Detection of Rose Leaf Diseases Using Deep Learning Methods

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