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This is a Experimental Study study.

September 17, 2025PLoS ONEOpen Access

Multi-model machine learning for automated identification of rice diseases using leaf image data

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Hybrid deep-learning framework achieves 98.61% accuracy in detecting rice diseases, suggesting significant potential for agricultural modernization.

The proposed system achieved a classification accuracy of 98.61%, indicating high reliability.
Using a medium Gaussian kernel of the SVM classifier improved sensitivity to 97.25%, enhancing detection capabilities.
The novel framework utilizes pre-trained CNN models for feature extraction, showcasing advancements in machine learning applications.
Automated identification reduces farmers' reliance on manual inspections, promoting efficient disease management.

Tiwari et al. (2025) studied this question.

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Hybrid deep-learning framework achieves 98.61% accuracy in detecting rice diseases, suggesting significant potential for agricultural modernization.