Abstract:
The study revolves around automated detection and classification of rose plant
diseases, a central aspect of high-end agricultural technology. Disease in rose plants
is a serious threat to sustainable plant growth because manual detection is slow,
imprecise, and often useless in preventing damage. The plant health loss not just
downgrades its ornamental quality but also impacts the agric economy that relies on
numerous individuals for their survival. Leaves being the plants' primary source of
energy, any disease that infests them leaves the plant vulnerable. It is challenging to
diagnose diseases in leaves because of their fragile appearance and environmental
factors. To overcome this, deep learning techniques were employed, which were highly
accurate at detecting the diseases from images. A dataset was made and augmented
by data augmentation techniques such as rotation, flip, zoom, and brightness
adjustment so that classes can be balanced and models can be generalized. The process
involved included image preprocessing, data augmentation, and hyperparameter
tuning with different CNN-based architectures. Preprocessing included resizing
images, normalization, and format normalization. ResNet50, VGG19, Xception, and
InceptionV3 were also tested for performance with four classes of rose leaf disease.
Among them, the highest accuracy of 99.60% was recorded by InceptionV3 and it was
well justified in its accurate classification. The approach is significantly promising for
integration into automated systems to enable q
