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GK Ganesh Kumar P, RR RajiniGanth(Anna University/SNS College of Technology, India)提出基于物联网的环境信息学框架用于实时作物病害检测,采用YOLO 10-YSTs(注意力驱动CNN+基于stigmergy的编码方法)+DBGU-LSTM(Dual-Bi-Gated Recurrent Unit with Long Short-Term Memory)两级模型,在对比其他主流深度学习模型时实现97.99%识别准确率。该框架利用IoT传感器收集土壤湿度、温度、湿度等环境参数数据,通过深度学习算法识别作物病害模式,降低作物损失并提高整体产量。

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Frontiers in Environmental Science 2026-07-28 接受

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ORIGINAL RESEARCH article

Front. Environ. Sci.

Sec. Environmental Informatics and Remote Sensing

Volume 14 - 2026 | doi: 10.3389/fenvs.2026.1894362

Published in Image 6: Frontiers in Environmental Science Frontiers in Environmental Science * #### Environmental Informatics and Remote Sensing * 4.4 impact factor * 9.2 citescore

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ORIGINAL RESEARCH article

Front. Environ. Sci.

Sec. Environmental Informatics and Remote Sensing

An IoT-Enabled Environmental Informatics Framework for Real-Time Crop Disease Detection Using Attention-Driven Deep Learning

  • Image 8: Ganesh Kumar P Ganesh Kumar P 1

  • R R
    RajiniGanth R 2

    1. CEG Campus, Anna University, Chennai, India
    1. SNS College of Technology, Coimbatore, India

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Frontiers in Environmental Science 2026-07-28 接受
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