Fusarium wilt continues to pose a significant threat to global banana production, driving substantial crop losses, which makes early detection of soil conditions favorable to disease onset critical for effective management. This study presents an integrated, rule-based IoT-enabled soil monitoring system for real-time detection of Fusarium oxysporum f. sp. cubense in banana-growing environments. IoT sensors continuously acquire nitrogen, phosphorus, potassium, pH, moisture, electrical conductivity, and temperature readings, which are evaluated against predefined risk thresholds — low nutrient levels (N<85, P<40, K<90), acidic pH (3.0–5.5), elevated moisture (>55%), low electrical conductivity (<0.20), and elevated temperature (24–30°C) — representing soil conditions conducive to Foc proliferation. These evaluations generate a composite risk score that triggers automated alerts, enabling timely, data-driven intervention. System performance was validated under controlled, plantation-simulating conditions.
 

Internet of Things (IoT), Fusarium Wilt, Soil parameters, Rule-Based Algorithm, Banana Plantations, Precision Agriculture.