Cocoa Pod Disease Classification Performance Metrics

A 5.5 KB Excel file containing summary data of split disease conditions, likely including model performance metrics. The dataset was authored by Henry Techie-Menson and last updated on April 30, 2026. It supports research on a novel deep learning framework for cocoa disease classification.

Use Cases

• Benchmarking deep learning models for plant disease classification based on reported accuracy and F1 scores.
• Evaluating model generalization across different cocoa disease datasets mentioned in the description.
• Training or validating attention-based neural network architectures for agricultural image analysis.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Model performance is documented with specific metrics, including 98.95% accuracy and 99.11% F1 score on a primary dataset.
• Cross-dataset evaluation results are provided, showing accuracies from 96.19% to 98.53% on three other datasets.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's scope is limited to summary performance metrics, not raw image data.

Provenance

Source

figshare, author Henry Techie-Menson

Collection Method

Likely generated as part of a research study introducing the LGF-CBAM deep learning model.

Freshness

Last updated 2026-04-30 17:51:52; freshness should be verified.