An IoT-Enabled Environmental Informatics Framework for Real-Time Crop Disease Detection Using Attention-Driven Deep Learning
论文提出物联网与注意力深度学习结合的作物病害实时检测框架，方法新颖，数据详实，但发表于一周前，时效性一般。
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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- Atmosphere and Climate
- Biogeochemical Dynamics
- Drylands
- Ecosystem Restoration
- Environmental Citizen Science
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- Environmental Informatics and Remote Sensing
- Environmental Policy and Governance
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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
Frontiers in Environmental Science * #### Environmental Informatics and Remote Sensing * 4.4 impact factor * 9.2 citescoreReviewers
Reviewed by
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Outline
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
-
R R
RajiniGanth R 2 -
- CEG Campus, Anna University, Chennai, India
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- SNS College of Technology, Coimbatore, India
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