Apple leaf disease severity grading based on deep learning and the DRL-Watershed algorithm.

Abstract

Apple leaf diseases significantly impair the photosynthetic efficiency and growth quality of apple trees, leading to reduced fruit yields. Existing methods for disease detection and severity classification struggle to quickly and accurately segment and quantify diseased areas on leaves, particularly in complex backgrounds. To address this issue, we propose a method for assessing the severity of apple leaf diseases based on a combination of improved HRNet and DRL-watershed algorithms. First, we selected HRNet_w32 as the backbone feature extraction network and incorporated a Normalization Attention Mechanism (NAM). Then, we combined the Dice Loss and Focal Loss functions to construct an enhanced HRNet based semantic segmentation model for pixel-level segmentation of both apple leaf and diseased regions. Furthermore, the segmented leaf and disease regions were further optimized using the DRL-watershed algorithm to distinguish overlapping leaf regions. Experimental results demonstrate that the modified HRNet model achieved a mean intersection over union (mIoU) of 88.91% and a mean pixel accuracy (mPA) of 94.13%, representing improvements of 8.77 and 7.25% points, respectively, over the original HRNet. The disease severity assessment accuracy reached 97.65%. This study not only accurately segments apple leaves and diseased areas, but also effectively addresses the impact of complex backgrounds and leaf overlap on disease severity assessment, providing a solid scientific basis for disease management strategies.

Plant phenotyping relevance

リンゴ葉の病斑領域を画像から分割・定量し、病害重症度を推定する深層学習・DRL-Watershed手法が研究の中心であり、植物病害状態のフェノタイピングに該当する。

Code and data availability

The paper's apple leaf disease image dataset (417 annotated images plus augmented subsets) is explicitly not publicly available; it can only be obtained from the corresponding author upon reasonable request. No author analysis code, trained models, or other paper-specific public assets are mentioned.

No evidence-backed public reproduction asset is currently recorded.