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The document compares the performance of various deep learning architectures, specifically MobileNet, DenseNet-121, ResNet-50, and SE-ResNet-50, in improving the Matthews Correlation Coefficient (MCC) for detecting paddy sheath blight, a significant disease affecting rice production. Results indicate that MobileNet consistently outperforms the other architectures, achieving an accuracy of 98% compared to 96% for DenseNet-121, 93% for ResNet-50, and 88.55% for SE-ResNet-50. The study emphasizes the importanc

Author

vmurali251959

Language

EN