Disease Detection in Rose Leaves
Description
This project focuses on developing an efficient and accurate system for detecting diseases in rose leaves. Roses are among the most cherished ornamental plants worldwide, and their health is crucial for both commercial cultivation and aesthetic value. Early identification and treatment of diseases in rose leaves can significantly reduce crop losses, improve plant health, and ensure high-quality flowers. Leveraging image processing and machine learning techniques, this project aims to classify rose leaves into different categories based on their health condition, enabling proactive disease management. Dataset Overview: The dataset for this project comprises 3366 png images of rose leaves, categorized into four key classes: Fresh Leaf (1379): Healthy, green leaves with no visible signs of disease. Black Spot (932): Leaves exhibiting dark, circular spots caused by fungal infections. Hole Leaf (881): Leaves with physical damage, likely due to pests or environmental factors. Yellow Leaf (174): Leaves showing discoloration, a potential indicator of nutrient deficiencies or early disease stages. Applications: Smartphone App Integration: A mobile app that allows gardeners and farmers to capture leaf images and receive instant disease diagnoses. Precision Agriculture: Targeted application of fertilizers and pesticides based on disease detection, reducing waste and environmental impact. Research and Extension: Assisting botanists and plant pathologists in studying disease patterns and developing effective treatments.