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What is Explainable Multimodal Crop Yield Prediction about?

This study investigates the intrinsic explainability of Transformer-based multimodal learning models for crop yield prediction, utilizing diverse data sources such as satellite imagery and weather data. The research employs methods like Attention Rollout and Weighted Modality Activation to assess feature attributions and compares their effectiveness against traditional Shapley-based techniques. Findings indicate that Transformer models significantly outperform other architectures in yield prediction accurac

Author

Rudra Rudraiah

Language

EN