Rose Plant Leaf Disease Recognition Using Machine Learning Methodologies
Egamamidi Rishika Reddy
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
Sai Durga Satturi
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
Medavarapu Harshini
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
Subhani Shaik *
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
The most popular flowers in the world are roses, not only cheer people up but also support livelihoods. Diseases, however, can harm these priceless flowers' health and negatively affect both their quality and the growers' livelihoods. The increased occurrence of ailments in rose plants poses a severe danger to the ornamental flower industry and agricultural productivity. In this paper, we describe a novel deep learning-based method for the automated diagnosis of leaf diseases in rose plants. A big dataset containing images of both healthy and damaged rose leaves was carefully picked to illustrate different disease types and stages. To analyze and identify the visual characteristics that correspond to various illnesses, we used a Convolutional Neural Network architecture, Support Vector Machine, and K-Nearest Neighbors architectures specifically intended for picture classification tasks. We address the interpretability and explainability of the model's predictions in addition to performance indicators, offering insights into the decision-making process. This work addresses a fundamental requirement for effective and long-lasting disease management in rose cultivation by bridging the gap between deep learning and plant pathology. CNNs are often the preferred choice due to their ability to automatically learn relevant features from raw pixel values.
Keywords: Rose plant, leaf disease detection, deep learning, convolutional neural networks, image classification, agricultural technology