Internet of things based seasonal auto regression integrated moving average model for hydroponic water quality prediction

Abstract

Technological progress significantly impacts agriculture, with the rapid expansion of industrial and residential areas leading to a scarcity of agricultural land. Modern farming techniques like hydroponics have emerged as a solution, allowing plant growth with water as a medium. Real-time monitoring of water quality is crucial for hydroponic systems. Lettuce (Lactuca sativa) is particularly compatible with hydroponics due to its short growth cycle and nutritional value. Key factors for successful cultivation include maintaining pH, temperature, and nutrient levels within optimal ranges. To address water quality monitoring complexities, internet of things (IoT) technology offers a promising solution. IoT devices autonomously gather environmental metrics such as temperature, pH, humidity, and nutrient concentrations. This study integrates an IoT-driven hydroponic water quality monitoring system using the seasonal auto-regressive integrated moving average (SARIMA) algorithm and the ESP32 microcontroller. This approach allows real-time water quality management, enhancing lettuce cultivation efficiency and productivity. The proposed model achieved 98.6% accuracy, effectively predicting water quality.