<![CDATA[In response to the growing market demand for oyster mushrooms, effective monitoring of the growth environment becomes crucial. This research aims to develop an automated Internet of Things (IoT)-based monitoring system for oyster mushroom cultivation, utilizing Wemos D1 R1 hardware, DHT22 temperature and humidity sensor, LM35 temperature sensor, and the K-Nearest Neighbour (KNN) method for data analysis. The IoT platform employed in this study is Antares. The system is designed to monitor real-time temperature and humidity within the mushroom cultivation chamber. The DHT22 sensor is employed for simultaneous measurement of air temperature and humidity, while the LM35 sensor gauges the soil temperature in the mushroom growth substrate. Data collected by these sensors are automatically transmitted to the Antares platform through Wemos D1 R1 via WiFi connectivity. The K-Nearest Neighbour (KNN) method is applied to analyze the accumulated temperature and humidity data. KNN provides the capability to identify patterns and trends in oyster mushroom growth based on environmental conditions. The results of this analysis offer valuable insights for oyster mushroom farmers to optimize growth conditions and enhance harvest yields. Through the implementation of this system, it is anticipated that efficiency and productivity in oyster mushroom cultivation will be improved. This IoT-based automated monitoring system provides an effective and practical solution for real-time monitoring of oyster mushroom growth conditions, offering farmers the opportunity to take prompt and informed actions to enhance their harvest yields.]]>
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