<![CDATA[Most existing implementations rely on static rule-based or fuzzy logic control, which lack adaptability to dynamic environmental changes and often require manual tuning by experts. These limitations are particularly challenging for small-scale farmers who face constraints in technical knowledge, infrastructure, and operational flexibility. To address these issues, this study proposes an intelligent hydroponic irrigation system that embeds the Extreme Gradient Boosting (XGBoost) algorithm as a decision-making model. The system collects real-time sensor data including temperature, humidity, and light intensity, and uses the trained XGBoost classifier to determine irrigation needs with binary output (FLUSH or NO). The system was implemented on a vertical hydroponic setup for strawberry cultivation, and evaluated over a 21-day observation period. The results show that the XGBoost-based model was effective in maintaining consistent vegetative growth, with plants in upper-tier pipes achieving an average height above 25 cm by the end of the cycle. This demonstrates that the model could support responsive and resource-efficient irrigation control. Beyond technical performance, the research highlights the urgency of adopting data-driven smart farming systems to ensure sustainable food production, optimize limited resources, and empower small-scale farmers with accessible and scalable solutions. However, the proposed XGBoost model is still limited to local crops; therefore, when introducing new plant types or additional sensor inputs, parameter adjustments and retraining are required to maintain accuracy. Future improvements may include dynamic model retraining and integration with real-time feedback systems to enhance system autonomy and resilience in broader agricultural settings.]]>
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