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- W4313270469 abstract "Grain output must increase in response to the world's expanding population. Grain output must be enhanced to meet the requirements of a growing population. The current climate is producing various illnesses in the grain crop, disrupting grain supply. Plant protection and pest management have the potential to boost cereal crop yields. Plant disease classification is critical for disease prevention and diagnosis. Participants are led to a paddy contaminated with bacteria, viruses, or fungus. Rice production is often increased by accurate disease classification and early crop protection. Rice diseases such as leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot were identified using deep learning techniques. The Ensemble Model had a critical role in the method's success. The second set of photographs was used to verify the results. The networks DenseNet-121, SEResNet-50, and ResNet-50 are three examples." @default.
- W4313270469 created "2023-01-06" @default.
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- W4313270469 date "2022-09-21" @default.
- W4313270469 modified "2023-09-30" @default.
- W4313270469 title "Detection of Varieties of Diseases in Rice Plants using Deep Learning Techniques" @default.
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- W4313270469 doi "https://doi.org/10.1109/icirca54612.2022.9985745" @default.
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