Iterative development and functional verification of GREENESA, a sensor-based smart microgreen cultivation system with IoT monitoring and PLC–HMI migration

Background Microgreen production in compact controlled environments requires timely irrigation and lighting, but many small-scale systems still rely on manual observation. This study developed GREENESA, a portable cultivation prototype integrating environmental sensing, local and remote monitoring, and manual and threshold-based actuator control, while documenting its migration from an ESP32–Blynk prototype to a more robust PLC–HMI architecture. Methods An iterative engineering design was applied. The initial prototype integrated an ESP32 controller, DHT11 temperature–humidity sensor, BH1750 illuminance sensor, analogue substrate-moisture sensor, 20 × 4 LCD, 4 × 4 keypad, relay module, two cultivation lamps, a 12-VDC pump, and Blynk connectivity. Functional verification assessed I2C communication, keypad response, moisture-sensor calibration in dry, moist, and wet media, local display, automatic and manual control, and remote monitoring. The redesign introduced an aluminium-profile frame, separated 24-, 12-, and 5-VDC power rails, PLC control, an eight-page HMI, and support for rockwool, cocopeat, and paper media. Evidence was synthesized from design records, test observations, and the project progress report. Results The ESP32 prototype acquired temperature, illuminance, and moisture data; displayed measurements locally; transmitted values to Blynk; and controlled lamps and the pump automatically or manually. Calibration produced non-overlapping analogue ranges for wet (2659–2850), moist (2900–3050), and dry (3565–4095) media. Averaging 20 readings with ±3% hysteresis reduced short-term fluctuations and actuator cycling. All 16 keypad positions were detected. Intermittent LCD corruption or blanking after repeated actuator switching motivated separated power distribution and PLC–HMI migration. The physical prototype and manual were completed, whereas IoT database integration and AI functions remained under development. Conclusions GREENESA enabled integrated monitoring and sensor-triggered control in a portable microgreen unit. However, agronomic benefits, resource savings, long-term reliability, and AI performance require controlled trials and continuous data logging under operational conditions.