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<p>Plant diseases threaten<!--Query ID="Q1" Text="Please check if article title presented correctly. " Resolved="yes"--> global agriculture, and deep learning-based disease recognition has become crucial for addressing this challenge. While DenseNet excels in plant disease classification due to its dense connectivity, its large size limits deployment on resource-constrained edge devices. This paper proposes Connection-Aware DenseNet Pruning (CADP), achieving efficient compression through three collaborative modules. First, the EdgePrune module explicitly models inter-channel feature flows via an edge weight network, using dual-channel importance scoring that fuses activation correlation and gradient information to remove redundant connections while preserving critical propagation paths. Second, connection-guided CP decomposition leverages EdgePrune's importance information, adaptively assigning differentiated ranks through the Connection<!--Query ID="Q2" Text="Please check if the authors and their affiliation are presented and indicated correctly. " Resolved="yes"--> Importance Index (CII) to balance preservation of critical layers with deep compression of secondary layers. Third, dual-stream knowledge distillation integrates throughout post-pruning and post-decomposition fine-tuning, combining output-level soft labels and intermediate spatial attention transfer to recover compression losses. CADP achieves 88% parameter reduction and 89% computational savings on DenseNet-121, maintaining 99.67% and 99.66% accuracy<!--Query ID="Q3" Text="Please confirm if the author names are presented accurately. " Resolved="yes"--> on PlantVillage and RiceLeaf datasets, achieving competitive accuracy with significantly fewer parameters. This provides a promising approach for resource-constrained deployment with potential generalizability and practical value.<!--Query ID="Q4" Text="Please check abbreviation section if the provided description of the corresponding terms are captured correctly. " Resolved="yes"--></p>

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CADP: Connection-Aware DenseNet Pruning for lightweight plant disease classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Huiling Jiang,
  • Xian Cao,
  • Jun Liu,
  • Gang Yi,
  • Zhihui Wang,
  • Yingying Peng,
  • Man Li

摘要

Plant diseases threaten global agriculture, and deep learning-based disease recognition has become crucial for addressing this challenge. While DenseNet excels in plant disease classification due to its dense connectivity, its large size limits deployment on resource-constrained edge devices. This paper proposes Connection-Aware DenseNet Pruning (CADP), achieving efficient compression through three collaborative modules. First, the EdgePrune module explicitly models inter-channel feature flows via an edge weight network, using dual-channel importance scoring that fuses activation correlation and gradient information to remove redundant connections while preserving critical propagation paths. Second, connection-guided CP decomposition leverages EdgePrune's importance information, adaptively assigning differentiated ranks through the Connection Importance Index (CII) to balance preservation of critical layers with deep compression of secondary layers. Third, dual-stream knowledge distillation integrates throughout post-pruning and post-decomposition fine-tuning, combining output-level soft labels and intermediate spatial attention transfer to recover compression losses. CADP achieves 88% parameter reduction and 89% computational savings on DenseNet-121, maintaining 99.67% and 99.66% accuracy on PlantVillage and RiceLeaf datasets, achieving competitive accuracy with significantly fewer parameters. This provides a promising approach for resource-constrained deployment with potential generalizability and practical value.