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Table 9 Advantages of proposed study over SOTA models

From: Robust CRW crops leaf disease detection and classification in agriculture using hybrid deep learning models

Feature/metric

Proposed slender-CNN

Traditional CNNs

Hybrid models

Parameter count

387,340 (extremely low)

High (MobileNetV3: ~ 3 M)

Very high

Accuracy (corn/rice/wheat)

98.45%/87.11%/99.96%

Lower for comparable models

High but with increased computational cost

Generalization across crops

High; no architectural modifications needed

Moderate; often requires re-training

High but computationally expensive

Multi-scale feature extraction

Enabled using multi-scale filters in a single layer

Nil

Partially enabled through attention mechanisms

Computational efficiency

High; suitable for resource-constrained devices

Moderate

Low; high computational requirements

Flexibility

Robust for various diseases and crops

Limited to specific conditions

Robust but dependent on large-scale resources

Deployment feasibility

Highly deployable on edge devices

Limited to mid-range devices

Requires high-end hardware

Scalability

Excellent; works effectively across datasets

Limited

High but with higher cost