SILOSENSE: AN IOT-BASED CORN GRAIN SILO LEVEL MONITORING SYSTEM USING HC-SR04
Abstract
The availability of corn grain in production silos is important for maintaining the continuity of corn flour production. However, manual level inspection is discontinuous, requires operator involvement, and is prone to reading errors. This study presents SiloSense, an Internet of Things (IoT)-based corn grain silo level monitoring prototype using an HC-SR04 ultrasonic sensor, ESP8266 WeMos D1 Mini, SSD1306 OLED display, Firebase Realtime Database, and Progressive Web App. Ultrasonic distance data are converted into estimates of fill percentage, volume, and mass through seven-sample trimmed median filtering, adaptive exponential smoothing with a dynamic alpha range of 0.35–0.85, and two-point linear calibration. The prototype was tested under five main fill conditions, namely 0%, 13%, 45%, 87%, and 100%, along with an additional sweep from 5% to 100%. The results showed consistent readings between the OLED display and the web application, with a median interface response time of 1600.5 ms. The system also classifies silo conditions into SAFE, MODERATE, NEARLY EMPTY, and EMPTY. These findings indicate that SiloSense is feasible as a low-cost, non-contact monitoring approach for small-scale corn grain silo level estimation, with further validation required before industrial deployment.
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