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The "Plant Health Monitoring Project" is an intelligent system designed to monitor and maintain optimal conditions for plant growth. This project incorporates sensors such as LDR (Light Dependent Resistor), DHT11 (Temperature and Humidity Sensor), Soil Moisture Sensor, a Water Motor, and a Relay. The collected data, including light intensity, temperature, humidity, and soil moisture, is sent to Thing Speak for real-time monitoring. The system automatically activates the water motor through the relay if the soil moisture level is low, ensuring the plants receive adequate water. This project aims to promote efficient plant care and ensure a healthy growth environment. This project aims to develop an advanced plant health monitoring system by integrating Internet of Things (IoT) technology with machine learning algorithms. The system will utilize various sensors to collect real-time data on environmental conditions such as temperature, humidity, soil moisture, and light intensity, as well as plant health parameters including leaf color, size, and shape. The collected data will be transmitted wirelessly to a central server for analysis. Machine learning algorithms will then be employed to analyze the data and identify patterns indicative of plant stress, diseases, or nutrient deficiencies. The system will provide timely alerts to farmers or gardeners, enabling them to take proactive measures to maintain the health and productivity of their plants. This innovative approach to plant health monitoring has the potential to revolutionize agricultural practices and contribute to increased crop yields and stainability.
P Triveni (Sat,) studied this question.
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