<![CDATA[Melon cultivation requires appropriate light intensity and photoperiod management because light directly affects photosynthesis, vegetative growth, and plant productivity. In conventional cultivation, additional lighting is often controlled manually, making it less adaptive to changes in sunlight intensity, temperature, and daily photoperiod. This study develops an Internet of Things (IoT)-based automatic light intensity control system using the Fuzzy Sugeno method to optimize photoperiodism in melon plants during the vegetative phase. The system integrates an ESP32 microcontroller, BH1750 light intensity sensor, DHT21 temperature sensor, real-time clock, AC light dimmer, LED grow light, LCD I2C, and Blynk application. The Fuzzy Sugeno method was implemented to determine the percentage of LED grow light output based on light intensity, temperature, and daily time period. Experimental results showed that the BH1750 sensor calibration reduced the average measurement error from 10.13% to 2.753%, while the DHT21 temperature sensor calibration reduced the average error from 3.95% to 0.31%. The AC light dimmer responded proportionally to PWM input, producing lamp outputs from 0 lux to approximately 28,000 lux. Data transmission to Blynk was successful with an average delay of about 1.01 seconds. Plant testing for 14 days showed that the IoT-based automatic lighting system produced better vegetative growth than the conventional method, with plant height reaching 19 cm and five leaves. These findings indicate that the developed system can support adaptive lighting control for melon photoperiodism optimization.]]>
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