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- W3148710612 abstract "Plants are an integral part of our ecosystem, and automation in detecting their diseases has intrigued researchers all around the world. In the proposed context, we illustrate a comparison analysis, to detect diseased leaf images of bell pepper and tomato plants. In this research, we have developed a custom-designed CNN architecture and a deep neural network, DenseNet121. The model was evaluated using standard parameters like precision, sensitivity, specificity, F-measure, FPR, and FNR, which guarantees the outperforming ability of pre-trained classifier with respect to the custom CNN. The balanced accuracy (BAC) of CNN and DenseNet121 was 96.5% and 98.7%, respectively, thus outperforming all other works on this particular dataset. The train data size was 80%, and the test data size was 12% with validation as 8%." @default.
- W3148710612 created "2021-04-13" @default.
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- W3148710612 date "2021-01-01" @default.
- W3148710612 modified "2023-09-27" @default.
- W3148710612 title "Detecting Diseased Leaves Using Deep Learning" @default.
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- W3148710612 doi "https://doi.org/10.1007/978-981-33-4866-0_6" @default.
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