Rose Plant Disease Detection using Deep Learning

Abstract

The detection and identification of rose plant disease is the focus of this investigation. Identification and detection are essential components of contemporary agro technology. In this case, AI technology was utilized to identify a disease in rose plants, although plant disease detection is difficult for sustainable agriculture. There are several instances of rose plant disease, and as a result, fascinating decoration is being lost. Due to this situation, which is getting worse every day in Bangladesh, the economy of agricultural sector is suffering. Bangladesh's population relies heavily on agriculture industry for their revenue. This study includes some disease detection of rose plants, albeit not all plants are affected equally by the illness. The plant leaf provides the plant with vital sustenance. When a leaf is ill, the plant is at its most vulnerable. Due to the accessibility of the sick leaf, disease identification is difficult. Agriculture field must be properly assessed to see significant improvements in proposed work. The best resource for creating this kind of disease detection model is deep learning technology. Image pre-processing and model analysis are steps in the disease detection construction process. Few CNN architectures are used in this study, including ResNet50, VGG-16 (Visual Geometry Group), MobileNetV2, and Inception V3. Four diseases have been identified in rose plant leaves. Here, image processing is investigated using a discovered approach and obtain a MobileNetV2 model accuracy of 96.11%.