Cocoa Pod Disease Image Dataset for Model Evaluation

98.95% accuracy was achieved by the LGF-CBAM model on the Cocoa_Pod_Disease_Gh dataset. This dataset was used to evaluate a novel deep learning framework for classifying cocoa pod diseases, contributed by Henry Techie-Menson and last updated in April 2026. The 5.5 KB Excel file likely contains summary metrics from cross-dataset evaluations on four other cocoa disease image sets.

Use Cases

• Benchmarking computer vision models for plant disease classification based on the described cocoa pod disease categories.
• Evaluating model generalization and robustness based on cross-dataset performance metrics mentioned in the description.
• Training or fine-tuning attention-based neural networks for agricultural imagery using the referenced dataset characteristics.

Strengths

• Model evaluation results are highly specific, citing 98.95% accuracy and 99.11% F1/PPV scores.
• Cross-dataset robustness is demonstrated with performance metrics on four other cocoa disease datasets.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• The dataset is extremely small at 5.5 KB, suggesting it contains summary statistics rather than raw image data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain analyses.

Provenance

Source

figshare, author Henry Techie-Menson.

Collection Method

Likely compiled as a summary of evaluation results from multiple cocoa disease image datasets used in a research study.

Freshness

Last updated 2026-04-30 17:51:53; freshness should be verified.

Geography

The referenced datasets suggest a geographic focus on Ghana (Cocoa_Pod_Disease_Gh), Davao, and other regions.