<![CDATA[Lettuce (Lactuca sativa L.) is a high-value horticultural commodity susceptible to tip burn, a calcium deficiency disorder triggered by high temperature and low humidity, and frog eye, a fungal leaf spot caused by Cercospora canescens that thrives under low temperature and high humidity. Visual inspection detects both only after damage has occurred. This study developed an Internet of Things early detection system that classifies environmental risk conditions using Gaussian Naive Bayes embedded directly on an ESP32 microcontroller. The system integrates DHT22, pH, and TDS sensors and sends automatic Telegram notifications. Environmental data were collected for 31 days at a hydroponic farm in Makassar, yielding 1,005 valid records after preprocessing. The model was trained in Python with scikit-learn, and its parameters were deployed on the ESP32 as embedded machine learning. The simulation model achieved 95.56 percent accuracy, while the real-data model achieved 94.03 percent accuracy on 201 test samples, with a five-fold cross-validation accuracy of 92.91 percent and a spread of only 0.12 percent across four validation methods. Field visual validation showed 52 percent agreement, indicating that the system is best suited as an early warning of conducive conditions rather than as a symptom diagnosis tool. Keywords—Embedded Machine Learning, ESP32, Frog Eye, Naive Bayes, Tip Burn]]>
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