IMPLEMENTATION OF IOT-BASED SMART FARMING FOR OPTIMIZING PLANT WATERING BASED ON MOISTURE AND PH IN DURIAN SEEDLINGS
Abstract
This The rapid development of Internet of Things (IoT) technology has significantly impacted agriculture by enabling automation and real-time monitoring. This study aims to design and implement an IoT-based Smart Farming system to optimize irrigation of durian seedlings based on soil moisture and pH levels. The system uses an ESP32 microcontroller connected to soil moisture and pH sensors, with data transmitted via the MQTT protocol to a Flutter-based mobile application. The application allows users to monitor soil conditions and control a water pump both automatically and manually. In addition, the system integrates a durian leaf disease detection feature using a machine learning model trained with Google Teachable Machine and deployed with TensorFlow Lite on the mobile application. System evaluation was carried out using the Dimension of Quality for Goods (DQG) to assess product quality and the Usability, Learnability, Efficiency, Acceptability (ULEA) method to measure user benefits. The results show that the system operates stably, provides real-time monitoring, and achieves a leaf disease detection accuracy of 93.33%. Furthermore, the product feasibility score reached 86.95%, while the system benefit score was 87.13%, both classified as acceptable. These findings indicate that the proposed system can help farmers improve water efficiency, reduce manual labor, and support the modernization of durian cultivation practices.
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