AI-Enhanced Plant Leaf Disease Identification And Treatment Recommendation System With Google Vision Tag Integration Users

Abstract

The AI-Enhanced Plant Leaf Disease Identification
and Treatment Recommendation System leverages
deep learning to automate and improve the accuracy
of plant health diagnostics. A convolutional neural
network (CNN) is trained on a diverse, augmented
dataset of leaf images covering 15 disease categories
and healthy samples. During inference, the system
accepts user-uploaded leaf photographs, preprocesses
them, and computes a binary health probability via a
sigmoid-activated output layer.
Probabilities are visualized through dynamic bar and
scatter charts, illustrating the confidence in
“Healthy” versus “Diseased” classifications. When
disease is detected, a dedicated Medicine Store
module—hosted on a separate webpage—presents
scientifically vetted treatment options; for healthy
leaves, it offers preventative care products. The
system’s web interface, built with Flask and enhanced
by Chart.js, ensures an intuitive user experience, while
comprehensive evaluation metrics (accuracy,
precision, recall, and loss) and real-time visualization
uphold rigorous performance standards
By integrating AI-driven detection with actionable
treatment recommendations, this solution empowers
growers to make data-backed decisions, reduce crop
losses, and streamline plant health management.