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<p>Agriculture is fundamental to human civilization; however, the increasing prevalence and diversity of plant diseases continue to threaten crop yield and quality. Early detection and accurate severity assessment are, therefore, critical for effective disease management. Although recent advances in computer vision have shown promising results in plant disease diagnosis, real-world deployment remains challenging due to complex backgrounds and varying environmental conditions. To address these challenges, we propose Plant Disease Severity Net (PDSNet), a novel framework for disease severity classification in Pome leaves that integrates advanced segmentation, feature extraction, and classification strategies. Mask R-CNN is employed to accurately isolate and segment leaves from complex backgrounds, ensuring precise region-of-interest extraction. PDSNet incorporates a fused feature extraction approach. First, a novel Shifted-3D Local Ternary Pattern (S-3DLTP) descriptor is introduced to capture discriminative textural patterns associated with disease severity, while Shifted Delta Cepstrum (SDC) further improves robustness and spatial feature dynamics. In parallel, an enhanced EfficientNet-B8 architecture with L2 regularization is utilized to extract deep semantic features. Before applying classification, features are optimized using a hybrid metaheuristic optimization algorithm. Experimental results demonstrate that PDSNet achieves validation accuracies of 90.32% on the DiaMOS pear dataset and 95.46% on the PlantVillage apple dataset with 5 severity classes each, significantly outperforming existing state-of-the-art methods. The generalizability of this framework was validated on an unseen dataset, AI-Challenger, which also proved its high performance.</p>

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PDSNet: A Novel Framework for Pome Leaves Disease Severity Classification

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Muhammad Qasim,
  • Qamas Gul Khan Safi,
  • Syed Muhammad Adnan Shah

摘要

Agriculture is fundamental to human civilization; however, the increasing prevalence and diversity of plant diseases continue to threaten crop yield and quality. Early detection and accurate severity assessment are, therefore, critical for effective disease management. Although recent advances in computer vision have shown promising results in plant disease diagnosis, real-world deployment remains challenging due to complex backgrounds and varying environmental conditions. To address these challenges, we propose Plant Disease Severity Net (PDSNet), a novel framework for disease severity classification in Pome leaves that integrates advanced segmentation, feature extraction, and classification strategies. Mask R-CNN is employed to accurately isolate and segment leaves from complex backgrounds, ensuring precise region-of-interest extraction. PDSNet incorporates a fused feature extraction approach. First, a novel Shifted-3D Local Ternary Pattern (S-3DLTP) descriptor is introduced to capture discriminative textural patterns associated with disease severity, while Shifted Delta Cepstrum (SDC) further improves robustness and spatial feature dynamics. In parallel, an enhanced EfficientNet-B8 architecture with L2 regularization is utilized to extract deep semantic features. Before applying classification, features are optimized using a hybrid metaheuristic optimization algorithm. Experimental results demonstrate that PDSNet achieves validation accuracies of 90.32% on the DiaMOS pear dataset and 95.46% on the PlantVillage apple dataset with 5 severity classes each, significantly outperforming existing state-of-the-art methods. The generalizability of this framework was validated on an unseen dataset, AI-Challenger, which also proved its high performance.