Rose Plant Leaf Disease Recognition Using Machine Learning Methodologies

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.

Plant phenotyping relevance

バラ葉の画像から病害状態を自動診断する画像解析・機械学習手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。

Code and data availability

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