Status: published
Agricultural productivity plays a
vital role in the economic stability of many
developing countries. However, the rise of
plant diseases causes significant losses to
farmers and agricultural industries every year.
Early detection and accurate identification of
plant diseases are essential to prevent these
losses. This paper proposes a Plant Disease
Detector Application, a mobile-based system
designed
using
the
Flutter framework
integrated with a Convolutional Neural
Network (CNN) model built using TensorFlow
and Keras. The app enables users to capture
images of infected plant leaves and instantly
identify the disease along with suggested
preventive measures. The proposed model is
trained on the PlantVillage dataset, achieving
a
testing accuracy of over 97%. The
integration of TensorFlow Lite allows the
model to run efficiently on smartphones, even
without internet connectivity. This application
demonstrates a scalable, cost-effective solution
for farmers, researchers, and agricultural
institutions to promote sustainable farming through technology driven diseases management
Keywords: machine learning , flutter , flask api , python