Rose Plant Disease Detection Using Deep Learning

Abstract

This work is about the detection and identification of rose plant disease. Detection and Identification are part and parcel of modern agro-technology. Here we just used AI technology to detect rose plant disease but disease detection on plants is not easy for sustainable agriculture. Disease detection is challenging because of the infected leaf's availability. To see much enhancement in our work, we must analyze the agriculture field properly. Deep Learning technology is the most helpful tool for building this kind of disease detection model. Disease detection building involves the steps like image pre-processing and model analysis. In this paper, the algorithms used are ResNet50, (Visual Geometry Group) VGG-16, MobileNetV2, and Inception Version 3. There are four disease detections from the rose plant leaves. We researched image processing here with a detected method and achieved a successful accuracy of 96.11% with the MobileNetV2 model.