Precision agriculture increasingly depends on smart and automated solutions to improve crop health and resource use. This work presents a smart plant monitoring system using the ESP32 microcontroller, along with soil moisture and environmental sensors. It provides real-time insights and automated irrigation for different plant types in various soil conditions. The main goal is to create sustainable growing environments by continuously tracking soil moisture, temperature, and humidity. Machine learning models analyze the collected data. Sensor readings are sent to an interactive web dashboard for easy user access. The system automatically starts irrigation when soil moisture falls below ideal levels, based on threshold values and recommendations from the machine learning model. This method cuts down on manual work, prevents under- or over-watering, and adjusts irrigation based on plant needs and environmental changes. Field tests show the system’s ability to enhance plant health, optimize water use, and improve yield consistency with timely data-driven decisions. Machine learning provides tailored recommendations for different crops and soil types, supporting sustainable farming practices. The system’s scalable design and user-friendly dashboard make it suitable for both small gardens and larger agricultural projects. The main contribution is an integrated setup that merges IoT-based monitoring with predictive analytics to automate and improve plant care.
P et al. (2025) studied this question.