion matrix analysis, and plant growth observations. Experimental results showed average end-to-end delays of 6.0 s for Telegram communication and 11.3 s for n8n integration, with an average command latency of 2.779 s. The irrigation decision mechanism achieved 90% accuracy, 88.9% precision, and 91% recall. Furthermore, pak choy irrigated with catfish pond wastewater demonstrated superior growth performance, reaching 16 cm plant height, 20 leaves, and 81 g harvest weight, compared with 14 cm, 16 leaves, and 67 g under conventional irrigation. These findings demonstrate that the proposed system provides reliable irrigation automation while supporting sustainable agriculture through the reutilization of aquaculture wastewater.