LeafDet: Tomato Leaf Disease Detection Model Performance by Class

LeafDet model performance data for detecting eight distinct tomato leaf diseases. The dataset is associated with the PlantTom image collection, which contains 7,836 images, and was authored by Vaskor Mostafa. It was last updated on May 22, 2026.

Use Cases

• Benchmarking object detection models for plant disease identification based on the described 91.6% [email protected] score.
• Analyzing class-specific performance for tomato leaf diseases based on the eight distinct classes mentioned.
• Studying the impact of architectural modules like ECA and Shuffle Attention on model accuracy and speed.

Strengths

• Model achieved a 91.6% [email protected] score, a 2.2% improvement over the baseline YOLOv8n.
• Associated PlantTom dataset contains 7,836 images across 8 classes, designed to address class imbalance.
• Model inference time is reported as 2.4ms, suggesting potential for real-time application.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, authored by Vaskor Mostafa.

Collection Method

Likely contains performance metrics from model evaluation on the PlantTom dataset.

Time Range

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Freshness

Last updated 2026-05-22 18:00:32; freshness should be verified.

Geography

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