Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method
Author's Country: Indonesia
DOI:
https://doi.org/10.36805/bitcs.v6i2.10164Keywords:
Accuracy, Disease Classification, GLCM, Leaf Image, M-SVM, RiceAbstract
The rice farming sector plays an important role in the Indonesian economy, considering that rice is the main staple food. According to IRRI, rice farmers experience crop losses of up to 37% each year due to pests and diseases. This study aims to classify rice plant diseases using the Multi-Class Support Vector Machine (M-SVM) method based on leaf images. This study aims to provide education to farmers in recognizing and overcoming diseases in rice plant leaves. The types of rice leaf diseases classified in this study include Blast, Kresek, and Tungro. The data used in this study amounted to 1200, which were divided by varying training and testing data ratios, from 10% training and 90% testing to 90% training and 10% testing. Each variation of features and data division was evaluated by calculating the model performance parameters. The features used for classification include color (RGB) and texture (GLCM) from leaf images. The test results showed that the best accuracy obtained was 85.5% using a combination of color and texture features
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