Tomato Plant Traits and RGB Image Indices Under Water Stress, 2022-2023

RGB image indices achieved R² values reaching 0.99 for predicting plant traits like biomass and yield under different irrigation levels. This dataset, created by Mohamed S. Abd El-baki, contains measurements from field experiments conducted during the 2022 and 2023 growing seasons. It likely includes tabular data on plant traits and calculated image indices for use in artificial neural network models.

Use Cases

• Train artificial neural network models to predict tomato yield based on RGB image indices.
• Analyze the relationship between canopy water content and irrigation levels using measured plant traits.
• Develop early stress detection systems for vegetables using proximal sensing data.
• Compare the predictive power of RGB indices incorporating green components versus those that do not.

Strengths

• High prediction accuracy reported, with R² values reaching 0.99 for various plant traits.
• Data collected over two growing seasons (2022 and 2023) at multiple plant growth stages.
• Includes multiple measured plant traits: fresh biomass, dry biomass, canopy water content, relative chlorophyll content, soil moisture, and yield.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Dataset is very small (5.5 KB), indicating limited scope or a summary dataset.

Provenance

Source

figshare, authored by Mohamed S. Abd El-baki.

Collection Method

Field experiments capturing digital RGB images and measuring plant traits.

Time Range

2022 and 2023 growing seasons.

Freshness

Last updated 2026-04-30 17:29:23; freshness should be verified.

Geography

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