AI-Smart Plant Health Monitoring System using Deep Learning

The increasing demand for food production has made
efficient plant health monitoring a critical aspect of modern
agriculture. Traditional methods rely on manual inspection,
which is time-consuming and often inaccurate. This paper
proposes an AI-based smart plant health monitoring system
that integrates Internet of Things (IoT) sensors with deep
learning techniques to enable real-time monitoring and
early detection of plant diseases. Environmental parameters
such as temperature, humidity, and soil moisture are
continuously collected using IoT devices, while leaf
images are analyzed using a Convolutional Neural Network
(CNN) model for accurate disease classification.
The collected data is transmitted to a cloud platform for
analysis and monitoring, allowing farmers to receive timely
alerts and recommendations. The proposed system
improves accuracy, reduces manual effort, and helps
prevent crop loss through early intervention. Overall, the
integration of IoT and deep learning provides a scalable,
cost-effective solution for precision agriculture and
sustainable farming practices.