August 15, 2025AgricultureOpen Access

Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model

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The DEMM-YOLO model achieves 94.8% precision in detecting cotton leaf diseases, indicating strong accuracy for agricultural applications.
With 93.1% recall, the model effectively identifies true disease instances, enhancing timely management of cotton crops.
Enhanced by large language model-generated images, the DEMM-YOLO model improves detection capabilities for low-frequency diseases.
Introducing innovative components like the multi-scale feature aggregation module ensures robust performance in diverse conditions.

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Implication

Experimental findings demonstrate the DEMM-YOLO model achieves 94.8% precision and 93.1% recall in cotton leaf disease detection, indicating effective use of synthetic data.

Cite This Study

Gao et al. (2025) studied this question.

synapsesocial.com/papers/68a3656a0a429f797332ba8f https://doi.org/10.3390/agriculture15151712

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Implication

Experimental findings demonstrate the DEMM-YOLO model achieves 94.8% precision and 93.1% recall in cotton leaf disease detection, indicating effective use of synthetic data.

Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model

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Implication

Cite This Study

Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model

Key Points

Discussion

Implication

Cite This Study

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