CNN-based Flower Disease Detection

TITLE	CNN-based Flower Disease Detection
ABSTRACT	The proposed method is a holistic deep learning-based framework of automatic flower disease detection and classification. The developed study aims at creating an in-depth smart system able to analyze the presence of plant diseases, Like Downy Mildew, Gray Mold, Leaf Scars etc. identify the difference between a healthy plant and an infected one. Using the CNN in relation to ResNet50 system architecture, the device possesses an elevated accuracy rate of classifying the disease by subjecting the system to intensive training using a collected set of pictures of flowers. The proposed system incorporated is a Django web framework on the user interface, which can predict disease in real-time based on the uploaded image. The experimental findings confirm the early detection capabilities of the system that can be an effective early identification aswell as preventive treatment tool among farmers and other agricultural professionals in observing the plants& health
AUTHOR	Darshan N P, Dr. Raghavendra S P J
PUBLICATION DATE	2025-09-03 19:11:31
VOLUME	13
ISSUE	3
DOI	DOI: 10.15662/IJMSERH.2025.1303071
PDF	pdf/2025/7/71_CNN-based Flower Disease Detection.pdf
KEYWORDS

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