Model Performance Metrics for Crop Parameter Predictions

MAE test loss results for LSTM and MLP models predicting crop parameters, with and without feature selection. The dataset is a 5.5 KB Excel file authored by Iman Hindi and updated in March 2026.

Use Cases

• Compare MAE test loss between LSTM and MLP model architectures for crop parameter prediction
• Evaluate the impact of feature selection on model performance using the provided MAE metrics
• Analyze performance differences across specific crop parameters as indicated by the dataset's tags

Strengths

• Provides direct model performance comparison via MAE test loss metrics
• Includes results for two distinct model types: LSTM and MLP
• Contains results for models trained both with and without feature selection

Limitations

• Extremely small scale at 5.5 KB, indicating very limited data volume
• No row count, column names, or sample data provided to assess structure
• Scope is restricted to model performance outputs, not the underlying crop or feature data

Provenance

Source

figshare

Collection Method

Results from trained machine learning models (LSTM and MLP).

Time Range

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Freshness

Last updated March 2026.

Geography

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