Abstract: The integration of machine learning and Internet of Things (IoT) sensors has significantly advanced crop monitoring and disease detection methods. This study introduces an innovative approach that combines IoT sensors with live image capture to monitor crop health and identify plant diseases in real time. The proposed system utilizes high-resolution cameras to obtain live images of crops, while IoT sensors collect critical environmental and soil data. These images are then analyzed using enhanced machine learning algorithms trained on large datasets to accurately detect and classify plant diseases. By identifying early signs of disease, the system enables timely intervention, minimizing crop losses. Compared to traditional methods, the fusion of sensor data with image analysis greatly improves the precision of disease detection. The relevance of this research lies in its potential to transform agriculture by equipping farmers with real-time, actionable insights that support better crop management, increased yield, and sustainable farming practices.

Keywords: IoT sensors, crop monitoring, machine learning, disease detection, real-time monitoring, agricultural technology, live image capture, precision agriculture, smart farming, environmental data.

| DOI: 10.17148/IARJSET.2025.12630