ANN Model Performance for Predicting Tomato Traits from RGB Images

5.5 KB of tabular data compares the performance of different Artificial Neural Network models for predicting plant traits and tomato yield. The dataset, authored by Mohamed S. Abd El-baki and last updated in April 2026, likely contains evaluation metrics from models trained and tested using RGB image indices captured at the fruit ripening stage.

Use Cases

• Benchmarking ANN architectures for agricultural yield prediction based on RGB image indices.
• Analyzing the relationship between image-derived indices and specific plant traits mentioned in the description.
• Selecting optimal models for non-destructive tomato yield estimation at the ripening stage.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• File size is 5.5 KB, ensuring quick download and inspection.

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

Collection Method

Likely derived from experimental research comparing ANN models using image data.

Time Range

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Freshness

Last updated 2026-04-30 17:29:28; freshness should be verified.

Geography

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