<![CDATA[This study proposes a rule-based IoT decision support system for real-time chili plant growth monitoring. Its main contribution is an integrated framework for chili cultivation that combines plant growth indicators and environmental sensor data within a single transparent IF-THEN rule structure an integration not commonly addressed in prior chili-monitoring systems. The system integrates eight parameters stem height, branches, leaves, fruits, soil moisture, temperature, water volume, and soil pH collected from 100 plant samples and 704 repeated environmental sensor records over a single 14-week growing season. Classification is formalized as a maximum-severity decision over a knowledge base of eight rules: each rule carries a severity level, and a record receives the label of the most severe triggered rule (Less Optimal > Fairly Good > Optimal). Thresholds were set through a two-step calibration that combines established agronomic ranges for Capsicum annuum with empirical refinement against observed plant responses. The logic was validated against 100 expert-labeled records using a confusion matrix, achieving 96% overall accuracy (95% CI: 90.2–98.4%), a macro-average F1-score of 93.4%, and per-class F1-scores between 88.9% and 98.0%. Classification results show that 75% of records fell into the Less Optimal category, driven primarily by elevated temperature (mean 34.09°C) and acidic soil pH (mean 5.38). The system delivers interpretable, actionable decision support without complex machine learning infrastructure; its main limitation is that thresholds were calibrated on a single site and season, so external multi-site validation is required before broader deployment.]]>
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