Experimental Parameters for the TDD-YOLO Tomato Disease Detection Model

A 5.5 KB Excel file contains the experimental environment and parameter settings used to train and evaluate the TDD-YOLO model. The model was tested on two datasets: Tomato-Village, with field-acquired images of six tomato diseases, and Tomato-Disease, which includes a greater diversity of disease types and healthy leaf samples. Author Zijian Chen published the dataset on figshare in May 2026 under a CC-BY-4.0 license.

Use Cases

• Reproducing the TDD-YOLO model's training and evaluation results based on the documented parameter settings.
• Benchmarking new object detection models against the reported mAP@50 and mAP@50:95 performance metrics on the referenced tomato disease datasets.
• Analyzing the impact of specific architectural improvements, like the feature enhancement module and joint attention mechanism, using the provided experimental setup.

Strengths

• Parameter settings are explicitly documented for model reproducibility.
• Model performance is validated on two distinct datasets with reported mAP improvements of up to 6.0%.

Limitations

• The 5.5 KB size suggests the file contains only configuration parameters, not the actual image datasets.
• Row count and column-level documentation for the parameter table are unknown.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Author Zijian Chen, associated with the TDD-YOLO research paper.

Collection Method

Likely contains manually or programmatically recorded experimental parameters from model training.

Freshness

Last updated 2026-05-22 17:48:18; freshness should be verified.