Integrating remote sensing data assimilation, deep learning and large language model to interactive yield prediction for wheat breeding

Key Points

• Yield prediction shows enhanced accuracy with deep learning techniques and remote sensing data, indicating a significant advancement.
• A model integrating remote sensing data assimilation and artificial intelligence offers promising results for crop breeding initiatives.
• Analysis of data from multiple sources emphasizes the importance of accurate yield predictions for optimizing wheat cultivation practices.
• This approach highlights the potential for integrating technology in agriculture, calling for broader adoption in crop management.