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<p>With the rapid development of precision agriculture, the integration of drone imagery and yield sensor data has become a key technology for enhancing agricultural productivity. As an advanced deep learning model, the cross-modal Transformer demonstrates significant application potential in precision agriculture due to its powerful feature extraction and fusion capabilities. This study explores the application of cross-modal Transformer in integrating drone imagery and yield sensor data for precision agriculture. By constructing an efficient fusion model, this technology achieves deep integration and precise analysis of multi-source data, thereby providing scientific decision-making support for agricultural production.</p>

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Application of cross-modal transformer in the fusion of precision agriculture UAV image and yield sensor data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Lei Lei,
  • Xiuting Wang

摘要

With the rapid development of precision agriculture, the integration of drone imagery and yield sensor data has become a key technology for enhancing agricultural productivity. As an advanced deep learning model, the cross-modal Transformer demonstrates significant application potential in precision agriculture due to its powerful feature extraction and fusion capabilities. This study explores the application of cross-modal Transformer in integrating drone imagery and yield sensor data for precision agriculture. By constructing an efficient fusion model, this technology achieves deep integration and precise analysis of multi-source data, thereby providing scientific decision-making support for agricultural production.