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- W4383670020 abstract "Every year tomato crops suffer huge losses due to the occurrence of disease outbreaks. Therefore, the identification of diseases has its importance in the research fraternity. However, this whole process is influenced by the presence of a complex natural environment and similarity in diseases in terms of disease symptom patterns, size of disease region, and similar color variations in leaves with its background. To counter all these issues, in this work we propose an Integrated model that helps to distinguish between various tomato diseases. The Region of Interest ROI identification process along with CNN architecture can capture discriminative features. To ensure the effectiveness of the proposed work, the model has been evaluated on a real-world dataset and Plant Village standard dataset and it is compared with several state-of-the-art CNN architectures. It is shown that the proposed model has achieved higher accuracy compared to other state-of-the-art models." @default.
- W4383670020 created "2023-07-09" @default.
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- W4383670020 date "2023-01-01" @default.
- W4383670020 modified "2023-10-14" @default.
- W4383670020 title "Novel Image Processing Technique Using Thresholding and Deep Learning Model to Identify Plant Diseases in Complex Background" @default.
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- W4383670020 doi "https://doi.org/10.1007/978-981-99-0483-9_23" @default.
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