Abstract: This research investigated the development and evaluation of a cloud-based smart plant watering system designed to automate plant irrigation based on real-time environmental conditions. The purpose of this study was to address the inefficiencies and inconsistencies associated with manual plant watering by integrating environmental sensing technologies, cloud computing, and user-friendly applications. The methodology followed a developmental research approach, incorporating stages such as conceptualization, system design, prototyping, testing, and evaluation. Data was collected using environmental sensors, with the system architecture based on a Raspberry Pi Model 3B controller and cloud services for data storage and monitoring. The evaluation process included 32 evaluators (13 in-person and 19 online), who assessed the system's hardware (interface circuits) and software (desktop and mobile applications). Evaluation instruments comprised a five-point Likert scale and ISO/IEC 25010 standards, with statistical analysis performed using SPSS version 27.
The system demonstrated effective automation of irrigation using a modular setup of environmental sensors, a microcontroller, and cloud-based monitoring accessible via desktop and mobile platforms. Performance testing revealed the system’s rapid and responsive moisture regulation, with Sensor 3 recording a moisture increase of 78.33% in 29 seconds (162.07%/s), confirming high efficiency in water delivery. Despite some sensor anomalies and environmental variability, consistent trends in data validated the system’s reliability. User evaluation of the external interface circuit yielded an average satisfaction score of 4.77, while application usability under ISO/IEC 20510 standards averaged 4.59, both categorized as “Very Acceptable.” Limitations included the absence of backup power and local monitoring options, though future enhancements involving AI and blockchain were identified to improve precision and data security.
Keywords: Cloud-Based, Watering, Automation, Sensor data, Development, Evaluation.
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