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September 10, 2025Open Access

Enhanced Convolutional Networks for Accurate Leaf- Based Plant Disease Classification

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RVRamu VankudothG Pulla Reddy Dental College & Hospital

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Implication

Experiments demonstrate enhanced convolutional networks achieve 98.7% accuracy in plant disease classification, indicating their viability for large-scale monitoring.

Key Points

Enhanced convolutional networks achieve an accuracy of 98.7%, indicating significant improvement over traditional methods.
The model utilized multi-scale feature extraction and attention mechanisms, ensuring robust feature representation.
Experiments on the PlantVillage dataset showed the model outperformed traditional CNNs like VGG16 and ResNet50 in precision and recall.
With only 4.9M parameters, the model is suitable for real-time deployment in agricultural settings, suggesting practical applications.

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Ramu Vankudoth (2025) studied this question.

synapsesocial.com/papers/68c1d98554b1d3bfb60fb3c6 https://doi.org/10.20944/preprints202508.2111.v1

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Implication

Experiments demonstrate enhanced convolutional networks achieve 98.7% accuracy in plant disease classification, indicating their viability for large-scale monitoring.

Enhanced Convolutional Networks for Accurate Leaf- Based Plant Disease Classification

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Member takes

Implication

Key Points

Cite This Study

Enhanced Convolutional Networks for Accurate Leaf- Based Plant Disease Classification

Discussion

Member takes

Implication

Key Points

Cite This Study

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