Emerging Trends in Engineering and Management
Rice Plant Disease Classification and Comparative Analysis of SVM Hyperparameters
Authors: Rajvir Kaur and Avtar Singh
Publishing Date: 04-02-2023
ISBN: 978-81-955020-3-5
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.
Keywords
Rice Plant Diseases, Classification, Machine Learning, SVM Hyperparameters
Cite as
Rajvir Kaur and Avtar Singh, "Rice Plant Disease Classification and Comparative Analysis of SVM Hyperparameters", In: Vikram Dhiman and Pooja Dhand (eds), Emerging Trends in Engineering and Management, SCRS, India, 2023, pp. 13-25. https://doi.org/10.56155/978-81-955020-3-5-03