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<p>In this work, we propose a Graph Neural Network (GNN) and Vanilla Transformer-based hybrid model for IoT driven soil and crop prediction. Conventional forecasting approaches are unable to model complicated spatial and temporal inter-dependencies and are not very effective. The given paper solves this problem by using GNNs to learn the spatial relationships among the IoT sensor nodes and vanilla transformer model to learn the temporal dependencies in crop and weather data. Vanilla vision transformer is able to recover missing contextual information during training. It is trained on data from IoT sensors that monitor soil moisture, temperature, humidity and a variety of other environmental factors as well as historical crop yield and weather related information. The hybrid model can enable the real-time accurate prediction for crop yield production and soil health status, which enables a smarter agriculture decision. The experimental results show that the proposed work achieves the lowest root mean square error (RMSE 2.1) and the highest crop accuracy (92%) for short-term and long-term forecasts.</p>

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A hybrid GNN–vanilla vision transformer model for IoT-based soil and crop forecasting

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • Shrabani Mallick,
  • S. Suhas,
  • Tegil J. John,
  • Soubhagya Ranjan Mallick,
  • Neha Chaudhary,
  • Shyam Sunder Tumma,
  • Aurobinda Behera

摘要

In this work, we propose a Graph Neural Network (GNN) and Vanilla Transformer-based hybrid model for IoT driven soil and crop prediction. Conventional forecasting approaches are unable to model complicated spatial and temporal inter-dependencies and are not very effective. The given paper solves this problem by using GNNs to learn the spatial relationships among the IoT sensor nodes and vanilla transformer model to learn the temporal dependencies in crop and weather data. Vanilla vision transformer is able to recover missing contextual information during training. It is trained on data from IoT sensors that monitor soil moisture, temperature, humidity and a variety of other environmental factors as well as historical crop yield and weather related information. The hybrid model can enable the real-time accurate prediction for crop yield production and soil health status, which enables a smarter agriculture decision. The experimental results show that the proposed work achieves the lowest root mean square error (RMSE 2.1) and the highest crop accuracy (92%) for short-term and long-term forecasts.