  Monitoring plant health is crucial for maintaining agricultural productivity
and food safety. Disruptions in the plant's normal state, caused by diseases,
often interfere with essential plant activities, and timely detection of these
diseases can significantly mitigate crop loss. In this study, we propose a deep
learning-based approach for efficient detection of plant diseases using
drone-captured imagery. A comprehensive database of various plant species,
exhibiting numerous diseases, was compiled from the Internet and utilized as
the training and test dataset. A Convolutional Neural Network (CNN), renowned
for its performance in image classification tasks, was employed as our primary
predictive model. The CNN model, trained on this rich dataset, demonstrated
superior proficiency in crop disease categorization and detection, even under
challenging imaging conditions. For field implementation, we deployed a
prototype drone model equipped with a high-resolution camera for live
monitoring of extensive agricultural fields. The captured images served as the
input for our trained model, enabling real-time identification of healthy and
diseased plants. Our approach promises an efficient and scalable solution for
improving crop health monitoring systems.
