Multi-Modal Data for Predicting Onion Losses During Transport

A multi-modal dataset designed for predicting post-harvest losses of onions during transportation. The dataset was uploaded to Kaggle, but the author, organization, and specific collection details are not provided. The description indicates a focus on modeling losses that occur in the supply chain after harvest.

Use Cases

• Predicting spoilage rates during transport based on multi-modal sensor data mentioned in the description
• Analyzing factors contributing to post-harvest loss in agricultural supply chains
• Building decision-support tools for logistics planning to minimize food waste

Strengths

• Focuses on a specific, high-impact agricultural problem of post-harvest loss.
• Described as multi-modal, which likely provides diverse predictive features.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Kaggle