Deep Learning Based Crop Health Monitoring Using IoT Sensors

This paper proposes a smart crop-health monitoring system that combines Internet of Things (IoT) sensors with deep-learning-based image analysis. Crop health is affected by soil moisture, temperature, humidity, light and plant diseases. Periodic manual inspection may delay the identification of crop stress. The proposed approach collects field conditions through low-cost sensors and captures leaf images using a camera. Sensor readings and images are processed by a controller or computer. A trained deep learning model can analyse leaf images for visible symptoms, while sensor readings provide information about growing conditions. The system aims to display crop information and generate alerts to support timely crop-care decisions. This is an introductory project proposal; performance results will be added after implementation and testing