MD-Unet for tobacco leaf disease spot segmentation based on multi-scale residual dilated convolutions.

Abstract

Identification and diagnosis of tobacco diseases are prerequisites for the scientific prevention and control of these ailments. To address the limitations of traditional methods, such as weak generalization and sensitivity to noise in segmenting tobacco leaf lesions, this study focused on four tobacco diseases: angular leaf spot, brown spot, wildfire disease, and frog eye disease. Building upon the Unet architecture, we developed the Multi-scale Residual Dilated Segmentation Model (MD-Unet) by enhancing the feature extraction module and integrating attention mechanisms. The results demonstrated that MD-Unet achieved 92.75%, 90.94%, 84.93%, and 91.81% for the lesion CPA, recall, IoU, and F1 metrics, respectively, with an overall Dice score of 94.67%. Furthermore, the model parameters, floating-point operations, and inference time per single image for MD-Unet were 4.65 × 10 7 , 2.3392 × 10 11 , and 65.096 ms, respectively. Compared to Unet, PSP, DeepLab v3+, FCN, SegNet, UNET++, and DoubleU-Net, MD-Unet significantly improved accuracy while effectively managing model complexity, achieving optimal overall performance. This work provides the theoretical foundations and technical support for precise segmentation of tobacco lesions, with potential applications in the segmentation of other plant diseases.

Plant phenotyping relevance

タバコ葉の病斑を画像から分割・定量する深層学習手法を開発し、複数モデルと精度・計算量・推論時間を比較検証しており、植物病害状態の取得方法が中心である。

Code and data availability

The paper's tobacco leaf disease image dataset is explicitly stated as publicly available via a Kaggle DOI in the Data availability statement. Labelme is a generic annotation tool, not a paper-specific asset; no author code or model checkpoint is released.