Abstract:
Rose cultivation is essential to the world's floriculture industry. But because it is
frequently plagued with various leaf diseases, both the quality and quantity produced on
roses will decline.Early discovery and prompt and correct identification are crucial to
effective control of these diseases. This paper presents a deep learning approach to the rose
leaf disease diagnosis problem based on convolutional neural networks (CNNs) . A set of
2000 rose leaf images was collected and pre-processed by resizing, normalization and data
augmentation techniques to improve model robustness . Five cutting-edge CNN models—
VGG16, ResNet50V2, InceptionResNetV2, DenseNet121 and EfficientNetB0—were
trained and tested to distinguish between infected and normal leaves. The experiment
results indicate that InceptionResNetV2 achieved the best classification accuracy (97.57%)
followed by DenseNet121 (97.03%) and ResNet50V2 (95.14%) , while VGG16 and
EfficientNetB0 achieved the comparative worse results (88.24% and 90.62%) . Our
experimental results justify the necessity of deeper and more complex CNN structures for
the plant disease detection problem over the earlier models. This investigation shows the
promise of deep learning for automatic, accurate, scalable, and broadcast rose disease
classification and decision support in support of farmers and researchers toward
intelligent agricultural systems. In future we aim to extend our dataset, and deploy the
top performing models to real-time use in mobile or web application for practical usage.
