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To optimize the agricultural logistics system and address issues such as seasonal supply-demand imbalances, inadequate infrastructure, and data fragmentation, this paper proposes an integrated artificial intelligence algorithm framework. By combining the Grey Prediction Model GM(1,1) with BP neural networks, a hybrid prediction model is established that can successfully handle sparse time data and capture nonlinear feature relationships. This significantly enhances the accuracy and stability of logistics capacity predictions. The paper also provides a quantitative basis for decision-makers, contributing to the implementation of rural revitalization plans and sustainable economic growth in local areas.

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Multimodal Data Fusion for Intelligent Assessment and Dynamic Forecasting in Agricultural Logistics Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Yuhui Sun,
  • Jiabao Shi,
  • Yifan Sun,
  • Dongyang He,
  • Yingjie Zhao,
  • Minfeng Qi

摘要

To optimize the agricultural logistics system and address issues such as seasonal supply-demand imbalances, inadequate infrastructure, and data fragmentation, this paper proposes an integrated artificial intelligence algorithm framework. By combining the Grey Prediction Model GM(1,1) with BP neural networks, a hybrid prediction model is established that can successfully handle sparse time data and capture nonlinear feature relationships. This significantly enhances the accuracy and stability of logistics capacity predictions. The paper also provides a quantitative basis for decision-makers, contributing to the implementation of rural revitalization plans and sustainable economic growth in local areas.