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- W4319752482 abstract "Rice plant diseases are among the most critical problems that are being faced by the farmers. The diseases affect the quality and quantity of the crop, which impacts the economy of countries like India, where agriculture is the primary occupation. Therefore, an early and accurate identification of plant disease is crucial to get the maximum yield from the crop. Traditionally, identifying plant diseases by observing or testing them in the laboratory is time-consuming. Many researchers have worked on image-based machine learning (ML) approach for detection and classification of plant diseases. This paper presented an ML-based Support Vector Machine (SVM) kernel technique for detecting diseases in rice plants. Classification is done using SVM with different hyperparameters (SVM kernels and regularization parameter) for the early and critical assessment of rice plants. This paper concludes that the SVM model trained with the optimized parameters obtained the highest accuracy of 0.996, which is better than the previous techniques and reveals the novelty of the work." @default.
- W4319752482 created "2023-02-11" @default.
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- W4319752482 date "2023-01-01" @default.
- W4319752482 modified "2023-10-01" @default.
- W4319752482 title "Rice Plant Disease Classification and Comparative Analysis of SVM Hyperparameters" @default.
- W4319752482 doi "https://doi.org/10.56155/978-81-955020-3-5-03" @default.
- W4319752482 hasPublicationYear "2023" @default.
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