Plant Heath Detection and Growth Monitoring Using ML and IoT
Keywords:
Machine Learning (ML), Internet of Things (IoT), Soil Moisture Sensor, ESP-32 Microcontroller, DHT-11 Temperature Sensor, BreadboardAbstract
The health and growth of plants are crucial for agricultural productivity and food security. Traditional methods of plant health monitoring are time-consuming, labor-intensive, and often inaccurate. This study proposes an innovative approach to plant health detection and growth monitoring using machine learning and Internet of Things (IoT) technologies. Our system integrates sensors and cameras to collect real-time data on plant growth, temperature, humidity, and soil moisture. Machine learning algorithms are then applied to analyze this data and detect early signs of plant stress, disease, and pests. The system provides accurate and timely alerts to farmers, enabling them to take prompt action to prevent damage and optimize plant growth.
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