Deep Learning for Disease Detection: Building a Leaf Image Classifier for Roses.

Abstract

Early and reliable detection of rose diseases is important for automating plant monitoring and timely intervention throughout the crop lifecycle. In this context, leaf-image analysis combined with machine learning offers a practical approach for disease detection in roses. This study tests a binary classification framework that distinguishes diseased leaves using convolutional neural networks (CNNs). Three architectures were evaluated: a lightweight CNN trained from scratch as a baseline model, and two residual network models fine-tuned through transfer learning from weights pretrained on a large-scale visual recognition dataset. To assess robustness, two preprocessing strategies were also compared: a lightweight hue-based leaf isolation method that preserves full color information, and a grayscale conversion approach without masking. Experimental results obtained on a small held-out test set show strong classification performance across all evaluated models. At the same time, the findings indicate that additional validation is needed on more diverse datasets to confirm generalization under varying lighting conditions, background complexity, and plant growth stages. The results support the feasibility of CNN-based disease detection for roses and highlight its potential for integration into automated monitoring workflows.

Plant phenotyping relevance

バラ葉画像から病害状態をCNNで推定する分類手法の開発・比較が研究の中心であり、植物の病害表現型を直接評価しているため。

Code and data availability

The paper's rose leaf image dataset (318 images, healthy vs. powdery mildew) is paper-specific but only available upon request from the corresponding author; no public code, model checkpoints, or dataset URLs are provided. The ONNX/ONNX Runtime/NVIDIA/NeurIPS URLs are generic references, not paper-specific assets.

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