International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 08 | Aug 2026
p-ISSN: 2395-0072
www.irjet.net
Flora-Sense: Deep Learning Based Rose Plant Disease Detection and Cure Recommendation System Rutuja Mohite1, Tanvi Mayekar2, Saba Tamboli3, Dr. Mrs. K. A. Adoni4 1234Electronics & Telecommunications PES Modern College of Engineering, Pune, India
----------------------------------------------------------------------------***-------------------------------------------------------------------------Abstract-Rose plants are highly affected by several technologies have created new opportunities for diseases that reduce plant quality and overall developing automated crop monitoring systems. Neuralproductivity. Conventional disease recognition in plants network-based models are capable of examining leaf can be challenging because it depends heavily on regular images and identifying disease symptoms using visual field inspections and specialized agricultural knowledge. characteristics such as texture, color variation, and leaf To address this issue, Flora-Sense was designed as a smart patterns. These capabilities make CNNs particularly monitoring platform for rose plants that can recognize well-suited for identifying plant diseases across varying various diseases and provide appropriate treatment environmental conditions. The feature-learning ability of recommendations. The proposed framework analyzes rose neural network models enables effective recognition of leaf photographs to recognize diseases such as Powdery disease symptoms under different environmental Mildew, Black Spot, Rose Rust, Downy Mildew, Rose Mosaic, conditions. Flora-Sense is an intelligent disease analysis and Gray Mold. Before model training, the collected leaf and recommendation system that examines images of images undergo preprocessing operations including rose leaves and determines the type of infection present, resizing and augmentation. The developed prediction while also suggesting suitable preventive and corrective model examines captured leaf images and determines the measures. corresponding disease category. Based on the generated prediction, the system provides appropriate cure and prevention recommendations through an integrated platform. The proposed solution minimizes manual effort, improves disease prediction reliability, and supports efficient monitoring of rose plant health. Keywords-Rose Plant Monitoring, Neural Network Model, AI-Based Agriculture, Intelligent Crop Analysis, Automated Plant Health System, Disease Prediction
I. INTRODUCTION Agriculture has remained an essential part of human development and continues to contribute significantly to economic growth and food production. Among ornamental flowering plants, roses are widely cultivated because of their decorative value and commercial demand. However, rose plants are frequently affected by diseases such as Powdery Mildew, Black Spot, Rose Rust, Downy Mildew, Rose Mosaic, and Gray Mold, which negatively influence plant growth and flower quality. Traditional plant disease identification mainly depends on physical examination carried out by farmers and field specialists. On larger agricultural operations, early disease symptoms often go unnoticed, leading to substantial crop damage and considerable economic losses. Modern image-analysis
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Fig. 1: Black spot and Downy mildew The system employs a trained CNN model to classify various disease categories alongside healthy leaves, while providing suitable treatment recommendations that include both preventive measures and therapeutic options. Flora-Sense aims to provide farmers and gardeners with a precise, cost-effective, and userfriendly tool for early disease identification and management. By minimizing manual intervention requirements and enhancing diagnostic precision, the system promotes sustainable agricultural practices while reducing unnecessary pesticide applications. The developed platform supports efficient disease monitoring, minimizes manual effort, and assists growers in maintaining healthy rose cultivation practices.
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